In the context of the evolving role of Physical Therapists (PTs) in health promotion, this study explored the incorporation of nutritional assessment and counseling into PTs’ professional practice in Israel. Using an anonymous cross-sectional survey design, the research gauged PTs’ professional background, nutritional knowledge, lifestyle habits, and the extent of nutritional care integration. Our survey gathered data from 409 certified PTs in Israel, revealing inadequate nutritional knowledge, commendable nutritional lifestyles, and limited nutritional care integration. Participants with over 13 years of clinical experience demonstrated significantly higher levels of nutritional assessment and counseling integration within their physical therapy practices. Workplace setting, nutritional lifestyle, and nutritional knowledge emerged as significant predictors for nutritional care integration. Specifically, working in outpatient clinics and possessing better nutritional lifestyles and knowledge were associated with the increased integration of nutritional assessment and counseling within physical therapy practice. These findings underscore the need for targeted interventions and formal nutrition education to bridge the knowledge gaps and optimize patient care. These results advocate for comprehensive nutrition education in physical therapy curricula and the fostering of PTs as role models. Integrating nutrition care could empower PTs to enhance patient outcomes and fulfill their role in preventive healthcare. The scope of the physical therapy profession has traditionally been perceived as not fully encompassing nutritional assessment and counseling in clinical practice. However, there is a growing recognition of a wellness model in which physical therapists (PTs), similar to other healthcare professionals, play an important role in promoting health, wellness, and preventive healthcare . This shThe process of physical therapy rehabilitation, aimed at restoring energy reserves, enhancing body tissues, and improving functionality in patients grappling with acute or chronic illnesses or injuries, necessitates meticulous nutritional considerations . FurtherThe concept of the personal scope of practice pertains to the spectrum of activities for which a given physical therapist is educated, trained, and proficient . PTs exhWhat professional characteristics within the background of PTs are linked to the inclusion of nutritional assessment and counseling in professional practice?Does a correlation exist between the nutritional knowledge of PTs and the inclusion of nutritional assessment and counseling in professional practice?Is there a relationship between the nutritional lifestyle of PTs and the inclusion of nutritional assessment and counseling in professional practice?Therefore, the objectives of this present study are to address the following questions:This study utilized an anonymous cross-sectional nationwide online survey.Participation was extended to certified PTs holding a bachelor’s degree, with at least one year of clinical experience and actively participating in clinical practice. However, certified PTs possessing an additional degree as registered dietitians were excluded from participation.Questionnaire Design:The research protocol followed these distinct steps:An anonymous survey in Hebrew was formulated by the authors of the study (RN and MEG). The design of the questionnaire was rooted in the “Knowledge and Attitudes of Nutrition, Nursing, Physical Therapy, and Exercise Physiology/Physical Activity & Fitness Majors Regarding Nutrition Questionnaire”, initially developed by Thomas et al. (2006) . Modific2.Expert Panel Review:The survey underwent rigorous assessment by an expert panel comprising 5 certified PTs, 3 registered dietitians, and 3 physical therapy students. Feedback encompassed the questionnaire’s length, clarity, content validity (assessed by dietitians), and alignment with the field of physical therapy. The viewpoints of both PTs and physical therapy students were considered, focusing on the level of question complexity and the pertinence within the context of professional practice.3.Questionnaire Distribution:The finalized survey was disseminated to PTs across Israel via “Qualtrics” software [February 2022] . The distribution spanned a four-month timeframe (20 March 2022–17 July 2022). Participant recruitment involved a convenience sampling strategy, using social media platforms, physiotherapy professional groups, the newsletter of the Israeli Physiotherapy Society, and word-of-mouth communication within the physical therapy community. The survey included a statement that completing and sending the questionnaire indicated informed consent. This research was approved by the Ethics Committee at the Faculty of Social and Health Sciences, at the University of Haifa (number 2858).Personal and professional information—Gathering participants’ data encompassing age, body mass index (BMI), prior nutrition self-counseling, academic background, place of physical therapy studies, workplace, professional experience, and pre-existing nutritional knowledge acquired.Nutrition knowledge test: Comprising 19 questions—1 segment encompassed 7 questions with 3 possible answers , and 12 questions with 5 possible answers .Evaluation of the nutritional lifestyle: Participants responded to 5 statements using a five-point Likert-type scale ranging from 1 (strongly agree) to 5 (strongly disagree) . Additionally, there were 4 questions with 5 possible answers .Evaluation of nutrition integration in the physical therapy practice: This section consisted of 9 statements, each offering three potential answers: “yes”, “no”, and “sometimes”. For example, “During interview and examination, I ensure that there was no involuntary weight loss exceeding 5%”.The anonymous survey in Hebrew comprised four sections:Descriptive statistics were calculated for all outcome measures. For the nutrition knowledge test, means and standard deviations of the correct answers (out of 19) and total scores (out of 100) were calculated for the 19 questions. A higher score indicated higher participant nutritional knowledge. For the evaluation of the nutrition lifestyle score, the overall mean score was calculated using the average of the eight questions. A lower average score suggested a higher level of the nutritional lifestyle of the participants. The evaluation of the nutrition integration in physical therapy practice score was assessed using a total mean score with a range of 9 to 27, where a lower average score indicated a stronger integration of nutrition within physical therapy practice.The internal consistency of the evaluation of the nutrition lifestyle scale and the evaluation of the nutrition integration scale were determined through Cronbach’s alpha. Alpha values of  ≥0.9 were considered excellent, 0.7  ≤  α  <  0.9 were considered good, 0.6  ≤  α  <  0.7 were acceptable, 0.5  ≤  α  <  0.6 were poor, and α values < 0.5 were considered unacceptable .A bivariate correlation coefficient test was utilized to determine the relationship between the evaluation of the nutrition integration in physical therapy practice mean score variables including age, BMI, nutrition knowledge test mean score, and the evaluation of the nutrition lifestyle mean score.Differences in means across workplace and years of clinical experience were evaluated using one-way Analysis of Variance (ANOVA) for the total evaluation of the nutrition integration in physical therapy practice mean score. p-values ≤ 0.05.A multiple linear regression analysis was conducted. Multiple linear regression analysis was used for predicting nutritional assessment and counseling integration within the physical therapy framework. Variables included professional experience, workplace, age, nutrition knowledge test mean score, and nutrition lifestyle mean score. Statistical significance was set at The study encompassed a total of 426 respondents. Among these, 4 respondents were excluded from the analysis due to their additional qualifications as registered dietitians, while 13 respondents, whose primary workplace was in academia without clinical practice engagement, were also excluded. Consequently, the study included a final participant count of 409.Detailed scores for the nutrition knowledge assessment consisting of 19 questions are presented in The nutritional lifestyle scale demonstrated acceptable internal consistency . Most participants rated their lifestyle at a moderate level (2.9 ± 0.9 out of 5) and maintained a healthy body weight (2.1 ± 0.9 out of 5). Additionally, they reported infrequent consumption of fast food (1.8 ± 1.0 out of 5) and a preference for non-processed foods (1.9 ± 0.9 out of 5). However, in contrast to the Ministry of Health’s 2020 position paper, a significant number of PTs consumed animal protein in the form of meat or chicken more than three times a week (3.1 ± 1.4 out of 5).The nutrition integration in physical therapy practice scale exhibited acceptable internal consistency . With lower scores indicating higher integration, the mean total score was 20.3 ± 3.2 out of 27, indicating a relatively low integration level of nutrition within PT’s clinical practices. Further details on the individual items are provided in p < 0.05) and those working in outpatient clinics  = 8.6, p < 0.001) demonstrated significantly higher levels of nutritional assessment and counseling integration within their physical therapy practices. A comparison of the different clinical experience and workplace groups is detailed in p < 0.001). A significant negative moderate correlation was observed between the mean nutrition integration score and participants’ mean nutrition knowledge score . A negative low correlation was observed between the mean nutrition integration score and age , while no significant correlation was detected with BMI. For further details see Bivariate correlation coefficient analysis revealed a significant positive low correlation between the mean nutrition integration in physical therapy scores and participants’ mean nutritional lifestyle scores  = 8.7, p < 0.001). Specifically, working in outpatient clinics and possessing better nutritional lifestyles and knowledge were associated with the increased integration of nutritional assessment and counseling within physical therapy practice.The results of the multiple regression analysis are presented in The present study delves into three fundamental aspects within a domain that has not been deeply examined in prior research: nutritional knowledge, nutritional lifestyle, and the degree of integration of nutritional assessment and counseling within the area of physical