This research not only provides a location method for the new tapping line for the rubber tapping robot but also provides theoretical support for the realization of rubber tapping mechanization and automation.Aiming at the problem that the rubber tapping robot finds it difficult to accurately detect the tapped area and locate the new tapping line for natural rubber trees due to the influence of the rubber plantation environment during the rubber tapping operation, this study proposes a method for detecting the tapped area and locating the new tapping line for natural rubber trees based on the improved mask region convolutional neural network (Mask RCNN). First, Mask RCNN was improved by fusing the attention mechanism into the ResNeXt, modifying the anchor box parameters, and adding a tiny fully connected layer branch into the mask branch to realize the detection and rough segmentation of the tapped area. Then, the fine segmentation of the existing tapping line was realized by combining edge detection and logic operation. Finally, the existing tapping line was moved down a certain distance along the center line direction of the left and right edge lines of the tapped area to obtain the new tapping line. The tapped area detection results of 560 test images showed that the detection accuracy, segmentation accuracy, detection average precision, segmentation average precision, and intersection over union values of the improved Mask RCNN were 98.23%, 99.52%, 99.6%, 99.78%, and 93.71%, respectively. Compared with other state-of-the-art approaches, the improved Mask RCNN had better detection and segmentation performance, which could better detect and segment the tapped area of natural rubber trees under different shooting conditions. The location results of 560 new tapping lines under different shooting conditions showed that the average location success rate of new tapping lines was 90% and the average location time was 0.189 s. The average values of the location errors in the  Tapping rubber trees is the most important way to obtain natural rubber . Rubber Manual rubber tapping is labor-intensive, time-consuming, and laborious, with low work efficiency and high labor costs . TherefoIn recent years, with the development of computer vision technology, machine vision has been widely used in the field of agricultural engineering . Some sc(1) The dataset of natural rubber tree tapped area detection and new tapping line location was established. The image was preprocessed by using bilinear interpolation, data enhancement, and other methods to diversify the image dataset, enhance the anti-interference ability under complex conditions, and improve the network training model effect and generalization ability.(2) The attention mechanism was fused into the ResNeXt to enhance the feature extraction capability of the backbone network, the relevant parameters of generating the anchor box were modified in the region proposal network to improve the matching degree between the anchor box and the natural rubber tapped area, and a tiny fully connected layer branch was added into the mask branch to improve the mask quality to improve the Mask RCNN. The improved Mask RCNN was used to realize the detection and rough segmentation of the tapped area of natural rubber trees.(3) On the basis of the detection and rough segmentation of the tapped area, the fine segmentation of the existing tapping line of natural rubber trees was realized by combining the edge detection and logic operation. Then, the existing tapping line was moved down a certain distance along the center line direction of the left and right edge lines of the tapped area to obtain the position of the new tapping line, and the new tapping line was smoothed to realize the location of the new tapping line.(4) The tapped area detection and the new tapping line location method based on improved Mask RCNN were trained and tested to accurately detect and segment the tapped area and effectively locate the new tapping line.The method proposed in this paper not only provides technical support for the location of the new tapping line of the rubber tapping robot in the rubber garden environment but also provides theoretical support for the realization of mechanization and automation of rubber tapping. The rest of this paper is organized as follows: The "Materials and Methods" section introduces the dataset and methods adopted in this study. In the "Results and Discussion" section, the experimental results of the performance evaluation of the natural rubber tree tapped area detection model and the experimental results of the new tapping line location are presented, and the experimental results are discussed. Finally, the "Conclusion and Future Work" section gives the main conclusions of the study and makes suggestions for future research.22.1In this research, natural rubber trees in their natural growth state were taken as the test objects, and a Sony DSC-RX100 camera was used for multi-angle shooting. The imaging range was 400-800 mm, and the image resolution was 5472×3648 pixels. To ensure the diversity of image samples, the dataset was divided into direct sunlight on sunny days, backlight on sunny days, and cloudy days according to the light conditions at the time of the shooting, and was divided into one year, two years, and three years according to the year when the natural rubber tree tapped area had been cut at the time of the shooting. A total of 1800 images of natural rubber trees were collected in the natural rubber garden in Danzhou, Hainan, China. All images in the dataset included the tapped area, the existing tapping line (the existing tapping line was on the tapped area)  ResNeXtThe block of the ResNeXt   Attention mechanismThe attention mechanism adopted in deep learning is similar to the selective visual attention mechanism of human beings. The attention mechanism enables the model to select the information most critical to the current task from a large amount of information during the training process. The attention mechanism allows the neural network to focus on the relevant elements of the object in the input image while suppressing the irrelevant elements . As a liF∈CR×H×W of layer C3 be the input feature map of CBAM, as shown in F passed the global max-pooling and global average-pooling in parallel to obtain two feature maps with a size of C×1×1 and two feature maps with a size of 1×H×W. Second, two feature maps with a size of C×1×1 were merged by using element-wise summation after entering a weight-sharing network composed of a multi-layer perceptron (MLP) with one hidden layer, and then the sigmoid activation operation was performed to generate the final channel attention CM∈CR×1×1. Two feature maps with a size of 1×H×W were convolved after the channel dimension splicing, and then the sigmoid activation operation was performed to generate the final spatial attention SM∈R1×H×W. Then, MC and F were merged by using element-wise multiplication to obtain the feature map F ' after channel attention adjustment, and SM and F were merged by using element-wise multiplication to obtain the feature map F '' after spatial attention adjustment. Finally, we added the feature maps F ' and F '' to get the input F ''' of the C4 layer. The calculation process of parallel connected CBAM generating attention is shown in formula (2).Let the output F is the input feature map, F ''' is the output feature map, CM(F) is the output of CAM, SM(F) is the output of SAM, and ⨂ is the element-wise multiplication (multiply the elements at the corresponding positions of two matrices).Where (3) FPND2, D3, D4, and D5) from low to high. Then, these feature maps were used as the input of FPN to establish a feature pyramid network and output new features . P6 was obtained by the maximum pooling operation of P5. The specific corresponding relationship between feature maps is shown in formula (3).In the improved Mask RCNN backbone network, the image passed through the bottom-up ResNeXt fused with the attention mechanism to obtain 4-level feature maps  had different levels of features.FPN adopted the convolution layer with a convolution kernel of 1×1 and the top-down and horizontal connection methods to fuse the 4-level feature maps generated by the ResNeXt fused with the attention mechanism. After fusion, each feature map .L is the total loss function of the network, maskL is the mask loss function, clsL is the classification loss function, and boxL is the bounding box regression loss function. In view of the actual situation of this study, the number of categories is set at 2 (tapped area class and background class).Where 2.4The fine segmentation of the existing tapping line is an important prerequisite for the location of the new tapping line. To accurately obtain the existing tapping line, this research used the Canny algorithm based on edge detection  and logiStep 1: Input the image into the improved Mask RCNN model to generate a prediction map -(8). The smoothed gray-scale image was subjected to non-maximum signal suppression processing based on the calculated gradient amplitude and angle.Step 3: The gradient amplitude and angle of a pixel point of the gray-scale image through the gradient in the xG is the gradient of the image in the x direction, yG is the gradient of the image in the y direction, P is the pixel value of the image at a certain point, S is the gradient magnitude of the pixel point, and θ is the angle of the pixel point.Where Step 4: After double threshold edge connection processing was performed on the image obtained in step 3, binarization was performed, and the result was output to obtain the edge line and position information of the tapped area of the natural rubber tree.A1     and D2 , and the line segment D1D2  and E2 , and the line segment E1E2  denotes the coordinates of the lower endpoint of the center line F1F2, and  denotes the coordinates of the upper endpoint of the center line F1F2.Where 2.5.2jQ , and the coordinates Q1j   of each point of the new tapping line, the cubic polynomial was used to fit the curve of the new tapping line .n is the degree and ka is the polynomial coefficient. ka is obtained by substituting the coordinates Q1j of each point of the new tapping line into formula (12) and then substituting the obtained ka into formula (11) to generate a smooth new tapping line Q2  Xeon (R) Silver 4210 processor, 32GB memory, and an NVIDIA RTX 4000 (8GB memory). The detection and segmentation of the tapped area of natural rubber trees and the location test of the new tapping line were carried out on the hardware platform of an Intel Core i7-11800H processor, 16GB memory, and an NVIDIA RTX 3050 (4GB memory). The software environment used in the experiment was a Windows 64-bit system, the PyTorch deep learning framework, and the Python programming language.2.6.1To verify the detection and segmentation effects of the improved network model and determine the optimal network model, six models of two different backbone networks were used for comparative experiments, namely the original Mask RCNN model based on ResNet50 and ResNet101, the improved Mask RCNN model based on ResNeXt50 and ResNeXt101 fused with the attention mechanism, and the improved Mask RCNN model based on ResNeXt50 and ResNeXt101 fused with the attention mechanism, which changed the structural parameters and mask branches. Based on the above comparative experiments, the optimal Mask RCNN network model was determined, and the comparative experiments were carried out with YOLACT, Cascade Mask RCNN, PointRend, Swin-B Cascade Mask RCNN, FCN, and DeepLabv3 to compare the detection and segmentation performance of different models and further verify the detection and segmentation effect of the improved model. All of the above model training’s learning rate, batch size, momentum factor, weight decay, and number of iterations were set to 0.01, 2, 0.9, 0.0001, and 30 epochs, respectively.2.6.2P, %), recall , F1 score , average precision , and intersection over union  were used as indicators to evaluate the effectiveness of the model, and the location success rate  of the new tapping line was counted. P, R, F1, AP, IOU, and Y are calculated as follows:To test the performance of the algorithm proposed in this study, 560 images of the test set were tested and evaluated for the detection of the tapped area and the location of the new tapping line. The precision  and IMRA(XA50+FC) increased by 1.98%, 3.4%, 0.57%, and 1.96%, 6.35%, 0.75%, respectively. Compared with MR(101), the AP0.5, AP0.75, and AP0.5-0.95 of IMRB(XA101) and IMRA(XA101+FC) increased by 1.89%, 5.91%, 1.5%, and 2.02%, 6.14%, 4.81%, respectively. The AP values of MR(101), IMRB(XA101), and IMRA(XA101+FC) were higher than MR(50), IMRB(XA50), and IMRA(XA50+FC), indicating that compared with ResNet50, ResNet101, and ResNeXt50 fused with the attention mechanism, the ResNeXt101 fused with the attention mechanism had a strong ability to extract features, and the object detection performance of the model was significantly improved (0.5-0.95 of IMRA(XA50+FC) and IMRA(XA101+FC) with changing structural parameters and mask branches increased by 0.18% and 3.31%, respectively, when compared to IMRB(XA50) and IMRB(XA101). It showed that IMRA(XA101+FC), which modified the anchor box parameters and added a tiny FC layer to the mask branch, paid more attention to the object itself and had the best object detection performance  and IMRA(XA101+FC) were increased by 0.57% and 1.52%, respectively, compared with IMRB(XA50) and IMRB(XA101). It showed that a tiny FC layer branch was added to the mask branch to further increase the information diversity, realize feature enhancement, and improve the segmentation performance of the model . When coIn addition, ROC curves and confusion matrices were used to summarize the detection and segmentation performance of IMRA (XA101+FC). It can be seen from In the total time spent on detection and segmentation, IMRA(XA50+FC) and IMRB(XA50) were 0.032 s slower than MR(50), while IMRA(XA101+FC) and IMRB(XA101) were 0.044 s and 0.043 s slower than MR(101), which indicated that the ResNeXt50 and ResNeXt101 fused with the attention mechanism had slowed down the detection and segmentation speed of the Mask RCNN model to a certain extent, but the impact was not very obvious. IMRB(XA50) spent the same time as IMRA(XA50+FC), while IMRA(XA101+FC) was 0.001 s faster than IMRB(XA101), which indicated that changing the anchor box ratio and adding a tiny FC layer branch into the mask branch had a weak impact on the model detection and segmentation speed. In terms of model parameters, IMRB(XA50) and IMRB(XA101) decreased by 0.37 and 0.23, respectively, compared with MR(50) and MR(101). When compared to MR(50) and MR(101), IMRA(XA50+FC) and IMRA(XA101+FC) increased by 0.81 and 0.95, respectively. It showed that ResNeXt50 and ResNeXt101, which fused with the attention mechanism, reduced the parameters of the Mask RCNN model. However, modifying the anchor box parameters and adding a tiny FC layer to the mask branch increased the parameters of the Mask RCNN to a certain extent, but the impact was not significant.0.5, AP0.75, AP0.5-0.95, and IOU were increased by 2.02%, 6.14%, 4.81%, and 4.62%, respectively. Although IMRA(XA101+FC) was 0.043 s slower than MR(101) in detection and segmentation speed, and 0.95 M more than MR (101) in model parameters, it could meet the task requirements. In addition, considering that the fine segmentation of the existing tapping line and the location of the new tapping line were based on the detection and segmentation results of the tapped area. Therefore, IMRA(XA101+FC) was determined as the improved Mask RCNN network model, MR(101) was the Mask RCNN network model, and the improved Mask RCNN network model was used as the pre-network for the fine segmentation of the existing tapping line.To sum up, all the detection and image segmentation indexes of IMRA(XA101+FC) were optimal in both the training and testing stages. The detection and segmentation accuracy of IMRA(XA101+FC) reached 98.23% and 99.52%, respectively. Compared with MR(101), AP3.2To further verify the detection and segmentation performance of the improved Mask RCNN network model on the tapped area of natural rubber trees, it was compared with the Mask RCNN, YOLACT, Cascade Mask RCNN, PointRend, Swin-B Cascade Mask RCNN, FCN, and DeepLabv3 models . The tesbox and APmask values of the improved Mask RCNN reached 99.6% and 99.78%, respectively. Compared with YOLACT, Cascade Mask RCNN, PointRend, Swin-B Cascade Mask RCNN, and Mask RCNN, the APbox value of the improved Mask RCNN increased by 0.8%, 0.8%, 0.6%, 0.7%, and 2.02%, respectively, and the APmask value of the improved Mask RCNN increased by 0.98%, 0.98%, 0.78%, 0.88%, and 2.81%, respectively. Compared with FCN and DeepLabv3, the APmask value of the improved Mask RCNN increased by 0.67% and 2.47%, respectively. This indicated that, among these models, the improved Mask RCNN model had the best detection and segmentation performance.It can be seen from As can be seen from box and APmask and had a better detection and segmentation effect on the tapped area of natural rubber trees, which indicated that the improved Mask RCNN had better detection and segmentation performance and that the network’s accuracy, robustness, and generalization performance were better.Through comprehensive comparison, the improved Mask RCNN model had higher values in AP3.3To further verify the detection effect of the improved Mask RCNN network model on the tapped area of natural rubber trees under different shooting conditions, the 560 images of the test set were divided into direct sunlight on sunny days, backlight on sunny days, cloudy days, the tapped area had been cut for one year, the tapped area had been cut for two years, and the tapped area had been cut for three years. The comparative experiment of the detection effect before and after the model improvement was carried out on the divided test set . The speIt can be seen from P, R, and IOU of the improved model reached 95.35%, 98.7%, and 94.16%, respectively, indicating that the tapped area of natural rubber trees under this condition was easier to detect by the model. However, on sunny days, the P, R, and IOU of the improved model were lower than those on cloudy days. On cloudy days, the color, texture, and profile of the tapped area of natural rubber trees were clear; improved model detection was less difficult; and detection and segmentation effects were better. However, on sunny days, due to the strong light and uneven light distribution, the tapped area experienced an exposure phenomenon. The shadow generated by the light overlapped with the tapped area’s color, and the color of the tapped area was similar to the background. Therefore, the improved model was more difficult to detect the tapped area under the conditions of direct sunlight and backlight on sunny days, and the detection and segmentation effects were poor. It can be seen from P, R, and IOU of the improved model were significantly higher than those of the tapped area that had been cut for one year, indicating that the tapped area of natural rubber trees that had been cut for two or three years was easier to detect by the improved model. The reason was that the tapped area of natural rubber trees that had been cut for two or three years was relatively large and conspicuous, which made it easy to detect, and the model detection and segmentation effects were better. However, the tapped area of natural rubber trees that had been cut for one year was smaller and less obvious than the tapped area of natural rubber trees that had been cut for two or three years, which made it difficult to detect, resulting in poor detection and segmentation effects of the improved model. In addition, the F1 and IOU values of the improved model proposed in this study were higher than those of the unimproved model under different shooting conditions, indicating that the detection and segmentation performance of this method was better than that of the unimproved model under different shooting conditions.The detection results of the tapped area before and after the model improvement under different shooting conditions are shown in Aiming at the situation that the improved model mentioned above has poor detection and segmentation effects under the conditions of direct sunlight on sunny days, backlight on sunny days, and the tapped area has been cut for one year, we will further improve the feature extraction ability of the model by expanding the training dataset in the future to solve this situation.3.4To verify the influence of different shooting conditions on the location accuracy and speed of the new tapping line, the images taken under different shooting conditions in the test set were tested. The location effects of the new tapping line are shown in It can be seen from The success rates of the location of the new tapping line of natural rubber trees where the tapped area had been cut for two and three years were 90.24% and 90.76%, respectively, which were higher than those of the tapped area that had been cut for one year. In the images of the natural rubber tree, where the tapped area had been cut for two or three years, the tapped area was a large target, which was more conspicuous and easy to detect, and the image processing process was relatively simple. The tapped area was a small target in the images of the natural rubber tree where the tapped area had been cut for one year, making it difficult to detect, and the segmentation effect was poor. As shown in Xmin, Ymin), ] of the optimal new tapping line. The pixel location error was calculated according to the formula (19)  . The locx and y directions and the total location error were 5.5, 7, and 8.2 pixels, respectively, and the average errors were 3, 2.8, and 4.5 pixels, respectively. Compared with the average errors, the maximum deviations were 2.5, 4.2, and 3.7 pixels, respectively, which indicated that the location errors in the x and y directions and the total location error were small and that change was relatively stable compared with the average error. Considering that the rubber tapping operation of natural rubber trees is an operation with high precision requirements, it is required to have a certain fault tolerance range when designing the rubber tapping end effector, and its location tolerance is 10 pixels  and  are the abscissa and ordinate coordinates of the rectangular area that successfully locates the new tapping line, xe is the abscissa error, ye is the ordinate error, and e is the total error.Where , the AP0.5, AP0.75, AP0.5-0.95, and IOU values of IMRA(XA101+FC) increased by 2.02%, 6.14%, 4.81%, and 4.62%, respectively, indicating that compared with ResNet50, ResNet101, and ResNeXt50 fused with the attention mechanism, ResNeXt101 fused with the attention mechanism had a stronger ability to extract features, and the object detection and segmentation performance of the model had been significantly improved. IMRA(XA101+FC), which changed the anchor box ratio and added a tiny FC layer branch to the mask branch, paid more attention to the target itself, increased information diversity, realized feature enhancement, had the best object detection performance, and improved the segmentation performance of the model.(1) Compared with MR(50), IMRB(XA50), IMRA(XA50+FC), MR(101), and IMRB(XA101) network models, IMRA(XA101+FC) had the highest AP and IOU values. Its AP(2) Compared with Mask RCNN, YOLACT, Cascade Mask RCNN, PointRend, Swin-B Cascade Mask RCNN, FCN, and DeepLabv3, the improved Mask RCNN model proposed in this study had better detection and segmentation performance. The detection accuracy, segmentation accuracy, detection average precision, segmentation average precision, and IOU values of the improved Mask RCNN were 98.23%, 99.52%, 99.6%, 99.78%, and 93.71%, respectively. In addition, under different shooting conditions, the F1 and IOU values of the improved Mask RCNN were higher than those of the Mask RCNN, indicating that, compared with the Mask RCNN, the improved Mask RCNN could better detect and segment the tapped area of natural rubber trees.x and y directions were 5.5 and 7 pixels, respectively, and the maximum value of the total location error was 8.2 pixels, which met the location accuracy and speed requirements of the natural rubber tree tapping machine.(3) The location results of 560 new tapping lines under different shooting conditions showed that the location success rate of new tapping lines on cloudy days was the highest, at 92.11%, which was 1.57% and 3.18% higher than that on direct sunlight on sunny days and backlight on sunny days, respectively. The location success rates of the new tapping line of natural rubber trees where the tapped area had been cut for two and three years were higher than those of the tapped area that had been cut for one year. Under different shooting conditions, the average success rate of the new tapping line location was 90%, the average location time was 0.189 s, the maximum values of the location errors in the At present, the method proposed in this paper can accurately detect the tapped area of natural rubber trees, but the network model is slightly larger, and the segmentation accuracy needs to be further improved. In future research, we will collect images of natural rubber trees of different varieties, expand the dataset of natural rubber trees under different conditions, and study methods to further simplify the network structure and improve the segmentation accuracy. For the location of the new tapping line of natural rubber trees, there is a situation of location failure. The reason for this is that the segmentation effect of the tapped area is poor. In future research, further research will be conducted on the segmentation of the tapped area and the localization of the new tapping line for natural rubber trees, and the method of locating the new tapping line by detecting the key points on the edge of the tapped area will be explored.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.YC: conceptualization, methodology, software, investigation, and writing – original draft. HZ: investigation and writing – review and editing. JL: writing – review and editing. ZZ: writing – review and editing. XZ: conceptualization, writing – review and editing, project administration and funding acquisition. All authors contributed to this article and approved the submitted version.