therapy practice, with a specific focus on Israeli PTs. The findings revealed that registered PTs in Israel exhibit inadequate nutritional knowledge, commendable nutritional lifestyles, and a low level of integration of nutritional assessment and counseling within their physical therapy practice. In addition, a noteworthy association was demonstrated between enhanced integration and longer clinical experience, along with employment in outpatient clinics.The study findings revealed considerable knowledge gaps concerning the Ministry of Health’s position paper. Out of eight questions assessing participants knowledge of and familiarity with the 2020 position paper, respondents answered six questions incorrectly. Notably, 88% of respondents offered inaccurate answers concerning the revised food categorizations presented in Israel’s Ministry of Health’s updated dietary guidelines. The current study identified a notable gap in the ability of PTs to recognize and identify crucial health risk factors. A considerable percentage of participants had difficulty correctly identifying markers such as glucose and glycated hemoglobin levels for diabetes diagnosis (60% error rate) and cholesterol levels indicating dyslipidemia (70% error rate), and knowledge gaps were found among PTs in malnutrition detection, with 87% lacking awareness of serum albumin’s importance and 70% being unfamiliar with weight loss percentages as indicators of decline. However, the study’s outcomes emphasize that PTs exhibit a satisfactory understanding of the functions of vitamins and minerals, along with their pivotal role in maintaining proper human body function. The literature underscores PTs’ crucial role in detecting malnutrition risks, especially for those seeking physical therapy ,22,23. UA substantial knowledge of nutrition has been substantiated to positively impact the self-efficacy of PTs, leading to enhanced proficiency in dispensing nutritional guidance and managing weight-related concerns . The stuThe current findings underscore the necessity for comprehensive nutrition education within entry-level physical therapy programs, a prerequisite already established by educational governing bodies  . This skThe concept of role modeling holds a significant place in shaping healthy behaviors among PTs, as supported by prior research. Studies such as Black et al. (2012) have shown that a substantial number of PTs actively engage in healthy practices, such as regular physical activity, adhering to balanced diets, refraining from smoking, and maintaining healthy weights . This reA low level of integration of nutritional assessment and counseling within the area of physical therapy practice was found also in previous studies ,4,13,27.Our study demonstrated that employment in outpatient clinics emerged as a significant predictor of the heightened integration of nutritional assessment and counseling in clinical practice. Previous studies have documented an array of obstacles that confront PTs in their discussions about nutrition with patients. Among these challenges, time constraints emerge as a recurrent issue, particularly pronounced in dynamic clinical settings characterized by high levels of activity and demand ,12,30. AClinical experience, particularly when 13 years or more, emerged as a notably influential factor, demonstrating higher levels of nutritional assessment and counseling integration within PTs’ physical therapy practice. This observation stands in contrast to the study by John et al. (2020), wherein the significance of years of clinical experience in incorporating nutrition into physical therapy practice was not ascertained, despite a notable proportion of participants in their study having 15 years or more of clinical experience (56.7%) . One posOur study evaluated the integration of nutrition care in physical therapy practices to pinpoint areas needing improvement. Unlike prior research, we quantified the implementation of nutritional diagnosis and counseling, assigning each participant an average score based on recent PT recommendations ,15,16,31The role of nutrition in the practice of physical therapy should be considered within the framework of each country’s practice act and regulations . For exaAddressing these gaps necessitates formal education to empower PTs to confidently integrate nutrition care into practice ,13,28,33This study was accompanied by several limitations warranting consideration. The utilization of a cross-sectional design within this investigation hinders the establishment of causal relationships among the variables under scrutiny. While this approach permits the observation of associations, it falls short in ascertaining the directionality of these relationships. Furthermore, the reliance upon self-report measures, although enriching in terms of the information garnered, introduces potential biases such as recall bias and social desirability bias. In conclusion, this study, while navigating several limitations, sets the stage for future research endeavors to address these constraints.PTs in Israel displayed inadequate nutritional knowledge and a limited incorporation of nutritional assessment and counseling within their clinical practice. Yet, PTs demonstrated commendable nutritional lifestyles. Greater clinical experience demonstrated significantly higher levels of nutritional integration. Workplace setting, nutritional lifestyle, and nutritional knowledge emerged as significant predictors for nutritional care integration. Our findings also point to discrepancies, suggesting a need for targeted interventions and formal education to enable effective nutrition care integration. Future research could explore nutrition education in physical therapy curricula and assess the impact of specific nutritional interventions and evaluate the utility of nutritional screening tools and standardized questionnaires in optimizing patient care within physical therapy rehabilitation.


Mesenchymal stem cells (MSCs) play a crucial role in tissue engineering, as their differentiation status directly affects the quality of the final cultured tissue, which is critical to the success of transplantation therapy. Furthermore, the precise control of MSC differentiation is essential for stem cell therapy in clinical settings, as low-purity stem cells can lead to tumorigenic problems. Therefore, to address the heterogeneity of MSCs during their differentiation into adipogenic or osteogenic lineages, numerous label-free microscopic images were acquired using fluorescence lifetime imaging microscopy (FLIM) and stimulated Raman scattering (SRS), and an automated evaluation model for the differentiation status of MSCs was built based on the K-means machine learning algorithm. The model is capable of highly sensitive analysis of individual cell differentiation status, so it has great potential for stem cell differentiation research. Fteratoma , and theteratoma . Additioteratoma ,14. CurrThe free and protein-bound states of the intracellular metabolic cofactor nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) are closely related to cellular metabolism. Change in NADH at different levels of cell differentiation has been observed in various reports ,16,17,18Spontaneous Raman spectroscopy is a label-free analysis method that can provide information on specific molecules within cells and reveal molecular changes at the single-cell level. It has been applied to detect MSC differentiation. Lazarević et al.  used spoBoth FLIM and SRS can provide label-free microscopic images for cells. Due to the strong heterogeneity among cells during differentiation, statistical analysis of a large number of cells FLIM or SRS images can only show the average trend of metabolic changes or cell contents changes and cannot accurately reflect the differentiation state of individual cells. To investigate the individual cells, image segmentation and classification can be achieved using machine learning.Machine learning methods have been widely applied to investigate the cellular morphology of MSCs ,31. MotaThis article presents a new approach for evaluating the differentiation of MSCs into adipogenic and osteogenic lineages. FLIM and SRS data were collected to characterize the energy and substance metabolism of MSCs induced to differentiate into these two lineages. Single cells were obtained from high-density cell populations using a sliding window segmentation method, and the extracted feature data were input into K-means clustering. By implementing this unsupervised machine learning approach, the differentiation states of MSCs into adipogenic and osteogenic lineages were distinguished at the single-cell level in a label-free, non-invasive, and high-throughput manner, even under highly heterogeneous conditions. These results provide new insights for the clinical research of MSCs.The MSC culture medium was prepared by mixing 44.5 mL basal medium  with 5 mL premium fetal bovine serum  and 0.5 mL penicillin–streptomycin solution .The adipogenic induction medium A contained 10% premium fetal bovine serum, 1 mM dexamethasone, 0.5 mM isobutylmethylxanthine , 50 μM indomethacin , 10 μg/mL insulin , and 1% penicillin–streptomycin solution. The adipogenic induction medium B contained 10% premium fetal bovine serum, 10 μg/mL insulin, and 1% penicillin–streptomycin solution.The osteogenic induction medium contained 10% premium fetal bovine serum, 100 nM dexamethasone , 10 mM β-glycerol phosphate , 50 μM ascorbic acid , and 1% penicillin–streptomycin solution.All the prepared media was wrapped in aluminum foil and stored at 4°C for later use.MSCs at freezing passage 0 (P0) were obtained from the Cell Bank of the Chinese Academy of Sciences . The cryovial was removed from the liquid nitrogen freezer and immediately immersed in a 37 °C water bath with gentle agitation for 3–5 min until thawed. After centrifugation at 1000 rpm for 5 min, the supernatant was replaced with 1 mL preheated MSC basal medium at 37 °C. The cell suspension was transferred to a 6 cm culture dish  and mixed well with 2–3 mL basal medium. MSCs were cultured at 37 °C and passaged to multiple dishes when the confluency of cell reached 80–90%. When the confluency reached 80–90% again, the cells were passaged at a density of 20,000 cells/dish to several precoated confocal culture dishes  (P2). Because stem cells cannot attach to a glass surface very well, the confocal culture dishes were pretreated with human plasma fibronectin solution  before using. FN solution was added to cover the bottom of the confocal culture dish and incubated at 37 °C for 20 min. After incubation, FN solution was removed, and the dish was washed three times using PBS  to remove residual