The device is recommended for a variety of biosensing applications due to its inert material properties, stable design and easy integration with fiber-optic setups.In this work, fabrication of a dielectric photonic crystal device and numerical study of its spectral characteristics as a refractive index sensor are presented for near infrared range. The proposed nanosensor device is composed of low-cost dielectric materials, i.e., silicon dioxide and niobium pentoxide, and is fabricated using focused ion-beam milling lithography. In the first part, the fabrication process of the device is discussed, along with the process parameters and their effects on the structural properties of the resulting photonic crystal elements. In the second part, the device is numerically tested as a sensor for the biological refractive index range of 1.33 to 1.4. The performance considerations of the biosensor device are studied for 12 different structural profiles based on the fabrication results. It is shown that the angular-wall-profile of the fabricated structures downgrades the performance of the sensor, and the optimum value of hole depth should be in the range of 930–1500 nm to get the best performance. A sensitivity of 185.117 nm/RIU and a figure of merit of 9.7 were recorded for the optimum design of the device; however, a maximum sensitivity of 296.183 nm/RIU and a figure-of-merit of 13.184 RIU As modern-day technology is moving towards compact, fast and lab-on-chip devices, demand for miniaturized, ultra-power-efficient and non-destructive sensing techniques is rising. Considering the above-mentioned merits, optical sensing and spectroscopy have already been adopted in many opto-electro sensing applications. All-optical sensing techniques have long been researched with the goal of exploring new meta-materials, spectral ranges and application areas. Among nanostructures, photonic crystals (PhCs) have proven to be one of the favorite candidates for sensing applications, due to their ability to manipulate and filter light at a wavelength scale. Recently, the application of PhCs in sensing has been a primary topic of research, and they have been used in gas sensing ,2,3, pla2) and niobium pentoxide (Nb2O5) were chosen for design of the presented nanosensor device. The discussed PhC structures work on the principle of guided-mode resonance (GMR), also known as Fano-resonance [This work focused on fabrication, numerical modeling and testing of dielectric PhC-based nanosensor devices for fluid sensing applications in the NIR spectral range. Considering their wide spectral range, low absorption, material cost and easy prototyping, two commonly available dielectric materials, i.e., silicon dioxide  was used for deposition. The primary ion beam sputters the desired materials, i.e., Si and niobium from the target maintaining a constant flux with a typical vacuum condition of 1.7 × 10−7 mbar inside the chamber. The substrate holder is placed at a distance of 30 cm from the target with a tilt angle of 45° and rotates at a rate of 30 rpm. A secondary ion source directs a plasma of oxygen and argon gas towards the substrate to enable deposition of SiO2 and Nb2O5. Primary ion beam currents of 72 and 75 mA were used for deposition of SiO2 and Nb2O5, respectively, with an acceleration voltage of 100 V. A secondary ion beam current of 10 mA was adopted for deposition of both SiO2 and Nb2O5. The duty cycle of the primary ion source was set to 45% for SiO2 and 72% for Nb2O5, whereas for the secondary source it was 17% for SiO2 and 13% for Nb2O5. The deposition rates of SiO2 and Nb2O5 were measured to be 3.54 and 1.68 nm/min, respectively. Since the device was designed to work in the NIR range, the refractive indices of the deposited thin-films were measured to be 2O5 and 2 layers [NVision 40 manufactured by Carl Zeiss SMT  was used for structuring of PhCs. The chamber is maintained at a vacuum condition of below 10−6 mbar and the substrate holder is oriented at an angle of 54° to make it perpendicular to the plane of ion-beam during the milling process. When the specimen is placed into the vacuum chamber of the FIB machine, the surface of the specimen is ground with the mounting stage to improve the accuracy of the process. As per the optimized numerical simulation model, a software-based mask was designed for lithography on the standard GDSII compatible software provided by Raith Elphy [a = 1 µm. During the milling process, the deflection of the ion beam -based simulation software called MIT electromagnetic equation propagation (MEEP) . The numor point c basicalA perfectly matched layer (PML) boundary condition is used in The sensing capabilities of the fabricated nanosensor device may deviate from the standard theoretical model due to modifications in the structural and material properties of the PhC structures during FIB processing ,37,38. T−1.The performance characterization of the device for sensing of biological fluids was assessed by calculating the variations in resonant wavelengths As per the standard numerical model, the patterned air holes should be cylindrical. However, the fabricated structures may possess angular wall profiles following the material properties and the ion-beam currents used in prototyping. The shape of the PhC element affects the quality of Fano-resonances and the location of The electromagnetic (EM) field distributions in the PhC structures during the occurrence of Fano resonance in relation to the change in the PhC element shape from cylindrical to conical is shown in c shows that the hole shapes with −1 as the values of −1.A detailed analysis of the sensing performance of the device in terms of −1 can been seen for the straight wall profile of the air holes, and it turns out to be non-linear as the shape of the holes becomes conical.Practically, it is almost impossible to achieve a straight wall profile for PhC elements deeper than 1 µm using FIB technology and working with dielectric materials. Therefore, the performance of the sensor device was also investigated for deeper PhC holes of −1 for Precise control over the depth of the PhC elements in the nm range while using high-energy particles for material processing is very challenging. During FIB milling, the overall depths of the structures can deviate from the desired values due to various parameters, such as material properties, ion-beam current and area dose ,37,38. TThe high-energy particle beams such as FIB technologies are also corrosive to the surface of the specimen ,38. ThisMoreover, the plot for linewidth in  for FOM d is lined and cladding layer thickness c. The performance parameters are listed in terms of S range, FOM range and differences in values of S and FOM for each tested parameter. The variations in S and FOM were calculated as the differences between the maximum and minimum values of these parameters obtained from the simulation results. The best performance parameters are listed in bold font in To find the optimal performance parameters of the proposed sensor device, the S and FOM values against all the tested structural parameters are listed in 3N4/SiO2 [3O5/SiO2 [2 and Nb2O5.The performance of a sensor is highly dependent on the physical properties of the materials used and the structure of the sensor device. In general, sensors composed of metals and high-refractive-index materials offer higher values of S and FOM. This is because the optical field can attain good confinement in high-index media and the field coupled in the low-index cladding layers can only interact with the evanescent field of the resonant modes. Dielectric materials typically have a low refractive index and hence offer low-index contrast between the material layers, resulting in lower values of S and FOM. However, to achieve a cost-effective sensor design with low absorption, higher temperature tolerance and inert chemical properties, dielectric materials are always preferred. To evaluate the performance of the proposed sensor device, a comparative analysis of this work and previously reported work, mostly on dielectric materials, is shown in 3N4/SiO2  and Ti3O3O5/SiO2  with S v3O5/SiO2 , PhCs on3O5/SiO2  and 2D P3O5/SiO2  are also3O5/SiO2  and a 2D3O5/SiO2  are repo−1, respectively. However, during fabrication, keeping in mind the hardness of dielectric materials and process durations, an ion-beam current of 700 pA or above is recommended for prototyping of such a sensor device, which may result in a conical hole shape due to angular wall profile, and the depth of the air holes may also slightly deviate from the ideal value. Considering these fabrication artifacts, it is concluded that the sensor device can perform well with S in a range of 185 nm/RIU and FOM near 10 RIU−1 if the hole shape deviation is kept near In conclusion, a low-cost, dielectric PhC-based nanosensor was fabricated and numerically investigated in terms of its performance characteristics as an index sensor for a biological refractive range of 1.33 to 1.40. The FIB process parameters influencing the structural properties of the fabricated structures and their effects on the sensing response of the device were thoroughly investigated. For an optimum structural design of the nanosensor device with a cylindrical hole shape of


The results showed that the slurry fluidity decreased but the compressive strength gradually increased when the content of slag was increased from 20% to 50%. With the increase in alkali content , the slurry fluidity decreased and the compressive strength increased and then decreased. Combined with the analysis of the test results of Scanning Electron Microscopy (SEM), the microscopic structures of mechanical properties of geopolymer were investigated. Lastly based on ridge regression theory, a regression model was established to predict the mechanical properties of fly ash-slag-based geopolymer. The results indicate that fly ash-slag-based geopolymer has good mechanical properties and fluidity with proper contents of slag and alkali activator, which provide a reference for experiment research and engineering application.Fly ash-slag-based geopolymer is a grouting material with good fluidity and excellent mechanical and eco-friendly properties. The geopolymer can react chemically with the inert minerals of road subgrade under alkali excitation to form a good interfacial bond between road subgrade; therefore, it is suitable for the repair of weak road sections. In order to solve the problems such as the difficulty to store and transport the liquid activator of existing geopolymer grouting materials and to study the unclear mechanism of the influence factors on the fluidity and mechanical properties of geopolymer; the research on the mechanical properties of fly ash-slag based geopolymer was carried out in this paper. Experiments on the preparation of geopolymer and research on different ash-slag ratios under solid alkali excitation were studied. The influence of slag content and solid alkali content (NaOH, Na Every year, under the action of traffic loads and natural factors, highways have many diseases such as subgrade debonding a, subgramissions . Since tGeopolymer is a new type of gelling material with the main components of inorganic silica-oxygen tetrahedra and aluminum-oxygen octahedra. It has a three-dimensional network structure in space, which is formed by the polymerization of natural minerals, solid wastes and artificial silica-aluminum compounds as raw materials through strong alkali action and lattice reconstruction, as shown in Due to its low density, low thermal conductivity, and