FN solution. Subsequently, 1 mL basal medium was added into the confocal culture dish and multiple dishes of cells were placed in a 37 °C incubator for cultivation.When the confluency of MSC reached 50%, osteogenic and adipogenic differentiation were induced separately in multiple dishes of MSCs. For the adipogenic group, the medium was changed to induction A medium for three days, followed by induction B medium for one day. This alternation pattern between A medium and B medium was repeated until Day 28. For the osteogenic group, the induction medium was replaced every three days until Day 28.The cells were fixed after FLIM observation to preserve their original state. After the medium was removed from the culture dish, the dish was gently rinsed with 1 mL of PBS solution three times, 1 mL of 4% paraformaldehyde  solution at room temperature was added for 20 min, the paraformaldehyde solution was removed, and the dish was rinsed three times with PBS.FLIM data of MSCs were acquired using a microscope equipped with a 60× oil immersion objective (N.A. = 1.40). The autofluorescence of NAD(P)H was excited using a femtosecond pulse laser  at a wavelength of 740 nm with a power of approximately 5 mW. The emission filter was set to 417–477 nm to ensure that the autofluorescence signal mainly originated from NAD(P)H. FLIM data were collected on Day 1, Day 7, Day 14, Day 21, and Day 28 after induction of MSCs.1 and τ2 are the fluorescence lifetimes of free and protein-bound NAD(P)H, respectively, and a1 and a2 are the percentages of free and protein-bound NAD(P)H, respectively. Then the data were processed in batch using ImageJ software to obtain the FLIM images of a2 in the cells.FLIM data was fitted by a double-exponential function using LAS X Single Molecule Detection software . The average fluorescence lifetime was calculated as −1.MSCs were fixed on Day 1, Day 7, Day 14, Day 21, and Day 28 after adipogenic induction for SRS imaging. The SRS imaging setup was reported previously . The StoAfter FLIM observation of osteogenic induction cells at different time points, the cells were fixed and stained for quantitative analysis of osteogenic differentiation. The staining method involved removing the PBS in the culture dishes, adding 1 mL ALP staining solution , incubating at room temperature for 90 min, removing the staining solution and rinsing three times with PBS. Bright-field imaging was performed using a microscope equipped with a 20× air objective (N.A. = 0.4) and a color CMOS camera  with an exposure time of 20 ms.The statistical results of NAD(P)H and lipid levels in cells showed the changes in cells from undifferentiated to differentiated . To studDue to the significant heterogeneity of MSCs during differentiation, the metabolic state and cell content distribution in each cell under the same conditions are not identical, making it impossible to assign a clear label to each cell. Therefore, an unsupervised machine learning method was used for cluster analysis. The K-means clustering algorithm, as an unsupervised machine learning algorithm, is widely used in various unsupervised clustering applications due to its simplicity, good clustering results, and strong interpretability. However, the choice of initial centroids in the K-means clustering process greatly affects the final clustering result. If the initial centroids are selected randomly, it may result in slow convergence and local optima . TherefoThe feature extraction and clustering processes were implemented using Spyder (Python 3.9) software. The feature extraction and K-means++ clustering process is shown in 2 distribution histogram, were obtained by plotting the distribution histogram and reading the values from FLIM data. In the lipid content analysis, the average grayscale value was obtained by dividing the total grayscale value of the cellular region by the total number of pixels counted in SRS images.Morphological features such as area, perimeter, and circularity were obtained using Python’s built-in functions from FLIM or SRS images. NAD(P)H lifetime features, including the peak value and full width at half maximum of the NAD(P)H aThe extracted features were normalized before determining the number of clusters. The number of clusters is determined by the elbow rule. More details on the principle and a more in-depth flowchart for determining the true number of clusters are provided in 2 distribution histogram as input features. Considering that NADH is a more effective feature for reflecting metabolic changes than cell morphology, morphological features such as perimeter and circularity of cells were added for clustering, but no significant improvement was observed in the results, and it increased the running time of program. K-means++ clustering of SRS data during adipogenic differentiation process used area, perimeter, circularity, and average grayscale value of cells as input features.K-means++ clustering of FLIM data during adipogenic and osteogenic differentiation used area of cells, peak value, and full width at half maximum of NAD(P)H aA Z-test statistical analysis method suitable for large samples was selected because of the large sample size, and all statistical analysis was performed using Excel 2019.2 images (the percentage of the protein-bound NAD(P)H) of MSCs in adipogenic differentiation from Day 1 to Day 28 are shown in 2 below 30%, and green represents a2 above 30%. Approximately 150–200 cells were involved in the averaging for each condition. The statistical averaged peaks of the a2 distribution curve from Day 1 to Day 28 are shown in Typical FLIM a2 values, and half width at half maximum of the a2 curves from the FLIM data as features, as shown in Considering that using a single feature for cluster analysis leads to issues such as model instability and sensitivity to outliers , multidiThe more refined clustering results are shown in Typical SRS images of lipids in MSCs in adipogenic differentiation from Day 1 to Day 28 are shown in Similar to the K-means++ clustering of FLIM images, multidimensional features were used as the input for K-means++ clustering of SRS images. The feature inputs included cell area, perimeter, circularity, and mean gray value in the SRS data. The optimal number of clusters was three, as shown in The distribution patterns in Since the unsupervised K-means++ clustering method using multiple features as the input was found reliable for determining the differentiation status of individual cells during adipogenic induction, it was tested for its ability to discriminate the differentiation status of individual cells during osteogenic induction. During the process of osteogenic differentiation of MSCs, hydroxyapatite (HA) gradually accumulates in the extracellular matrix. HA has a strong SRS signal, but this signal cannot accurately characterize the differentiation state of individual cells. Therefore, only FLIM data analysis was conducted, and the results were validated by ALP staining.2 images of MSCs induced to undergo osteogenic differentiation from Day 1 to Day 28 are shown in 2 distribution curve from Day 1 to Day 28 are shown in Typical FLIM aThe detailed clustering results are shown in To verify whether the K-means++ clustering results were consistent with the actual differentiation statuses, the differentiation statuses of cells were evaluated using chemical staining. ALP is a highly active enzyme during osteogenic differentiation and is commonly used to characterize osteogenic differentiation. Therefore, ALP staining solution was used to stain induced cells from Day 1 to Day 28. The staining results and the statistics of differentiated cells are shown in As shown in The induction media for osteogenic and adipogenic differentiation of MSCs have been widely utilized in the process of MSCs differentiation ,45,46. ADuring the differentiation process of MSCs, complex metabolic changes and cell content changes occur within individual cells. During the induction of adipogenic differentiation, the metabolic changes are reflected in the shift from glycolysis to oxidative phosphorylation, while the cell content changes are reflected in the gradual accumulation of lipid droplets within the cells. During the induction of osteogenic differentiation, the energy metabolic changes are similar to adipogenic cells, with a shift from glycolysis to oxidative phosphorylation . However2 data are closely related to the metabolic state of the cell [2 in MSCs from red to green. Since metabolic changes during cell differentiation are highly complex and heterogeneous, with vastly different metabolic states among different cells under the same conditions, using statistical averaging methods to determine differentiation status is not suitable for characterizing the cellular state and is inadequate for analyzing large amounts of highly heterogeneous cell data; for example, 2 had no significant differences. Manual analysis is time-consuming and makes it difficult to ensure accuracy, whereas using an unsupervised machine learning method to perform cell classification analysis enables analysis of individual cells and ensures robust data. In this article, the K-means++ algorithm, which is simple to use and produces good clustering results, was employed to perform cluster analysis on the FLIM data obtained from approximately 1000 cells. From the results in The changes in intracellular FLIM athe cell . TherefoThe clustering results based on three features of the osteogenic differentiation process are shown in These results indicated that different MSCs induced under different conditions have little heterogeneity in their differentiation status and exhibit similar proportions of each cluster at different time points. By combining machine learning analysis methods, high-throughput, label-free classification of osteogenic MSCs at the single-cell level was achieved.The heterogeneity of differentiation states in MSCs during adipogenic and osteogenic differentiation was investigated using an analysis approach combining FLIM or SRS with the unsupervised K-means++ clustering algorithm. The classification of undifferentiated, differentiating, and differentiated MSCs into adipogenic and osteogenic lineages at the single-cell level was achieved noninvasively and efficiently without labeling. The proposed analysis approach provides a convenient tool for metabolic studies of cell differentiation processes and enables the recycling and reuse of differentiated cells. Furthermore, this approach offers a rapid and nondestructive detection method for the application of stem cells in tissue engineering and clinical therapy, which may promote further development of stem cell applications in these fields.


Tactile attention tasks are used in the diagnosis and treatment of neurological and sensory processing disorders, while somatosensory event-related potentials (ERP) measured by electroencephalography (EEG) are used as neural correlates of attention processes. Brain-computer interface (BCI) technology provides an opportunity for the training of mental task execution via providing online feedback based on ERP measures. Our recent work introduced a novel electrotactile BCI for sensory training, based on somatosensory ERP; however, no previous studies have addressed specific somatosensory ERP morphological features as measures of sustained endogenous spatial tactile attention in the context of BCI control. Here we show the morphology of somatosensory ERP responses induced by a novel task introduced within our electrotactile BCI platform i.e., the sustained endogenous spatial electrotactile attention task. By applying pulsed electrical stimuli to the two proximal stimulation hotspots at the user’s forearm, stimulating sequentially the mixed branches of radial and median nerves with equal probability of stimuli occurrence, we successfully recorded somatosensory ERPs for both stimulation locations, in the attended and unattended conditions. Waveforms of somatosensory ERP responses for both mixed nerve branches showed similar morphology in line with previous reports on somatosensory ERP components obtained by stimulation of exclusively sensory nerves. Moreover, we found statistically significant increases in ERP amplitude on several components, at both stimulation hotspots, while sustained endogenous spatial electrotactile attention task is performed. Our results revealed the existence of general ERP windows of interest and signal features that can be used to detect sustained endogenous tactile attention and classify between spatial attention locations in 11 healthy subjects. The current results show that features of N140, P3a and P3b somatosensory ERP components are the most prominent global markers of sustained spatial electrotactile attention, over all subjects, within our novel electrotactile BCI task/paradigm, and this work proposes the features of those components as markers of sustained endogenous spatial tactile attention in online BCI control. Immediate implications of this work are the possible improvement of online BCI control within our novel electrotactile BCI system, while these finding can be used for other tactile BCI applications in the diagnosis and treatment of neurological disorders by employing mixed nerve somatosensory ERPs and sustained endogenous electrotactile attention task as control paradigms.  Selective attention allows the extraction of behaviorally relevant information while ignoring distracting stimuli that compete for the central nervous system resources. Exogenous attention is typically induced by presenting an abrupt stimulus that directs attention without intention, while endogenous attention can be voluntarily directed to a specific event or spatial location to select the most relevant information for current goals .A tactile attention task is a cognitive task that involves focusing attention on tactile sensations. The participant is typically presented with a series of tactile stimuli, such as vibrations, pressure, or electric pulses and is instructed to respond to specific features of the stimuli, such as their intensity, duration, or location on the body, while the effects of attention are measured by comparing responses to sensory stimuli, when they are attended vs. unattended . MoreoveSuch tactile attention tasks are often used in research to study the neural mechanisms underlying tactile perception and attention, as well as in clinical settings to assess sensory processing disorders and other neurological conditions . TactileIn addition to the assessment of sensory processing, tactile attention tasks can be used for training and improvement of sensory functions, though perceptual learning mechanisms, referring to the improvement in sensory processing and perception because of experience and practice . TactileSomatosensory event-related potentials (sERP) measured with EEG during selective attention tasks are a useful tool for exploring the spatio-temporal dynamics of neural processes involved in tactile attention . MoreoveIn the context of tactile attention task training, BCI can detect changes in ERPs associated with user’s tactile attention, and provide online feedback on their performance, , which may be a useful tool for validating the immediate effects of task executions and improving the training effectiveness through the user’s online modulation of task execution, towards generating desired electrophysiological output. These real-time modulations of task execution are enabled by closing the loop via online feedback provided by the BCI device .We have previously designed and presented an electrotactile BCI system that enables online detection of tactile attention, directed towards one of the two proximal stimulation locations on the user’s forearm . HoweverIn this paper we present an analysis of sERP responses induced within a novel paradigm for endogenous spatial tactile attention training, particularly designed for assessing the immediate effects of the sustained spatial tactile attention task. The aim of this work is to present the basic morphology of electrically induced somatosensory ERPs derived from EEG channels placed over the sensorimotor cortex and to explore the effect of endogenous spatial tactile attention on sERP morphological features.We have not identified other previous studies that explore the morphology of somatosensory ERPs induced by electrotactile stimuli , delivered to the mixed branches of the median and radial nerves of the forearm, especially in the context of the prospective BCI control for somatosensory training. Therefore, this study aims to contribute to the existing knowledge on the neural correlates of endogenous spatial tactile attention, but also present evidence about basic electrophysiological signal features that could be further utilized to improve sERP based electrotactile BCI control.This is also the first study to present the changes in sERP induced by sustained spatial tactile attention to one of two stimulation hotspots with equal (50%) probability of stimulus occurrence. Therefore, our study aimed to isolate and present the somatosensory mismatch response (within the sERP waveform morphology) which corresponds predominantly to the endogenous spatial tactile attention, while it is independent of target/distractor probability, unlike in a classical oddball paradigm with rare and frequent stimuli , most often used in ERP-based BCIs .This study was performed on 15 healthy  subjects without history of neurological disorders and with normal or corrected to normal vision. Volunteers were recruited among the students and employees of the University of Belgrade. The subjects had no previous experience with EEG recordings and were completely naive to the spatial tactile attention task they executed during tests. All the participants were informed about study procedures and signed a written informed consent form before participation. The study was approved by the local ethic committee and is in accordance with ethical guidelines defined by the Declaration of Helsinki.The EEG data was recorded using the g.USBamp amplifier . Six active electrodes  were positioned at standard 10–20 locations: C3, Cz, C4, CP3, P3 and Fp1. The ground electrode was placed at the AFz location, and the reference electrode was placed on the left earlobe. The Fp1 channel was used to detect ocular artifacts i.e., blinks, since eye movements, and were controlled during the tests. The sampling rate was set to 1200 Hz. The amplifier was configured to use embedded Notch filtering with a cut-off frequency of 50 Hz. Electrical stimulation was delivered by an eight-channel current-controlled electrical stimulator, MOTIMOVE . This stimulator is fully programmable with independent control of stimulation parameters  for each channel, by sending commands from the PC via USB port in real time. The stimulator is battery powered and fully isolated from the main power supply. For this study, two stimulation channels were used, comprised of two active electrodes of 1 cm diameter placed on the stimulated locations and a common indifferent electrode of 2.5 cm diameter.The subjects were seated in a chair in front of a computer monitor at approximately 1 m. Preparation for the tests involved setup of the EEG and electrical stimulation. For signal acquisition and stimulation control, a graphical user interface was developed in a MATLAB programming environment , through which the experimenter inspected the EEG quality and controlled the electrical stimulation settings. Two active stimulation electrodes were placed on the dorsal surface (D stimulation location) and volar surface (V stimulation location) of the right forearm, over the extensor carpi radialis longus muscle, and flexor carpi radialis muscle, respectively. A common indifferent electrode was placed on the volar aspect of the right wrist. Electrical stimulation setup and fine-tuning was already described in detail in our previous publication . Motor threshold was obtained for both electrical stimulation channels separately by increasing the pulse amplitude starting from 5 mA with 1 mA steps. Stimulation was increased over the motor threshold to inspect the muscle activation associated with each stimulation location. If the stimulation didn’t selectively activate the target muscles, stimulation electrodes were manually repositioned until selective activation of the flexor carpi radialis and extensor carpi radialis longus was successfully obtained. When the expected muscle twitch responses were observed at each location, the stimulation amplitude was reduced by 1 mA below the motor threshold. Next, the subjective stimulation sensations at two stimulation hotspots were balanced by decreasing the stimulation amplitude at the channel which induced the stronger sensory response reported by the subject. The goal was to achieve the most similar subjective sensation produced by electrical stimulation at both sites. The experiment consisted of 30 blocks. In each block, 60 stimuli were delivered sequentially in a randomized order to locations D and V with a 750 ms interstimulus interval. The subjects were instructed to perform a sustained spatial tactile attention task by silently counting the stimuli delivered to only one target location. The target location (D or V) alternately switched between the blocks, while the staring target location was randomized over subjects. In short pauses between the blocks the subject reported the counted number of trials and verified the attended location. Then the experimenter announced the next location to attend, instructed the subject to fix the gaze to a fixation cross on the screen, limit all movements, and initiated the next block. Attending stimuli delivered to D location, while D location was being stimulated: ADSD—attending D, stimulated D,Attending stimuli delivered to D location, while V location was being stimulated: ADSV—attending D, stimulated V,Attending stimuli delivered to V location, while V location was being stimulated: AVSV—attending V, stimulated V, andAttending stimuli delivered to V location, while D location was being stimulated: AVSD—attending V, stimulated D.The experiment comprised of two different attention tasks: sustained attention, i.e., counting the stimuli delivered at the dorsal location (AD), and sustained attention at the volar location (AV). However, during each attention task the stimuli were delivered to both locations which results in four possible of sERP waveforms responses (conditions):µV on filtered Fp1 channels for identifying blink artifacts, and the threshold of 50 µV on an absolute value on all EEG channels. Noise-free epochs were divided into four sERP responses associated with combinations of stimulus location and attention focus . Finally, mean sERP responses associated with each condition were extracted by averaging all noise-free single trials per condition. The total number of trials per experimental condition per subject was 450, while the average number of rejected trials was 39.17 ± 34.9 per individual subject. For further statistical comparison of sERP peaks, latencies, and areas under the sERP voltage curves, between conditions, we applied the following methods [µV) for each subject and experimental condition, dependent on the sERP component. Latency in milliseconds (ms) of each component (maximum or minimum) was calculated also. Area under the rectified sERP voltage curve .p < 0.05) in extracted values, for each channel, over all subjects, between conditions associated with the same stimulation location.We conducted analysis of sERP amplitude peaks, peak latencies, and areas under the voltage curves between associated conditions. Peaks, latencies, and areas were extracted from several time windows of the poststimulus interval, while the window selection was explained in more detail in the following sections. For the sERP peak, latency, and area under the voltage curve analysis, we extracted the maximum amplitude, minimum amplitude, the latency of each peak, and area in predetermined subintervals (windows) of the poststimulus sERP waveforms. Wilcoxon signed rank test was used to determine significant difference (p < 0.05) between sERP waveform amplitudes for each time point  of each EEG channel, over all subjects.We also conducted a deeper exploratory analysis of the sERP amplitude at different time instants of the poststimulus interval . The goaAdditionally, to eliminate errors due to multiple comparisons while maintaining a relevant statistical output, in line with electrophysiological expectations , we haveData from four subjects was rejected from analysis due to the following reasons. One subject’s data was contaminated with electrical stimulation artifacts which contributed to a high number of noisy epochs while three subjects were unsuccessful in the sustained attention task. These subjects had a high incidence in incorrect reported counts (in multiple blocks) and had problems in sustaining attention at the instructed location which was reported between the blocks. Namely, subjects reported attending to the different location then requested, incorrect counts due to counting stimuli at both locations or failing to count stimuli at target locations. The present results are extracted from 11 remaining subjects .Positive peak marked as P100, at latencies ranging from 75 to 135 ms over different EEG channels,Negative peak marked as N140, at latencies ranging from 115 to 165 ms over different EEG channels,First positive local maximum within a larger window between 200 and 400 ms, termed P3a, andSecond positive local maximum within a larger window between 200 and 400 ms, termed P3b.Based on the grand average sERP waveform morphology B, over ap values) are given in We have conducted a peak, latency search, and area under the curve calculation, for sERP of each subject within “search windows” presented in ms duration or longer) to filter out statistical artifacts due to multiple comparisons, in line with the expected electrophysiological signal properties. Bold p values represent the statistically significant differences (p < 0.05). For the purpose of a deeper explorative analysis, comparisons between experimental conditions associated with the same stimulation hotspot, at all electrode sites, were conducted by using sERP amplitudes of consecutive time instants. ms (P3) and 245 ms (C4) and ends between 288 ms  and 304 ms (P3). The second interval for V stimulation starts between 381 ms (P3) and 388 ms (C4) and ends between 418 ms (C3) and 423 ms (C4). The obtained intervals of statistical significance between the attended and unattended conditions for the V stimulation location show two distinct intervals of significant differences, which are present in all channels and are temporally aligned. The first interval for V stimulation starts between 230 ms–307 ms) is present while for the P3 channel, three intervals  were found.Regarding intervals of statistical significance between the attended and unattended conditions for D stimulation location, statistically significant differences were found only for CP3 and P3 channels. For the CP3 channel, a single interval (285 (1)The stimulation of the mixed nerve branches instead of exclusively sensory nerve branches commonly used in basic ERP studies exploring tactile attention.(2)The electrotactile attention task involving two stimulation hotspots with equal (50%) probability of stimulus occurrence, unlike commonly used oddball paradigm with rare and frequent stimulus.This work introduced several methodological novelties in obtaining sERP responses. For this reason, the discussion of the obtained results and sERP components needs to address previous studies most comparable to our protocol design. Novelties introduced by our study design are: Therefore, the obtained sERP morphology in our study is dependent on both novelties (the novel stimulation hotspot and the novel attention task paradigm). For this reason, in the next sections we discuss the obtained sERP responses for each stimulation hotspot independent of the attention task, to address the sERP morphology induced by the novel mixed nerve stimulation paradigm. Moreover, we discuss the effects of the sustained endogenous spatial attention task on the sERP features. Finally, we discuss the immediate implications of these findings for the design of electrotactile BCIs generally, and more specifically, the implications of the current results for further improvements in the control of our recently presented novel electrotactile BCI prototype . Studies of the brain mechanisms concerned with stimulus evaluation must differentiate between two classes of brain responses to any evoking sensory stimulus: (1) exogenous or obligatory responses which reflect the physiological registration of the sensory message in afferent pathways and cortical receiving areas, and (2) endogenous, cognitive components which reflect the activation of additional brain circuits in conjunction with the behavioral use of the information supplied by the sensory input .ms after the stimulus which were affected by the attention task  [In previous studies, somatosensory ERP responses elicited within the tactile attention task to electrical stimuli of exclusively sensory nerve branches of the fingers showed early (exogenous) components  ,27,28 wh0, P300) ,26,29. Ims, either because of subject specific responses to mixed nerve stimulation, or the contamination of early components by the stimulation artifact. However, the attention task explored in our work is expected to influence only the detected endogenous components as explained previously. Those components for each EEG channel are marked in grand average sERP curves  coinciding with the known morphology of P100, N140, P300 . We have not systematically extracted the components earlier than 100 The P100 component is thought to reflect early sensory processing of the tactile stimulus, specifically the activation of primary somatosensory cortex in response to the tactile input . PreviouIn our study, statistical analysis of the P100 component amplitude, latency, or area under the voltage curve did not reveal any significant differences in any of the channels or stimulation locations. The grand average values of peak amplitudes reveal a trend of increase for the attended condition in all channels except C4 for D, and C3 and P3 for V. The grand average latency values for both stimulation locations and all channels show an increase in latency when stimulus is attended, however these differences didn’t reach statistical significance. These results imply a low sensitivity of P100 component features to our novel attention task/paradigm.N140 is defined as a prefrontal component that shows sensitivity to attention change . AmplituµV (ADSD) vs. −0.94 ± 1.34 µV (AVSD), and 0.06 ± 1.20 µV (AVSV) vs. −0.71 ± 1.37 µV (ADSV). Statistically significant increases of amplitude in attended condition are shown for channels C3 and P3 for the D location, and in all channels except CP3 for the V location. These results are in contrast with related studies using the oddball paradigm to assess tactile attention task effects on somatosensory ERPs, [p < 0.05) and C4 (p < 0.05) for the D location , and for all channels except P3 for the V location . Area under the curve analysis showed statistically significant difference for channel P3 (D location) and channels C3, Cz, P3 and C4 (V), where the area under the curve decreased in attended condition.In our paradigm we detected the decrease of negativity of the N140 peak amplitude for all channels in the attended condition, for both stimulation locations. Average peak amplitude over all channels is −0.28 ± 1.24 ry ERPs, , where ams after a stimulus is presented. It is thought to reflect the automatic processing of novel or unexpected stimuli. P3b is a later subcomponent that occurs between 300–500 ms after a stimulus is presented. P3b is associated with the evaluation of the significance of a stimulus, memory retrieval, and decision-making processes. P3b is typically elicited by a more complex and meaningful stimulus, such as a target stimulus in a task where the participant is required to respond to it [ms inter-stimulus intervals produces the novelty effect reflected in P3a, which is more pronounced in the attended condition (reflected in P3a amplitude increase), while the counting task of the attended stimuli reflected in the P3b presence may be attributed to the stimuli evaluation and memory processes (coinciding with P3b increase in attended condition). P3a and P3b are two distinct subcomponents of the P300 wave that occur in response to an external stimulus and are involved in stimulus evaluation or categorization. Both P3a and P3b are commonly studied in the context of cognitive and neural processing, and they have different functional and neural origins. P3a is an early subcomponent of the P300 ERP that typically occurs between 200–300 nd to it ,38,39. OµV (ADSD) vs. 1.69 ± 1.62 µV (AVSD), and 3.16 ± 1.12 µV (AVSV) vs. 2.20 ± 1.07 µV (ADSV). The statistical comparison showed significant differences in the peak amplitude for all channels for both stimulation locations, except for channel P3, the V location. Analysis of P3a latency did not result in statistical differences in any of the channels. A statistically significant increase of area under the P3a component in the attended condition is found in the Cz channel (D location), and for C3, Cp3, Cz and C4 channels (V location).More specifically, our results show the increase of P3a peak amplitude in the attended condition for both stimulation locations  vs. 1.53 ± 1.40 µV (AVSD), and 3.23 ± 1.32 µV (AVSV) vs. 1.96 ± 1.50 µV (ADSV)). Area under the P3b peak proved to be the most consistent index since it gave statistically significant differences for all channels, and for both V and D locations.Statistical analysis of P3b component amplitude, latency, and areas under the curve, showed the most consistent differences between attended and unattended conditions. For both stimulation locations, all channels showed significant increase in peak amplitude for the attended condition  are a relatively new and emerging field of research, and as such, they have received less attention than other types of BCIs, such as visual, auditory, or motor BCIs ,41,42,43The limitation of the current study is the lower number of recorded EEG channels which prevents a more comprehensive spatial analysis of the obtained sERP components. This limitation should be addressed in future basic studies exploring the effects of endogenous spatial tactile attention on sERP. The selection of the EEG channels in our current study was constrained to the analysis of sensorimotor areas since the effects of our recently presented electrotactile BCI for sensory training  are expeFuture work will involve testing the identified features of N140, P3a, and P3b in the online classification of the endogenous spatial tactile attention. Moreover, this paradigm may be utilized both for sensory training in the stroke population and possibly as a communication channel in the ALS population.The results of this study are the first to show that the sERP morphology associated with a novel sustained spatial electrotactile attention task, first introduced in our recent work exploring electrotactile BCI for sensory training . Our res