low coefficient of temperature expansion, geopolymer has been widely used in aviation, civil engineering, ceramic arts, and nuclear waste containment engineering. With differences in raw materials and manufacturing processes, geopolymer has a wide type range and each has significant advantages and disadvantages, resulting in different suitable application fields. Research shows that fly ash, slag, construction waste and some other industrial wastes with low energy consumption can be used as raw materials for the development of geopolymer, which can save energy by more than 75% and reduce emissions by more than 95% . So, theAs a common precursor of geopolymer material, the compressive strength of geopolymers formed from different types of fly ash is related to the variation of fly ash in particle size distribution, chemical composition, morphological properties and amorphous phase . It has 2O content, slag content and other factors. Among them, the alkali activator modulus  and concentration are the most important factors affecting the mechanical strength of geopolymer nodules [For fly ash-slag-based geopolymer grouting materials, good mechanical properties such as flexural and compressive resistance are important to reinforce the subgrade and improve the overall bearing capacity of roads. However, a variety of factors affect the mechanical performance of the geopolymer, such as the modulus and concentration of alkali activator, Na nodules . The var nodules . The inf nodules , and the nodules . Some sc nodules . In orde nodules . The app nodules . Some sc nodules ,25,26,27In addition, the fluidity of the geopolymer grouting materials determines whether the slurry can penetrate into the pores and cracks of the subgrade, therefore, determining the reinforcement effect of the subgrade. Many scholars’ studies have shown that the increase in slag and alkali activator content accelerates the polymerization reaction and generates a large amount of C-(A)-S-H gel in a short time and leads to poor flowability of the fly ash geopolymer slurry ,29. The In the above-mentioned research, scholars mostly use the “Two-step Method- liquid activator”  to prepa2SiO3) was used as the activator in this study to investigate the mechanical properties and fluidity of fly ash-slag based geopolymer grouting materials. The changes in the mechanical properties and fluidity with the influencing factors such as slag and solid alkali content were researched, and the microscopic morphology of specimens with different slag content and different alkali content with a Scanning Electron Microscope (SEM) were studied to explore the related mechanism. According to the test results, the ridge regression model of slag content, solid alkali content and mechanical properties of fly ash-slag-based geopolymer grouting materials was established to predict the macroscopic mechanical properties of geopolymer.In view of this, in order to solve the problem that the liquid exciter of existing geopolymer grouting materials is not easy to store, and the mechanism of the influence of solid exciter dosing and slag dosing on the fluidity and mechanical properties of geopolymer is not clear, solid alkali  and reactive silica-alumina materials  through geochemical-like reactions at room temperature. The raw materials in this paper are Class F fly ash ; v is the volume of water (L); 2O in solid mass and molar mass of Na2O in Na2SiO3; 2 in solid Na2SiO3; In this paper, the content of two solid activators is converted into the concentration of NaCuring: ① After final solidification, we put the geopolymer into a standard curing box with a temperature of 20 ± 2 °C and a relative humidity ≥ 95%, and removed the mold after 1 day; ② specimens included standard ones and steaming ones, of which the standard specimens are put into the standard curing box until a specified time while the steaming ones are put in an 80 °C steaming box for 2 days.Research ,20,32 ha2SiO3 on the performance of fly ash-slag-based geopolymer grouting materials, the single-factor gradient test method is adopted in the paper. That is, each of the three test groups changes one variable and fixes the other two variables so as to investigate the influence law of this variable on the performance of geopolymer grouting materials.In order to investigate the effect of the variation in the content of slag, NaOH and Na2SiO3 are fixed. The slag content is set into four gradients from 20% to 50% to investigate its effect on the fluidity and mechanical properties and use it as an indicator to select the slag content. Secondly, the contents of slag and Na2SiO3 are fixed and the content of sodium hydroxide is varied from 2% to 5% to investigate its effect on the mechanical properties and flowability. Then the step is the same with Na2SiO3 and the specific experimental scheme is shown in In the test, the slag content is first changed and the contents of NaOH and NaReferring to China Building Materials Industry Standard (JC/T0508-2005) “Cement Slurry Flow Determination Method” (flow cone method), the flow rate was defined as the time for the slurry flowing out of the flow cone. Firstly, the flow cone is placed vertically and the small opening below is blocked with a wooden plug, and the freshly prepared geopolymer grouting materials are poured into the flow cone with a total volume of 2000 mL to the calibration surface (Referring to the Chinese Standard (GB/17671-1999) “Cementitious Sand Strength Test Method”, the flexural and compressive properties of samples are tested by a cement mortar flexural/compressive tester with a flexural loading rate of 0.05 kN/s and a compressive loading rate of 2.4 kN/s  and three-dimensional mesh structures , the material mobility loss is too fast and the setting and hardening time is greatly advanced. That leads to a large number of fly ash particles and slag particles being wrapped by hydration products before dissolution, while the diffusion of Si-O and Al-O tetrahedral monomers that have been dissolved is inhibited and no further reaction can occur. So, the excessive alkali activator will reduce the degree of polymerization of the geopolymer and make its strength decrease. Therefore, with the increase in NaOH and Na2SiO3, the 28d and steaming strength show a trend of first increasing and then decreasing.In the process of alkali excitation, alkali solution dissolves fly ash particles and slag particles from the outside to the inside, and Cae stable ,37. Ther2SiO3 content is that when the content of Na2SiO3 < 3%, the C-S-H gel formed by the reaction between SiO32− and dissolved Ca2+ is also reduced, which is not conducive to the growth of early strength. When the Na2SiO3 content > 3%, the surplus SiO32− forms mSiO2-nH2O (silicate gel) attached to the surface of mineral powder and fly ash particles, which will hinder the dissolution of Ca2+ and reduce the amount of C-S-H gel generation [2SiO3 content is varied from 1% to 6%, the early strength of the geopolymer grouting materials has a fluctuating change that first slightly increases and then decreases.The calcium-rich phase in the slag determines that the early strength  of the fly ash-slag-based geopolymer mainly comes from the slag. When the content of slag is certain (40%), NaOH changes from 2% to 5%, and the initial alkali environment within the slurry is also gradually enhanced, but the calcium phase materials provided by the slag are certain. So when the initial NaOH content can meet the reaction of the calcium phase materials, the early strength has reached a high level. Even if the NaOH continues to increase, the early strength is always maintained at the same level. The reason why the early strength fluctuates with the change in Naneration ,39. TherIn addition, it can be seen from 2SiO3 is from 0% to 3%.The effect of slag content on the flexural strength of fly ash-slag-based geopolymer grouting materials at different ages is shown in 2SiO3 content. When the content of the alkali activator is low , the flexural strength increases and then decreases with the increases in curing age, and all of them reach the maximum value on the seventh curing day. When the content of alkali is high , flexural strength tends to increase with the curing age. This may be due to the low content of the alkali activator, and therefore, the low reaction rate of the geopolymer and the sufficient time to form a dense spatial structure during the hydration of C-S-H gels and sodium silicate gels [ate gels . Then thThe flow rate of the slurry is defined as the time required for the slurry to flow out of the inverted cone in this paper. For grouting materials, the flow rate requirement  has to be met to achieve a better filling bonding effect.− and SiO32− in the solution to produce Ca(OH)2 precipitate and hydrated calcium silicate gel [This is due to the physical water absorption characteristics of slag, increasing the slurry consistency and decreasing the fly ash content, then weakening the “micro-bead effect”. The so-called “micro-bead effect” is that fly ash contains a large number of glass beads of which the particle form is mainly spherical, as shown in cate gel ,44, thus2SiO3 on the flowability of the geopolymer slurry. From the figure, it can be seen that the flow rate of slurry tends to increase with the increase in NaOH and Na2SiO3 content, and the slurry fluidity gradually decreases.− and SiO32− concentrations, and the faster the condensation and hardening process of the geopolymer materials, therefore, the fluidity decreases. The reason can be explained as follows: the increasing solubility of the silicon-aluminum phase and calcium-rich phase components in the fly ash and slag lead to released Ca2+, Si-O and Al-O tetrahedral monomers diffusing into the solution and forming hydrated aluminate and hydrated silicate monomers through chemical hydration reactions and physical electrostatic reactions [Davidovits put forward that the reaction process of geopolymer materials in the presence of an alkali activator includes four steps: dissolution, diffusion, condensation, and hardening, and the increase in alkali activator increases the rate of the polymerization reaction . The higeactions . At the 2SiO3 content changing from 0 to 6%. This also shows that the initial state of the slurry gradually deteriorates and the flowability of the slurry gradually decreases with the increase in the alkali activator content.In this paper, SEM is used to analyze the microscopic morphology of 28d geopolymer grouting materials with different slag and alkali activator contents to study the morphology of the hydration products of geopolymer slurry hardening, which reveal the microscopic formation of its mechanical properties.2+, and the gel products such as hydrated calcium silicate (C-S-H), hydrated calcium aluminate (C-A-H) and hydrated calcium silicate aluminate (C-(A)-S-H) are gradually increased. Then, the reaction level and polymerization degree of the system is gradually improved. The internal hydration products of the geopolymer are sufficient to fill the pores between the undissolved particles. Moreover, the structure of the geopolymer slurry becomes denser, so the mechanical properties are gradually improved with the increase in slag content. Therefore, the increase in slag content can increase the gels of hydrated calcium silicate (C-S-H), hydrated calcium aluminate (C-A-H) and hydrated calcium silicate aluminate (C-(A)-S-H) in the system, increase the disorder in the system structure and reduce the structural porosity. Then the compressive strength of the geopolymer grouting materials is improved, which is consistent with the law obtained in When the slag content gradually increases to 50%, the active materials in the geopolymer system increase to provide a large amount of Ca2SiO3 content. As can be seen from 2SiO3 in the system, the overall alkali environment is weak when the NaOH content is 2%, which is not enough to fully excite the active substances within raw materials. At this time, the reaction degree is low, and the gel products such as hydrated sodium silicate aluminate (N-A-S-H) and hydrated calcium silicate (C-S-H) are insufficient, resulting in more voids inside. When the NaOH content is 3.5% and 5%, the gel products such as calcium silicate hydrate (C-S-H) and calcium aluminosilicate hydrate (C-(A)-S-H) increase, and the denseness of the structure is significantly improved. However, compared with the former, there are still some “holes” and “pits” in the microscopic morphology of the latter. This is most likely because the overall reaction process is accelerated by the excessive amount of alkali used. The dissolved active material and the generated hydration products have been condensed and hardened before they spread evenly, and the unresolved particles wrapped in them cannot continue to be dissolved, so the overall reaction degree is reduced, resulting in these “holes” and “pits”.2SiO3 content is from 0 to 3%, the microstructure of the geopolymer slurry is dense, but its content is high which also reduces the degree of polymerization, resulting in a large number of “holes” that are not conducive to the structure. In addition, from As can be seen from 2SiO3 content. The microscopic performance of NaOH and Na2SiO3 is consistent with the macroscopic change law of compressive strength in From the microscopic results, it can be seen that when the content of the alkali activator is low, the reaction products in the system are few and cannot form a dense structure, so the strength of the geopolymer materials is low. With the increase in alkali activator content, the reaction products gradually increase, which in turn, improves the strength of the materials. When the content of the alkali activator is high, the speed of solidification and hardening of geopolymer is accelerated, which inhibits the hydration reaction of slag and fly ash, resulting in a lower degree of polymerization of geopolymer, so the strength of the material decreases. Therefore, the mechanical properties of the geopolymer slurry increase and then decrease with the increase in NaOH and NaIn this section, the model of the unconfined compressive strength of fly ash-slag-based geopolymer grouting materials is constructed based on the ridge regression theory, and the validity of the model is verified based on the test results.In this paper, a ridge regression algorithm with L2 regularization is used to establish a regression prediction model for the unconfined compressive strength of fly ash-slag-based geopolymer grouting materials at different ages. Among them, the multiple regression equations for modeling are as follows:The coefficients of the multiple regression equation in the ridge regression algorithm are solved using a loss function with L2 regularization (see Equation (2)), which can weaken the effect of higher-order features while preserving all model features. The coefficients are solved when the sum of squared errors between the predicted and actual values is minimized.2SiO3 content are taken as the basic characteristic parameters and the unconfined compressive strength of each age is taken as the target value Y. To ensure the accuracy and generalization of the model, the basic characteristics are quadratic and standardized. The weight coefficients of each feature obtained and bias coefficients from the final model are shown in In this paper, the slag content, NaOH content and NaIn this paper, the coefficient of determination (COD) and mean square error (MSE) are used as indicators to evaluate the prediction accuracy of the ridge regression model. The evaluation indexes of the prediction models for each age are shown in From (1)The influence law and mechanism of the unconfined compressive strength of geopolymer grouting materials are following:In this paper, the mechanical properties and flowability of fly ash-slag-based geopolymer grouting materials are studied. Combined with the experimental data, a mechanical property prediction model is established based on the ridge regression theory to realize the prediction of the unconfined compressive strength of fly ash-slag-based geopolymer grouting materials at each age. The conclusions are as follows:2SiO3), the degree of polymerization increases first and then decreases, so the 28d unconfined compressive strength increases first and then decreases; the compressive strength of the geopolymer grouting materials at 1d, 3d and 7d did not change significantly with the increase in NaOH content but fluctuated with the increase in Na2SiO3 content. The microscopic structures based on SEM results verify the influence mechanism of mechanical properties. A regression prediction model of the unconfined compressive strength of different ages is established in the paper, which has good agreement with experimental data.The increase in slag content can generate a large number of gels such as C-S-H and C-A-S-H to increase the disorder of the geopolymer system, thus improving the unconfined compressive strength of the geopolymer grouting material at all ages. With the increase in alkali activator The influence law and mechanism of the flexural strength of geopolymer grouting material are as follows:2SiO3 ≤ 2%), the flexural strength increases and then decreases with the increases in curing age, and all of them reach the maximum value on the seventh curing day. When the content of alkali is high , flexural strength tends to increase with the curing age.With the increase in slag, the flexural strength of the geopolymer of 1d, 3d and 7d increases and reaches the maximum value on the seventh day. The flexural strength of 28d and steaming first increases and then decreases. The microscopic structures based on SEM results verify the influence mechanism of flexural strength. When the content of the alkali activator is low The influence law and mechanism of the fluidity of geopolymer grouting materials are following:2 precipitation and hydrated calcium silicate gel, increasing the consistency of the slurry. With the increase in alkali activator content, the fluidity of the slurry also gradually decreases. This is because the increase in alkali activator accelerates the reaction process of the geopolymer, which generates more hydration products at the same time and reduces the free water content, leading to the decrease in slurry fluidity.The fluidity of the slurry gradually decreases with the increase in slag content. This is because the increase in slag content not only weakens the “micro-bead effect” of fly ash but also introduces a large amount of calcium-rich phase materials, which promotes the generation of Ca(OH)


Anatase TiO2 generally has superior photocatalytic activity to the rutile phase. However, the anatase phase can be irreversibly transformed to rutile phase when calcined at an elevated temperature. Methods to improve the stability of anatase are especially important for the TiO2 gas sensors working at high temperatures. The addition of strong acids can effectively suppress this transformation process. However, these strong acids are relatively expensive, corrosive and environmentally unfriendly. Herein, oxalic acid (OA) as a natural acid was used to control the hydrolysis process of tetrabutyl titanate (TBOT), leading to controllable crystalline phase transformation and reduced crystalline size of TiO2 on the nanoscale. What is more, the photocatalytic degradation performances were enhanced continuously when the molar ratio of OA to TBOT increased. The degradation reaction rate constants of CT650-R25 were about 10 times that of CT650-R0. The mechanism study shows that the enhanced photocatalytic activity can be attributed to the improved dispersibility, increased specific surface area and reduced recombination rates of photo-induced charge carriers and decreased energy bands as the concentration of OA increased. Thus, this work provides a simple, mild and effective method for controlling the crystalline forms of nano-TiO2 with enhanced photocatalytic performance towards waste water treatment.Degradation of organic pollutants, especially organic dyes and antibiotics, by semiconductor photocatalysts is an efficient strategy for wastewater treatment. TiO However, the R25 sample not only continued to maintain the crystal form of anatase, but had improved crystallinity. For the same precursor calcined at different temperatures, the evolution of the XRD spectra is also exhibited in When the CT reached 750 °C, the characteristic peaks d of ruti2 crystals by OA was fully reflected. Thus, the properties of these samples were focused on. The weight fractions of rutile and anatase phase (WR/A) for the mixed-phase TiO2 were calculated according to Formulas (2) and (3), where IR and IA represent the intensities of the characteristic diffraction peaks corresponding to the rutile (110) and anatase (101) crystal planes, respectively , the temperature of phase transformation from anatase to rutile TiO2 regulated by OA, the samples calcined at 650 °C were selected for the degradation of MO and CT. After dark reaction for 30 min, the absorbance spectra  vs. time in 2 crystals increased.In order to evaluate the photocatalytic performance of nano-TiO spectra  of MO in−1, respectively. It was confirmed again that the higher the OA content in the precursor, the higher the photocatalytic activity of the calcined nano-TiO2 samples. The degradation effect of sample T650-25 was still the best.The degradation experiments of TC were also performed. The evolution of the absorbance spectra of TC in the supernatants is shown in 2 regulated by increasing [OA], the Eg of samples CT650-R0, CT650-R5, CT650-R15 and CT650-R25 were analyzed firstly based on the DRS in g of the photocatalysts can be calculated by Formula (4)  increased, the specific surface area of nano-TiO2 increased. The order of the specific surface area data is consistent with the order of the photocatalytic activity in 2.For catalysts, a larger specific surface area means more abundant catalytic binding sites, which are more conducive to the catalytic reaction. OA is a kind of polyoxyacid. In addition to regulating the crystal form, the effect of OA on the surface area cannot be ignored. The specific surface area data for each sample are shown in −-h+ pairs will be released in the form of photoluminescence (PL), the PL spectra of the TiO2 samples were recorded in −-h+ pairs of this sample was the lowest. The peak fluctuation of CT650-R15 is significantly lower than that of CT650-R5 and CT650-R0. The peak area of CT650-R5 is slightly lower than that of CT650-R0. The perspective of fluorescence quantum yield is basically consistent with the reverse order of photocatalytic activity of the samples in Since the energy after the recombination of photo-excited e2. The further study showed that OA could enhance the photocatalytic performance of TiO2 mainly in three aspects. First, the addition of OA can effectively reduce the Eg of TiO2 by C doping in the crystals. Second, the addition of OA can increase the specific surface area of TiO2, indicating more abundant the catalytic binding sites. Third, the more OA added, the less the photo-induced charge carriers are recombined, thereby improving the catalytic utilization of carriers.Therefore, the addition of OA can effectively control the crystalline forms, structure and photocatalytic performance of TiO2 samples was achieved by using OA. The study shows that the addition of OA can prevent the crystalline form’s transformation from anatase to rutile effectively, which is important for the applications of anatase at high temperatures. The transformation temperature increased gradually when increasing the ratio of [OA] in hydrolysis process of TBOT. At the same CT (650 °C), when the molar ratio of OA to TBOT increased from 2:10, to 5:10, to 15:10 to 25:10, the WA was tuned from 0% to 100%, and the photocatalytic activity could be significantly enhanced. The mechanism study showed that an increased [OA] can significantly improve the dispersibility of the catalyst, increase the specific surface area, reduce the recombination rates of photo-induced charge carriers and decrease the Eg by doping C. Thus, the purpose of improving the photocatalytic performance was achieved. This work provides a mild and simple synthetic method for controlling the crystalline forms and photocatalytic performance of nano-TiO2 effectively.In conclusion, controllable adjustment of the dispersion and transformation temperature of crystalline forms and photocatalytic performances of the nano-TiO