Interestingly, LMSN composites possess an unconventional opposite sensing behavior to piezoresistive sensing (resistance reduction) and impact stimulation (resistance growing), thus they can distinguish the low and high energy stimulus. Ultimately, a soft protective vest with thermal management and impact monitoring performance is further fabricated, and it shows a typical wireless impact‐sensing performance. This method is expected to have broad application potential in the next‐generation wearable electronic devices for human safeguarding.This work illustrates a “soft‐toughness” coupling design method to integrate the shear stiffening gel (SSG), natural leather, and nonwoven fabrics (NWF) for preparing leather/MXene/SSG/NWF (LMSN) composite with high anti‐impact protecting, piezoresistive sensing, electromagnetic interference (EMI) shielding, and human thermal management performance. Owing to the porous fiber structure of the leather, the MXene nanosheets can penetrate leather to construct a stable 3D conductive network; thus both the LM and LMSN composites exhibit superior conductivity, high Joule heating temperature, and an efficient EMI shielding effectiveness. Due to the excellent energy absorption of the SSG, the LMSN composites possess a huge force‐buffering (about 65.5%), superior energy dissipation (above 50%), and a high limit penetration velocity of 91 m s Multifunctional wearable leather/MXene/shear stiffening gel (SSG)/nonwoven fabric (NWF) (LMSN) composites with integrated properties of electromagnetic interference shielding, piezoresistive sensing, thermal management, and safeguarding are fabricated via assembling the LM, SSG, and NWF. A novel intelligent protective vest is further developed based on LMSN composites, and it possesses high promise for next‐generation wearable electronic devices with human safeguarding. Sensors with excellent flexibility were crucial perception units in smart electronic devices and most of the previous works were focused on the high sensitivity and rapid response to external stimuli. However, the stability and sensitivity under high energy impact which is very important for the practical application of electronic devices are often neglected due to the difficulty of structure designation. There is an urgent demand for wearable devices that can cope with the ubiquitous mechanical collision in daily life and high‐speed impacts in extreme situations, while sensing the impact simultaneously. Therefore, developing a facile, low‐cost, and efficient strategy for fabricating multifunctional wearable electronic devices integrated with high flexibility, good electrical conductivity, and superior impact resistance is highly required.As people's expectations of electronic devices become higher and higher, wearable technology has been developed into one of the largest growth industries. Different from traditional electronic devices, wearable ones possess ideal flexibility and convenience, and easily combine with the human body.3, 4 As an ancient natural material, leather with a 3D porous structure is composed of multilevel collagen fiber bundles, which enable itself to be a versatile matrix for flexible electronic devices. Ma et al. reported a multifunctional nanocomposite by decorating silver nanowires into leather via the facile vacuum‐assisted filtration process. The AgNWs/leather nanocomposites exhibited excellent Joule heating, electromagnetic interference shielding, and piezoresistive sensing behavior, which demonstrated high promise for next‐generation wearable electronic devices. Wang et al. directly edited the natural leather structure for obtaining multifunctional thermal camouflage armor by in situ growth of SiO2 nanoparticles. The special porous fiber structure and rough surface also endow the leather with good hydrophobicity and mechanical properties. Therefore, leather is considered a prospective substrate candidate in wearable electronic devices. However, although leather often has been used as a natural protective device since ancient times, the further improvement in its protection performance is still scarce. Moreover, it has been proved that the microstructure of the flexible substrates could significantly improve the sensing property of the flexible sensor. Regarding excellent flexibility and wearability, the integration of leather with versatile multifunctions plays a key role in developing high‐performance wearable electronic leathers with wonderful protecting behavior.For thousands of years, leather has been a popular clothing material because of its good flexibility, high permeability, biocompatibility, as well as mechanical strength.18 As  showing strong energy dissipation ability owing to phase transition (viscous state to solid state) during the impact process. Mostly, SSG had been used to strengthen flexible materials such as Kevlar fiber and polyurethane sponges, achieving good impact resistance performance. Liu et al. embedded the SSG in a chitosan/MXene lamellar architecture skeleton, and the resulting composite effectively attenuated the impact force of 85.9−92.8%. Recently, a new type of negative Poisson's ratio skeleton composites filled with SSG has been developed and the coupling effect between the auxetic effect of the structure and the energy dissipation of SSG was clearly observed. The results showed that the appropriate structure could enhance the impact resistance of composites, and the laminated structure showed better impact resistance ability when impact velocity exceeded a critical range. Our previous work reported a toughness–flexibility coupling structure based on leather and conductive carbon nanotube (CNT)/SSG, which showed efficient anti‐impact and superior impact sensing performance. However, due to the low conductivity and weak shear thickening behavior of CNT/SSG nanocomposite, further improvement of the energy dissipation, sensitivity, electromagnetic interference (EMI) shielding, and thermal management for the leather composites remains an urgent challenge to be solved.Shear stiffening gel (SSG), as a typical rate‐dependent material, has an obvious modulus enhancement effect under external stimulation,25 sho Xie et al. reported a high‐performance flexible piezoresistive sensor based on a 3D MXene/polyethylenimine network, which could monitor various human activities in real‐time and exhibit an excellent Joule heating performance. Obviously, owing to its superior conductivity and unique multisurface characteristics, the MXene is suitable for various substrate polymer materials to improve electric characteristics. In considering the porous fiber structure/rough surface, flexible/toughness nature, and sustainable natural resources, the MXene/leather composites possess large superiority in wearable electronics. Unfortunately, the relatively safe, comfortable, and multifunctional wearable electronic leather composite has not been well addressed.MXene, a 2D transition metal carbide, possesses outstanding metal‐like conductivity, large specific surface area, and excellent mechanical properties, shows extensive application potential in the thermal management and electromagnetic shielding fields. By a vacuum‐assisted filtration method, Gong et al. fabricated a phase change capsule/MXene/polyvinyl alcohol composite film based on MXene mortar, which realized Joule heating under the stimulation of low voltage (1.5 V) and excellent EMI shielding effectiveness values (43.13 dB).34 Xie−1). Interestingly, the LMSN composites could distinguish the low and high energy stimulus by exporting negative and positive resistance change, respectively. Finally, based on the synergy of each layer, an electrothermal protective vest for the human body was designed, realizing sensitive thermal management and impact monitoring by building the LM array area. As a result, the LMSN composites provided multiple protection for physical impact, low‐temperature injury, and electromagnetic radiation, demonstrating broad application prospects in wearable protective devices.Herein, this work reported a multifunctional leather/MXene/SSG/NWF (LMSN) composite with laminated structure prepared via suction filtration and assembling process, integrating capabilities of piezoresistive sensing, EMI shielding, anti‐impact, and thermal management together. The flexible and biocompatible leather was chosen as the substrate to carry MXene nanosheets. MXene nanosheets penetrated into the multilevel fiber structure of the leather to construct an efficient conductive network, which endowed the LM composites with excellent electrical performance. Then, the laminated structure of LMSN was assembled by introducing rate‐dependent SSG and nonwoven fabric (NWF) layers, and the product finally exhibited prime anti‐impact property of effectively buffering the impact force (65.5%) and high limit penetration velocity  phase shows the multilayer‐stacked structure. After etching and exfoliating, the MXene nanosheets show the nonordered thin‐flake structure  of the Al layer at 39°, which disappears in MXene owing to etching during the preparation. The typical peak (002) moves from 9.5° to a lower angle of 6.2°, which indicates the distance between the MXene nanosheet layers increases. Compared with leather, the representative peak (002) is found in LM at 6.2°, indicating the presence of MXene nanosheets in leather fibers.According to literature reported,37 mic−1, respectively. With the increase of MXene content, the electrical conductivity of LM increases exponentially. Next, the tensile mechanical property of them is investigated in Figure Then, LM composites  with different MXene contents were prepared  was used to evaluate the ability to shield EMWs with the calculation method in Discussion S1 in the Supporting Information.With the continuous advancement of flexible wearable electronic devices, more and more devices are used to prevent electromagnetic wave (EMW) pollution and shield humans from radiation. In general, the key factors affecting the electromagnetic shielding effect are the structure and conductivity of the materials. In view of the excellent conductivity of LM composites, they are considered promising candidate materials for electromagnetic shielding. Following the Schelkunoffs theory,39 theFigure T) of LM and LMSN are identical in the frequency range of 8.2–12.4 GHz (X‐band). The EMI SET of them is almost the same in the 8.2–12.4 GHz (X‐band), which are basically unaffected by frequency. Within the measurement range, the EMI SET of the sample is proportional to the electrical conductivity. The EMI SET of LMSN1, LMSN5, LMSN10, LMSN15, and LMSN20 are about 6, 15, 26, 31, and 40 dB, respectively. Note that the target value of the commercial EMI shielding materials is 20 dB, which can shield about 99% of incident EMWs energy in daily life. Therefore, LMSN10, LMSN15, and LMSN20 have reached the commercial standard, which can be used in promising EMI shielding applications. Figure R) and absorption (SEA) to the overall EMI SET. It is obvious that SEA of all samples accounts for a large proportion of SET. As shown in Figure A in SET of LMSN10, LMSN15, and LMSN20 are as high as 71.5%, 75.6%, and 75.8%, respectively. So the LMSN composites focus on microwave absorption to shield EMWs. The R, T, and A power coefficients are calculated and used to support this point in Figure R values of all samples exceed T and A, indicating that the EMW is mainly absorbed by multiple reflections inside the material. R‐value gradually increases with augmented MXene content, and the maximum value of R reaches 0.903. Furthermore, all the T‐values of the sample are very low. The LMSN10, LMSN15, and LMSN20 can block about 99% of EMWs, indicating that the LMSN composites possess high antipermeability performance to EMWs. The electromagnetic shielding effect of the LMSN10 composite after being hit by a hammer was measured /R0) is defined to characterize piezoresistive sensing ability, where R0 represents the initial resistance of the sample, R represents the resistance of the sample after applying pressure. The resistance changes of the LMSN composites increase as the pressure gradually increases, and possess a wide range of pressure perception (0–400 kPa), indicating their excellent piezoresistive sensing performance. During the entire compression process, the resistance gradually decreases, because the pressure causes the conductive layers in the 3D network to contact closely. According to the different applied pressures, the sensing ability of the LMSN sensors is divided into two stages. When the pressure is lower than about 70 kPa, the LMSN sensor shows a high sensitivity change (Stage I), which may be benefitted from the multilevel structure of the leather fiber being compressed to form a more compact 3D conductive network in Figure R/R0 starts to decline slowly.LM composites possess excellent conductivity due to the generous MXene nanosheets attached to the leather fibers. Then, the copper electrode is adhered to the fiber side surface of the LM, and the SSG and NWF as the protective layer were assembled to form a LMSN piezoresistive sensor. R/R0 of the LMSN10 sensor under excitation with different loading modes and different frequencies is tested. As illustrated in Figure R/R0 under the excitation of the triangular waves with different frequencies when the pressure is kept constant. And it also can sense frequency changes, different frequencies have different response signals. Similar characteristics under square waves loading with different frequencies but different response signals are exhibited in Figure R/R0 , showing excellent application potential for the intelligent protective clothing field.To further study the effect of dissipated energy of the LMSN composite, the acceleration data were calculated to quantify energy. Figure 2.5Figure vi) is measured by a laser velocimeter with an oscilloscope and adjusted by controlling the pressure of the gas gun. The sample is fixed on the clamp and reinforced by bolts. The high‐speed camera is used to record the instant of sample destruction and calculate the residual velocity (vr) of the bullet.To test the application potential of the LMSN composite in the field of protection, the ballistic impact performance of LMSN10 was tested by the ballistic system presented in vl is the limit penetration velocity of LMSN, a and p are the specific parameters. The function fitting curve has well coincided with the experimental point, indicating that the limit penetration velocity of LMSN is 91 m s−1, which is greater than 83 m s−1 that of leather. As shown in Figure −1, pure leather will be penetrated directly. The LMSN composite can prevent the bullet from penetrating, showing better anti‐impact performance. When the vi is greater than vl, the leather and the LMSN will be penetrated by bullets. Due to the modulus enhancement effect of SSG under external stimulation, the residual velocity of the LMSN is always less than that of leather. However, when the vi is high enough, the gap between the residual velocity of leather and that of LMSN will gradually narrow because the shear stiffening property of SSG will be weakened with the increasing velocity. Furthermore, defining the dissipated energy asEd is the energy dissipation and m is the bullet mass. As illustrated in Figure The relationship between residual velocity and incident velocity is shown in Figure 40 could also be monitored, which also verified the accuracy of the Bluetooth transmission module. To conclude, the electrothermal vest has both electric heating capacity and impact‐response characteristics, which will be expected to apply in protective clothing in cold environments.The soldiers on duty at the border are often in a harsh cold environment, it is sometimes difficult to take into account the effect of providing temperature for their bodies when wearing armor. So the demand for protective clothing with thermal management performance is increasingly urgent. The excellent anti‐impact and thermal management performance of LMSN composites prove their application potential in this field Figure 9a. ConseFigure 42 Wit3T of about 26 dB due to the ohmic loss and multiple reflections of EMWs in multistage fiber structure, a high saturation temperature of about 98 °C at 3.0 V and precise temperature control by voltage due to the resistance stability. Moreover, the LMSN composite also showed a high saturation temperature of about 87 °C at 3.0 V and identical EMI shielding capability after assembling SSG and NWF. Interestingly, the LMSN composites possessed better anti‐impact performance in comparison to the LM composites. On account of the rate‐dependent characteristic and energy dissipation performance of SSG, the LMSN composites possessed an obvious force‐buffering (about 65.5%) and energy dissipation (above 50%) performance, and a high limit penetration velocity of 91 m s−1, which were superior to the pure leather. Moreover, the LMSN composites could distinguish the low and high energy stimulus by exporting negative and positive resistance change, respectively. Eventually, a novel intelligent protective vest was designed and it showed excellent thermal management and wireless impact‐sensing performances, which demonstrated the application potential of the LMSN composites for next‐generation wearable electronic devices with human safeguarding.In summary, this work reported an LMSN composite via assembling the LM, shear stiffening gel, and NWF. The MXene nanosheets were immersed in the porous fiber network structure of leather through suction filtration to form an efficient 3D conductive network, which endowed the LM with excellent conductivity. Therefore, the LM composites showed superior EMI shielding, piezoresistive sensing, and Joule heating properties. Under a low MXene content, the LM10 composite achieved an EMI SE43AlC2 MAX powder with 400 mesh used in this study was purchased from 11 Technology Co., Ltd., Jilin, China. Lithium fluoride (LiF) was purchased from Aladdin Chemical Co., Ltd., Shanghai, China. Hydrochloric acid (HCl), boric acid, hydroxyl silicone oil , and octanoic acid were provided by Sinopharm Chemical Reagent Co. Ltd., Shanghai, China. All chemical reagents were used as received without further purification.Natural cow leather (≈1 mm in thickness) and NWF were the commercially available product, China. Ti First, boric acid and silicone oil were mixed and stirred uniformly with a mass ratio of 1:20, followed by the mixture being heated in an oven at 180 °C for 2 h. Then the octanoic (250 µL per 200 g hydroxyl silicone oil) was added to the mixture, and continued to heat for 30 min in the oven. Finally, the SSG was obtained after cooling the mixture to room temperature.SSG was prepared according to previous work.43 Fir First, LiF (4 g) and HCl (80 mL) were mixed into the homogenous solution in a Teflon beaker, followed by adding Ti3AlC2 MAX powder (4 g) at a slow rate. Afterward, the mixture solution reacted for 24 h at 35 °C to etch the Al layers from the Ti3AlC2 phase. To obtain the multilayer MXene, the reacted product needed to be washed repeatedly with deionized water until the solution pH reached ≈6. Then, the above multilayer MXene was continued with ultrasonic treatment in ice water for 2 h and centrifuged at 3500 rpm for 1 h to exfoliate multilayer MXene. Finally, the MXene aqueous solution was obtained by collecting the exfoliated supernatant.The delaminated MXene nanosheets were synthesized according to the methods described previously.44 FirThe industrial substances of the leather surface were removed by alcohol processing. Subsequently, the leather was fixed on the suction filtration device with the fiber side on the top. The MXene nanosheets were uniformly dispersed into the multistage fiber network of the leather by vacuum filtrating for 2 h. Then, a series of flexible LM composites with different MXene mass fractions were fabricated by filtrating different volumes of MXene aqueous solution. The LM with MXene aqueous solution filtration volumes of 1.0, 5.0, 10, 15, and 20 mL were, respectively, named LM1, LM5, LM10, LM15, and LM20. Finally, the SSG layer and NWF layer were step‐by‐step assembled on LM composites to form the LMSN laminated structure.The microstructure of materials was characterized by an SEM  equipped with an EDS system. The MXene nanosheets were observed by field‐emission TEM . The height morphology of MXene nanosheets was characterized by the AFM . The crystalline phase of MAX powder, MXene, leather, and LM composite was characterized by XRD (SmartLab). The FT‐IR spectra were tested by a Nicolet Model 759 spectrometer. The EMI shielding performance of different samples in the 8–12 GHz region (X‐band) was explored by a commercial vector network analyzer  with a waveguide cavity.The rheological properties of the SSG were investigated by a rheometer . A parallel plate with a diameter of 20 mm and a fixed gap of 1 mm was adopted. A certain mass of SSG was taken the oscillation frequency scanning with a shear frequency range of 1–100 Hz for testing. The mechanical performance of LM composites was tested by the material test system . The electrical performance of the sample was measured by the ModuLab materials test system . The anti‐impact properties tests were evaluated including the low‐velocity drop hammer impact test and the high‐velocity ballistic impact test. In the drop hammer test system , the hammerhead weighing 0.56 kg was released from 10 to 60 cm, and the sample was placed on the center of the force sensor. An oscilloscope was used to collect force and acceleration data. The ballistic impact test system was consisted of a high‐speed camera, a gas gun, a laser velocimeter, and a digital oscilloscope. The bullet used was a small steel ball with a mass of 2.08 g.The quantitative data were presented as mean ± standard deviation (s. d.) from at least three measurements. Normalization treatment was performed on the resistance to characterize the sensing performance. Pre‐processing of data for (EMI) shielding and impact testing could be found in Discussion S1 and S2 in the Supporting Information.The authors declare no conflict of interest.Supporting InformationClick here for additional data file.Supplemental Video 1Click here for additional data file.Supplemental Video 2Click here for additional data file.Supplemental Video 3Click here for additional data file.Supplemental Video 4Click here for additional data file.