The core concept of CO2 utilization is to reduce the influent pH and to minimize the risk of scale formation from magnesium or calcium salts. Three reverse osmosis (RO) units were operated with a control (U1), CO2 (U2), and a commercial antiscalant, MDC-220 (U3). The performances of all the units were compared in terms of change in transmembrane pressure (TMP). The overall efficiency trend was found as U1 > U3 > U2. The membrane surfaces were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) for the morphological and elemental compositions, respectively. The surface analysis signified a significant increase in surface smoothness after scale deposition. The noticeable reduction in surface roughness can be described as a result of ionic deposition in the valley region. A sludge-like scale layer was found on the surface of the control membrane (U1) which could not be removed, even after an hour of chemical cleaning. After 20–30 min of cleaning, the U2 membrane was successfully restored to its original state. In brief, this study highlights the sustainable membrane process developed via CO2 utilization for scale inhibition, and the appropriate cleaning approaches.Scaling, or inorganic fouling, is a major factor limiting the performance of membrane-based water treatment processes in long-term operation. Over the past few decades, extensive studies have been conducted to control the scale growth found in membrane processes and to develop sustainable and greener processes. This study details the role of CO Global population growth and uncontrolled water and energy consumption have resulted in serious environmental and health consequences . The pop4), calcium carbonate (CaCO3), magnesium hydroxide (Mg(OH)2), strontium sulfate (SrSO4), barium sulfate (BaSO4), and silica (SiO2) [Scaling, or inorganic fouling, refers to the nucleation, crystallization, and gathering of mineral deposits on a membrane surface . Once tha (SiO2) .Scale deposition deteriorates the operational efficiency of membrane-based water treatment processes, if not addressed in a timely manner. Following scale development, an increase in salt permeation, a decrease in permeate flux, or a rise in transmembrane pressure (TMP) may result in the production of low-quality product water and higher operational and maintenance costs . Membran2-saturated solution to clean the biofilm and particulate fouling from the spacer channels of the RO membranes [2 nucleation indicated a higher cleaning efficiency when compared with N2 sparging and water rinsing. In short, the air micro–nano bubbles and dissolved CO2 solutions were found to be highly effective for removing the biofilm and particulate foulants from the membrane; however, their role in the inhibition of scale formation in membrane-based water treatment systems was not examined.Earlier studies have presented the application of air micro–nano bubbles as a greener approach to improve the cleaning efficiency of RO processes . The micembranes . The CO22 as a green approach to control the fouling in reverse osmosis operation during wastewater reclamation [2 in RO processes is to reduce the influent pH and to minimize the risk of scale formation from magnesium or calcium salts. The main objectives of this study are to examine (a) the operation of RO units with a constant flux mode of operation, and (b) the practical applicability of different cleaning strategies to restore the initial state of the fouled membranes. Three RO units were operated with a control, CO2, and an antiscalant, respectively. A detailed membrane autopsy was conducted to identify the nature and extent of scale deposition on the surface of the membranes. Moreover, different cleaning protocols (CP) were adopted to determine the best-suited sustainable CP for fouled membranes. The outcomes of this study signify the practical application of the proposed green scale-inhibition approach to membrane-based water and wastewater treatment systems.In our earlier study, we applied COlamation . We exam2PO4, 0.08 mM of MgSO4, 0.30 mM of KNO3, 1.14 mM of KCl, 3.4 mM of MgCl2, 5 mM of CaCl2, and 20 mM of NaCl. Hypersperse™ MDC 220, a commercial antifouling agent, was acquired from GE Company, Shanghai, China. It is worth mentioning that MDC 220 is a widely applied antiscalant in wastewater reclamation plants in South Korea. It contains phosphonic acid, (1-hydroxyethylidene) bis-sodium salt (C2H7NaO7P2). It has specific gravity of 1.169, and is completely soluble in water. It is suggested for all kinds of RO membrane by manufacturer (GE Company). The RO membrane filters were purchased from HYUNDAI Wacortec, Seoul, South Korea, and the membrane specifications are described in Analytical research-grade reagents and chemicals were obtained from Fisher Scientific and utilized as received. The synthetic wastewater was used as feed for RO units. Considering the potential application of this study in advanced wastewater reclamation processes, the feed water quality was maintained in line with the quality of tertiary-treated effluents of domestic wastewater treatment plants. The wastewater was prepared using reagent-grade chemicals. The molar concentration of the chemicals added into the feed water included 0.07 mM of KH2, and an antiscalant, and categorized as U1, U2, and U3, respectively. The influent flow rate and permeate flux were maintained at 40 L/day and 2.32 LMH, respectively. The recovery rate was adjusted to 56% and the RO units were operated at 2 bar initial TMP. The influent pH was 7.00 ± 0.10 for U1 and U3, and was reduced to 6 by the addition of CO2 in U2. The CO2 injection rate was 150 mL/min, and nearly 1 min injection time was consumed to reach the desired pH level. Once the pH of the feed water was stabilized, U3 was operated. The RO membrane filters were rinsed prior to being applied to wastewater filtration. Three single-pass, stand-alone RO units were operated with a control, COA membrane autopsy was conducted to identify the nature of fouling during the filtration process. The membrane element was instantly opened and prepared for examination after the RO unit was shut down. The membrane element was removed from the pressure vessel (membrane housing) and then both surfaces of each leaf of the membrane module were carefully examined for any physical damage. Later, several small membrane pieces were cut from various sheets, and the fouling layer was examined with advanced instrumental techniques.The pH and conductivity were analyzed using a 96pH-L2  and an EC96 , respectively. The concentrations of cations and anions in the water samples were determined with Thermo Scientific™ Dionex™ ICS-1000 and Thermo Scientific™ Dionex™ ICS-5000 , respectively. The surface characterization of the RO membranes was performed using Scanning Electron Microscopy  coupled with Energy Dispersive Spectroscopy (EDS), manufactured by Hitachi Ltd., Tokyo, Japan. The surface-roughness statistics of the virgin and fouled membranes were determined by considering the topographical images taken using SEM analysis.2.The operational performance of all the units was examined for 15 days and the process efficiency was identified based on the change in TMP. All units were operated at 2 bar initial TMP to achieve 56% recovery. The control unit (U1) indicated successful operation for the initial seven days, as shown in 2+, Ca2+, etc.) can pile up and the subsequent precipitation of ions can leave serious impressions on the mass transport phenomena [As all the units were operated in constant flux mode, an increase in the duration of filtration and, subsequently, ionic precipitation, seriously influenced the TMP. The rise in TMP is assumed to be the result of the consistent deposition of multivalent ions onto the surface of the membrane. In addition, the contribution of concentration polarization to reducing the process efficacy after several cycles of filtration cannot be ignored. Higher concentration polarization in the neighborhood of membrane surfaces triggers the excessive accumulation of solute concentration at the membrane surface, which conspicuously aids the progression of a cake layer . As conchenomena .2 molecules are known to play a role in the demolition of deposited layers of scale on the surfaces of RO membranes [2 nucleation vicinities, thus lowering the free interfacial energy essential for CO2 nucleation. Moreover, the fouling layer evolved onto the membrane surface may also serve as a substrate for CO2 nucleation [2 molecules would, in all probability, travel into the apertures inside the scale layer, prompting the nucleation, progression, and extrication of CO2 molecules. Likewise, the surfeit presence of voids might be prevised in a porous cake layer compared to a thick gel layer, therefore encouraging CO2 nucleation and the resultant preservation of permeate flux. The fouled RO membranes were thoroughly washed with reported cleaning protocols [In addition, to reduce the pH of synthetic wastewater, dissolved COembranes . The voicleation . The disrotocols . The conIn the visual examination of the spiral-wound membrane element, outer wrap, brine seal, pressure vessel, and perforated product tube, no physical damage was found, which confirmed the nonexistence of any pressure-induced damage. The membrane elements were unfolded and inspected layer by layer. Upon the shutdown of all the units, the membrane surfaces were investigated in order to identify the nature of ionic deposition. The morphology of the scale deposits and their elemental composition were identified using SEM and EDS, respectively. The surface analysis of the virgin and used membranes exhibited a prominent difference in morphology of scale . The sur2 introduction in the influent of membrane-based water treatment systems can significantly reduce membrane fouling. The composition of feed water also affects crystal morphology.Based on the morphological inspection, the trend for scale load on the membrane surfaces from all the units can be drawn as U1 membrane > U3 membrane > U2 membrane. Most of the surface area of the U2 membrane was either free of foulants or somewhat exposed to foulants. This confirms that CO3 [2 is highly effective for scale inhibition.The elemental analysis of the fouling layer was conducted to understand the chemical composition of the foulants. The virgin polyamide membrane contains the major fraction of carbon, nitrogen, oxygen, and sulfur in its structure. Hence, these elements were ignored when the elemental analysis was conducted. The accumulated foulants were made up of different inorganic ions, including calcium, magnesium, phosphorous, sodium, and chloride. The membrane separated from U1 carried a major share of the calcium and magnesium. The scale formed on the surface of the U1 membrane was found to have many inorganic elements, including calcium, magnesium, phosphorus, sodium, and chloride that were at averages of 37.38%, 12.46%, 29.69%, 6.23%, and 14.22% by weight percentages, respectively . The maj3 . The hig3 . A relat3 deposits were observed on the membrane surface of U3, while little deposition was observed on the membrane surface of U2 (The fouled membranes were cleaned using 0.1% NaOH, 0.2% HCl, and 1% EDTA solutions. The membrane specimens were obtained from different locations on the membrane element. The membrane samples were kept in cleaning solutions for a certain time, and then the surface examination was conducted. Cleaning was conducted for different sets of contact times, ranging from 10 to 60 min. Initially, cleaning was carried out for 10 min. The membrane surface of U1 was found to be highly spoiled and fully covered by a slurry-like precipitation. The ineffectiveness of chemical cleaning on the membrane surface of U1 indicated the highly compact and crowded crystallization of small particles on the surface of the membrane. A number of CaCOce of U2 .The 10 min CP was found to have successfully removed most of the deposits from the surface of the U2 and U3 membranes. A more enhanced cleaning efficiency was achieved with a 20 min contact time. A slurry-like film with breaks was observed on the surface of the U1 membrane, which showed the slight effect of chemical cleaning. The U2 and U3 membranes were found with a minor existence of foulants. The calcium carbonate structures were observed on the membrane surface of U3, which shows that the membrane was carrying a significant amount of scale prior to cleaning practices.All the depositions from the surface of U2 were successfully removed by chemical cleaning for 30 min. The membrane surface of U1 was partially covered with a sludge-like layer . MoreoveThe surface roughness of the membranes was calculated based on the topographical images presented in 2 for the inhibition of inorganic scale growth in membrane-based water and wastewater treatment systems, and the effectiveness of cleaning protocols for the restoration of fouled membranes. CO2 was introduced into the feed water of the RO unit to decrease the solution pH and to control the fouling potential of the system. Three RO units were operated with a control, CO2, and a chemically synthesized antifouling agent. The operational performance of all the units was compared in terms of change in TMP and surface morphology. The efficiency trend for all the units was found as U1 > U3 > U2. The outcomes of this study confirmed that the fouling growth and development of the cake layer on the surface of the membrane was successfully inhibited when the feed water was conditioned with CO2. The results obtained from the morphological examination of the used membranes agreed with the operational data. The excess amount of scale identified on the surface of the control membrane was difficult to completely remove with chemical cleaning, even with a 60 min contact time. The U2 membrane showed a successful restoration of the membrane surface with 20–30 min cleaning. The surface roughness of the virgin membrane was decreased after the operation of the membranes; however, it was reinstated after chemical cleaning.This study was designed to examine the efficiency of CO


MLL3, also known as KMT2C, is a lysine mono-methyltransferase in charge of the writing of an epigenetic mark on lysine 4 from histone 3. The catalytic site of MLL3 is composed of four tyrosines, namely, Y44, Y69, Y128, and Y130. Tyrosine residues are highly conserved among lysine methyltransferases’ catalytic sites, although their complete function is still unclear. The exploration of how modifications on these residues from the enzymatic machinery impact the enzymatic activity of MLL3 could shed light transversally into the inner functioning of enzymes with similar characteristics. Through the use of QMMM calculations, we focus on the effect of the mutation of each tyrosine from the catalytic site on the enzymatic activity and the product specificity in the current study. While we found that the mutations of Y44 and Y128 by phenylalanine inactivated the enzyme, the mutation of Y128 by alanine reactivated the enzymatic activity of MLL3. Moreover, according to our models, the Y128A mutant was even found to be capable of di- and tri-methylate lysine 4 from histone 3, what would represent a gain of function mutation, and could be responsible for the development of diseases. Finally, we were able to establish the inactivation mechanism, which involved the use of Y130 as a water occlusion structure, whose conformation, once perturbed by its mutation or Y128 mutant, allows the access of water molecules that sequester the electron pair from lysine 4 avoiding its methylation process and, thus, increasing the barrier height. In recent decades, the histone methylation process has become a topic of increasing interest because of its implication in the gene-regulation process , as it iDespite the fact that the methylation process would only involve minor conformational changes in the main structure of the N-terminal domain, the amount of information codified through this mechanism is highly versatile in terms of its role in activating and silencing. In fact, it was proposed that the role of di-methylated H3K4 is related to determining a permissive chromatin environment, while the tri-methylated state corresponds to a fully active chromatin conformation ; whereasAmong the KMTs, the mixed-lineage-leukemia MLL family of enzymes are found in humans and bony vertebrates in general. In particular, MLL3 regulates the expression of a tumor-suppressor gene, whose malfunctioning has been related to several types of cancer disease. For example, MLL3 is a haploinsufficient 7q tumor suppressor in acute myeloid leukemia  and its In line with the background detailed above, we explore the role of the tetrad of tyrosines in the catalytic site of MLL3 by mutating these residues to determine their role in catalytic efficiency and predict their impact on the product specificity towards the methyl-transfer process. Our results shed light on the effect of such mutations on the generation of impaired or gain-of-function states of this enzyme. Considering that MLL3 is directly involved in the regulation mechanism of gene transcription, the findings related to this study allow us to set part of the molecular basis for the etiology of certain diseases, and consequently make this enzyme an attractive target for drug design in its different pathological states.≠) of the methyl-transfer process derived from the mutation of these four residues. While, Y44 and Y128 were mutated by phenylalanine and alanine, Y69 and Y130 were mutated only by phenylalanine. This was performed to unveil the influence of each residue on the energy barrier in the methyl-transfer process. Finally, we proceeded to study the effect of each mutation on product specificity.The present work focused on studying the role of tyrosine tetrad obtained from the catalytic site of M3RA, namely, Y44, Y128, Y69, and Y130. We aimed to study the changes in the barrier height (∆Ecat), which corresponded to an increase from 20 kcal/mol up to 25 kcal/mol.According to the crystal structure used in this study, the main role of these two residues corresponded to the proper anchoring of the H3K4 substrate lysine, where the oxygens from the hydroxyl groups of Y44 and Y128 pointed towards Nε(K4) obtained from the lysine. However, as described above, there were KMT enzymes capable of performing the methyl-transfer reaction with just one tyrosine residue in these positions, which raised the question of how necessary the presence of both Y44 and Y128 for the catalytic efficiency was, and which one, if any, was particularly essential for the process. To determine the role of the mutations of Y44 and Y128 in the catalytic efficiency, we systematically proceeded by mutating the two residues by phenylalanine and alanine to define the role of the hydroxyl group and the aromatic ring from the tyrosines in the anchoring process of the lysine substrate and its effect on the reaction process. In all cases, we proceeded by selecting twenty-five conformations obtained from the MD simulations of the respective mutant with deprotonated K4, assuming that the deprotonation process had already occurred. In this way, we focused our analysis on the effect of the mutation on the methylation process, the rate-limiting step. In any case, a detailed explanation of the arguments from which we assumed that the deprotonation process would also occur in the mutants was presented in ≠) of 26.4 ± 9.9, and 37.7 ± 13.5 kcal/mol for Y44F and Y128F, respectively, both higher than the 18.9 ± 5.3 kcal/mol of ∆E≠ calculated in our previous work for the native enzyme , labels depicted in To obtain a qualitative picture of the contribution and topology of the non-covalent interactions occurring inside the catalytic site, we calculated the non-covalent interaction index (NCI) ,57. ThisThe methylation of the N-terminal domain from histones represents a dynamic mechanism of epigenetic regulation due to its versatility in terms of the activation and inactivation of gene regulation together with the degree of activation depending on the product multiplicity. Herein, we explored through QM/MM calculations and molecular dynamics simulations the potential capacity of MLL3 to change its mono-methylation activity by modifying two key residues in charge of substrate anchoring, namely, Y44 and Y128. While our models showed that the mutation into phenylalanine of both residues led to inactive states of the enzyme, the mutation of Y128 to alanine (Y128A) resulted in an active enzyme for the mono-methylation of the substrate, whereas Y44A was inactive. However, the most remarkable result was that, according to our calculation, the Y128A mutant was actually able to di- and tri-methylate the substrate, which could cause diseases due to the increased range of products for this enzyme. Meanwhile, Y44A was only capable of di-methylating. According to this, we were able to suggest a proper mechanism for the inactivation and gain of function for this enzyme, in which Y130 showed a crucial role in the process. In this sense, we were able to expose how, when Y128 was mutated into phenylalanine, there was a conformational change of Y130 through steric effects, which opened the active site to water molecules and inactivated the lysine substrate by sequestering the electron pair of its nitrogen atom. However, the mutation into alanine eliminated that steric effect, allowing Y130 to restrict the access of water molecules and thus reactivate the enzyme. In addition, Y128A mutation provided access to the formation of a hydrophobic pocket capable of accommodating the methyl groups present in lysine during the di- and tri-methylation processes. Finally, it was possible to stablish the relevance of Y128 in the product specificity of the enzyme.


Designing bending elements made of fiber composites requires knowledge of the residual strengths. Residual strengths determined according to PN-EN 14651, regardless of the type of matrix and the fibers used, are characterized by a very-high coefficient of variation, about 30%. The variability of this feature is so large that the normal distribution adopted in statistical analyses, which is consistent for compressive strength or tensile strength, may, in the case of residual strengths, result in a significant overdesign of the elements. Therefore, the article proposes a novel method of determining the residual strength with the use of centrally bent square plates simply supported at the perimeter. The coefficient of variation of this characteristic in the case of plate testing is about 8%. Due to advantages such as general availability and relatively low production costs, concrete is one of the most commonly used building materials. Unfortunately, it is a brittle material, and its destruction occurs at low deformation values. Due to it slow tensile strength and susceptibility to crack propagation, concrete today, in some cases, is becoming a material with insufficient physical and mechanical properties.The brittle nature of concrete is a problem for structural designers because the ability of a structure to undergo high plastic deformation before failure can be critical. Therefore, research is constantly being carried out to find solutions to improve the properties of concrete. A way to improve the properties is to modify the concrete mix by adding fiber reinforcement. The very thorough and vast overview of different fibers was reported by Mohajerani et al. . They prComposites with the addition of fiber reinforcement have been known for a long time, and sometimes they are an alternative to ordinary concrete and characteristic of better properties. Compared to regular concrete, fiber-reinforced concrete shows higher resistance to dynamic loads ,3 and hiRegardless of the type of fibers or the matrix, in order for the design process to effectively use the ability of fiber-reinforced concrete to transfer load-destructive tension after post-cracking, it is necessary to understand the behavior of the post-cracking elements under tensile stress, which is defined by the residual strengths ,25,26,27Many methods have been developed to determine the fracture toughness of fiber-reinforced materials. These include the uniaxial tension test method proposed by RILEM (RILEM TC-162-TDF ), the weThe choice of the method to determine the flexural tensile strength of the fiber composite depends on the adopted calculation method based on specific properties. The most common method for analyzing the behavior of fiber-reinforced concrete after cracking under tensile conditions is the recommendations of RILEM TC 162-TDF , which aUnfortunately, based on the experience of the authors presented in the monograph  and the R.1 and fR.3 determined according to [For example, N. Buratti et al. , while trding to , whose crding to  researchrding to , in theirding to  found thLarge values of the coefficients of variation, which characterize the results of the research on the residual strengths of fine-aggregate fiber composite ,49 and hThe novel method of testing the residual strength proposed in the article using centrally bent square plates simply supported at the perimeter is aimed at more realistic modeling of biaxial bending than in the case of beams. The obtained residual strength values are characterized by a much lower coefficient of variation (about 8%), and thus this feature is determined with greater reliability. This is because the residual strengths are the basic and most important feature in the design of bending fiber-concrete sections.3 category based on the PN-EN 12620 standard [The test elements were made of fine aggregate cement composite with the addition of steel fibers. The fine-aggregate fiber composite is a novel construction material patented No. 239641, “Fine-aggregate mineral composite reinforced with scattered fibres”, Koszalin University, in which the aggregate used is post-production waste. In the analyzed composite, the aggregate was sand with granulation up to 4 mm, which is the waste of aggregate mines located in northern Poland (the Pomerania region) ,51. In tstandard .3), Portland cement CEM II/A-V 42.5R (420 kg/m3), silica dust (21 kg/m3), superplasticizer (16.8 kg/m3), and tap water (160 kg/m3).Fast-hardening Portland cement containing fly ash was used. Research has shown that fly ash has a positive effect on the properties of concrete mixtures [The elements for the tests were used as described above: sand . The aspect ratio of fibers was λ = l/d = 62.5 . The fibers in the mixture were distributed randomly. The fiber content was determined based on the results of previous studies of composites with a fiber content of 0–2.5% [w/c = 0.38. The consistency of the fiber composite mixture was determined by the Vebe method according to PN-EN 12350-3 [The fiber reinforcement consisted of steel fibers with a hooked shape in the amount of 1.2% in relation to the volume composite , it was stored under the conditions of 20 ± 2 °C and 50 ± 2%.The authors have been conducting research on the use of waste sands for the production offine-aggregate fiber composites of structural importance for many years. In the studies ,52,59,60t-distribution, with a tolerance of 10% and a significance level of 0.05.The assumed test program included testing the residual strength and the limit of proportionality of the fiber composite with the use of beam elements with dimensions of 150 × 150 × 700 mm and testing the energy absorption capacity of the fiber composite panels with dimensions of 100 × 600 × 600 mm. The necessary minimum number of samples to determine the statistical mean value of the examined feature was determined on the basis of statistical analysis using the Student’s The aim of the experimental research and theoretical analyses was to develop a method of testing the strength of residual fiber composites that would allow for the lowest possible variability of this property. As shown in R.1, fR.2, fR.3, and fR.4. Prior to testing, the samples were incised in the central part to a depth of 25 mm and a width of ca. 3 mm. The test elements were placed on articulated supports with 500 mm spacing. The beams were loaded in half of the span in the use of the scheme recommended by [The beam elements were tested according to PN-EN 14651 . The relended by   were determined for the respective CMOD, j, where j = 1, 2, 3, and 4. For the strengths fR.1, fR.2, fR.3, and fR.4, the mean tensile stress values in the cross-section for the given CMOD widths were 0.5, 1.5, 2.5, and 3.5 mm, respectively. The values of the residual strengths of the fine-grained fiber composite were determined according to the formula [sp is the distance between the notch tip and the top of the specimen; Fj is the load corresponding to CMODj; l is the span of the beam; and b is the width of the specimen.The beam load was applied in a continuous manner with a variable rate. The increase in the beam load speed was determined depending on the CMOD crack width. Due to the possibilities of the equipment, the test was performed by applying a load as a function of displacement, while the tested value was the width of the CMOD crack. According to , the mea formula :fR.j = , and the second by an increase in strength with an increase in the CMOD (post-crack hardening—pch). A general view of a sample on the fiber composite residual strength test stand is shown in The energy absorption capacity of composites with fibers is, in addition to the residual strength, an important feature determining their plasticity. This property is used in the design of fiber-reinforced concrete structures, in which, as a result of high loads, significant deformations could arise. The study of the energy absorption capacity of the fiber composite was carried out according to PN-EN 14488-5 . The samThe slab was loaded continuously so that its deflection increased by 1 ± 0.1 mm/min in a controlled manner. The test ended when the slab deflection in the middle of its span reached a value of 30 mm. The increases in the load, deflection, and displacement on the upper and lower surfaces of the plate were recorded using the SAD 256 data acquisition system. The accuracy of the inductive sensors used in the tests was 1 mV/V. A general view of the stand for testing the energy absorption capacity of a fiber composite is shown in R.3/fR.1 ratio). This means that the material has a high fR.1 value (range 1–8) and is characterized by the “post-crack softening” feature , in contrast to the indices obtained in the tests of other properties of this material (from ν = 6% for the compressive strength to ν = 8% in the splitting tensile strength test), show a sufficient quality of the fiber composite used. As stated in the introduction, the fiber composites’ residual strength determined on beams have a large variability of more than 30% . Such a p.exp) measured in the middle of the span of the slab (p.exp) shows the mean value of all the tests, as well as the minimum and maximum values that were recorded continuously during the test every 0.5 s using the SAD 256 data acquisition system.The tests of the bending plates with articulated support along the perimeter allowed for the making of a graph of the dependence of the loading force (F) and deflection (δthe slab . The grap.exp) in the middle of the plates span was compared with the equation proposed by Khaloo and Afshari [The relation between the loading force (F) and the plate deflection (δ Afshari  describi Afshari  is shownpexp) of the tested fiber composite was determined. The PN-EN 14488-5 standard [p) between the deflection values (δp), respectively: 0 and 25 mm. The results obtained for all plates are presented in Based on the obtained test results, the energy absorption capacity of the plates (Estandard  defines pexp) of the fine-aggregate fiber composite are presented in The statistical analysis parameters of the energy absorption capacity (Epexp) and the deflection (δp.exp) in the middle of the plate span is shown in The energy absorbed by the fiber composite for each deflection value was determined on the basis of the mean value from the load–deflection relation diagram see  as the ap.exp) each time.Such a presentation of the results allows for easy reading of the energy absorption capacity of the fiber composite (for a deflection of 25 mm) and the energy absorbed by the material for the selected deflection value, without the need to integrate the dependency graph , which took place on the bottom surface of the plates, the fibers did not break but were pulled out of the matrix .The PN-EN 14487-1  standardThe methodology of testing square plates according to  and the The conducted research prompted the authors to develop a procedure for determining the residual strength based on bending square plates .The plate tests allow for obtaining results with a much lower coefficient of variation than in the case of the beam test according to .R.j) determined for individual CMOD values with the beam deflection (δb.exp) and the energy absorbed by the beam during bending (Ebexp). The beam energy (Ebexp) for the respective CMOD values (b.exp) as the area under the F–δb.exp curve.This procedure was based on linking the residual strengths  and the CMOD width, given in PN-EN 14651 [The shapes and courses of the relation between the F and the δEN 14651 , reflectpexp) in relation to the deflection (bexp) for the CMOD values equal to 0.5, 1.5, 2.5, and 3.5 mm, respectively  after substituting into the formula  were obtained close to the experimental values, representing the CMOD widths of 0.5, 1.5, 2.5, and 3.5  with the energy absorbed by the plate (Epexp) known from the experiment.The relation shown in b), the mean values of the forces (Fj) were determined for the beam deflection (δb), representing the CMOD = 0.5, 1.5, 2.5, and 3.5 .Next the residual strengths of fiber composite fIn summary, in order to use the proposed proprietary procedure for calculating the residual strength, the flow diagram presented in R.j) determined according to our own calculation procedure are presented in The values of residual strength  are similar, it can be assumed that the test energy absorption capacity can also be used to determine the residual strength, characterized by a much lower coefficient of variation than in the case of beams.The shapes of the F–δ diagrams obtained in the authors’ own research indicate that after cracking, the fine-aggregate fiber composite had a slow decrease in the destructive force with increasing deflection. This was also noticed during the test of the residual strength on the beams. The slight decrease in the curve after obtaining the maximum value of the load is related to the number of fibers in the fiber composite.The essence of the author’s proposed method of determining residual strength for the fiber-reinforced cement composite consists of relating the energy absorbed by the beam when bending with a deflection corresponding to the width of the CMOD crack, for which the residual strengths are determined with the energy absorbed by bending panels at equivalent deflections. Then, on the basis of the determined absorption energy in the beam, the value of the load corresponding to the successive energies is determined, and as a last resort—the residual strength.The novel proposition for determining the residual strength with the use of centrally bending square plates simply supported at the perimeter presented in the article is an alternative to the 3-point bending of the beam using the method specified in PN-EN 14651. The proposed test method is aimed at a more realistic modeling of biaxial bending than in the case of beams. In this study, the obtained residual strengths are characterized by coefficients of variation not exceeding 8%, while according to PN-EN 1465, they reach 17%.The authors are aware, however, that the presented method of determining the residual strength, based on the energy absorption capacity of the fiber composite, is not perfect. Further experimental and analytical works are planned to improve the method, inter alia, by introducing appropriate factors to facilitate the described procedure. The great advantage of this experimental–analytical procedure is that the residual strengths defined in this way are characterized by a low coefficient of variation.