The PTi powder was utilized as a filler in the fabrication of ultrafiltration nanocomposite membranes using polysulfone (PSf) as the polymer. The synthesized nanoparticles and membranes were analyzed using various techniques, including BET, TEM, XRD, AFM, FESEM, FTIR, and contact angle measurements. The membrane’s performance and antifouling properties were also assessed using bovine serum albumin (BSA) as a simulated wastewater feed solution. Furthermore, the ultrafiltration membranes were tested in the forward osmosis (FO) system using a 0.6-weight-percent solution of poly (sodium 4-styrene sulfonate) as the osmosis solution to evaluate the osmosis membrane bioreactor (OsMBR) process. The results revealed that the incorporation of PTi nanoparticles into the polymer matrix enhanced the hydrophilicity and surface energy of the membrane, resulting in better performance. The optimized membrane containing 1% PTi displayed a water flux of 31.5 L/m2h, compared to the neat membrane water value of 13.7 L/m2h. The membrane also demonstrated excellent antifouling properties, with a flux recovery of 96%. These results highlight the potential of the PTi-infused membrane as a simulated osmosis membrane bioreactor (OsMBR) for wastewater treatment applications.This study explored the use of a combination of hydrothermal and sol–gel methods to produce porous titanium dioxide (PTi) powder with a high specific surface area of 112.84 m Wastewater treatment is a critical issue for public health and the environment. Industrial and domestic wastewater contains contaminants that can cause serious harm to human health and aquatic life if not treated effectively . MembranAn osmosis membrane bioreactor (OsMBR) is an innovative technology that combines osmosis and bioreactor processes for wastewater treatment . The OsMRecent studies have focused on enhancing the performance of the OsMBR system by improving the membrane properties, such as hydrophilicity and permeability, to reduce fouling and enhance the system’s lifespan . The useOn the other hand, porous nanoparticles offer several advantages over nonporous nanoparticles for a variety of applications. Firstly, their high surface-area-to-volume ratio provides a larger surface area for a possible reaction. Secondly, their porous structure allows for the easy diffusion of small molecules or ions, which can improve their performance as adsorbents or separation agents. Additionally, the pore size and shape of the nanoparticles can be tailored to specific applications, allowing for selectivity and specificity. The porous structure also provides mechanical stability and can help prevent the aggregation or degradation of the nanoparticles. Lastly, the porous nature of the nanoparticles can be used to encapsulate or immobilize other materials, such as drugs or enzymes, for targeted delivery or catalysis ,17.2) is a type of nanoparticle that has gained significant attention due to its unique properties and potential applications. The porous structure of TiO2 nanoparticles allows for improved surface area and reactivity, making them effective for various chemical reactions [2 nanoparticles arise from their ability to absorb light energy and create electron–hole pairs, which can then react with molecules to generate reactive oxygen species or reduce pollutants. Porous TiO2 nanoparticles can also be used as adsorbents for removing contaminants from water or air due to their high surface area and adsorption capacity [2 nanoparticles can be used as a drug delivery system [2 nanoparticles have shown great potential in a variety of fields, including environmental remediation, catalysis [2 nanoparticles, porous TiO2 nanoparticles demonstrate a greater surface area, which enhances their capacity to adsorb organic and inorganic contaminants during water treatment. This, in turn, can augment the removal efficiency and performance of the membrane. Moreover, the porous nature of TiO2 nanoparticles can enhance the hydrophilicity of the membrane surface, leading to improved water transport and reduced fouling by preventing the adsorption of organic and inorganic substances. The porous structure also promotes the stability and durability of the membrane, making it more resistant to damage and deformation [More importantly, porous titanium dioxide  membrane and the forward osmosis (FO) process. The FO process involves separating a solution with a lower concentration of dissolved solids (the feed solution) from a solution with a higher concentration (the draw solution) through a semi-permeable membrane. Meanwhile, the UF membrane in the hybrid system is used to filter out large molecules, such as BSA, and investigate the fouling effect, which is a major issue in membrane bioreactors. By using this hybrid approach, the FO process can effectively separate water molecules from dissolved solids while minimizing the fouling effect. Furthermore, this study aims to contribute to the development of advanced membrane materials and technology for wastewater treatment. The results of this research provide insights into the potential of PTi-infused membranes as an efficient and antifouling membrane bioreactor for wastewater treatment applications. Overall, this study demonstrates the importance of innovative approaches to address the challenges of wastewater treatment and the potential of porous nanomaterials to enhance membrane performance and reduce fouling.4)2, and cetyltrimethylammonium bromide  were purchased from Sigma Aldrich. To prepare the simulated wastewater, bovine serum albumin (BSA) (Sigma Aldrich) with a molecular cutoff of 66 KDa was employed. To provide sufficient driving forces, poly  was utilized as a draw solution.Polyvinyl pyrrolidone  with a molecular weight of 5500 g/mol and polysulfone Udel P-1700  were applied for polymeric membrane fabrication. N-Methyl-2-pyrrolidone (NMP), a solvent from the Sigma Aldrich Company, was used. To create the PTis, ethanol, Ti(SO4)2 was dissolved in distilled water, and a cetyltrimethylammonium bromide (CTAB) solution was added to the mixture. The resulting mixture was then stirred for 30 min before being allowed to settle for 12 h at room temperature. Subsequently, the mixture was transferred to an autoclave and subjected to hydrothermal treatment at 100 °C for 72 h. Once complete, the powders were separated from the solution through centrifugation and washed with water and ethanol to remove any organic impurities. Finally, the samples were calcined at 165 °C temperature for 6 h to promote crystallization [Initially, Ti as listed in One of the main objectives of this study was to explore the use of an ultrafiltration (UF) membrane in a forward osmosis process (hybrid UF/FO) as a simulated osmotic membrane bioreactor (OsMBR) application. In this work, BSA was used as a foulant model to simulate organic wastewater, which is a major issue in MBRs. To generate the driving force for the FO process, poly  was used at a concentration of 0.6% as the draw solution, resulting in a pressure of 0.6 bar  on the U2 within the cell. Two pumps were utilized to cycle the feed and draw solutions through the system while maintaining a constant temperature of 25 °C. In order to measure the water flow, two digital balances were positioned beneath each tank [ach tank . This se2 h) was calculated by the following equation:The water flux In this equation, 2 h) was obtained by utilizing the following equation [The solute flux equation :(2)Js=ΔCat 25 °C .Bovine serum albumin (BSA) was used for the preparation of the simulated wastewater (feed solution), and 0.6% poly (sodium 4-styrene-sulfonate), which is equal to 0.6 bar, was applied as an osmosis solution because of its particle size, which can be recovered by UF . p (kg/m2h)) was started after 10 min of the flow change. The membrane was cleaned with flowing distilled water without applying any pressure for 30 min after filtering the effluent solution for 210 min. After that, the test for clean water filtration continued for an additional 60 min. Therefore, 370 min was determined to be the overall filtering time. The flux recovery ratio (FRR) is given as [Fouling experiments were performed in a lab setting employing an osmosis membrane bioreactor system and a crossflow filtration unit. The input flow was changed from pure water to the effluent solution (BSA) after 60 min of pure water filtration. Then, the measurement of the solution that penetrated through the membrane Rt%=In this regard, 2 nanoparticles.The properties of the inorganic particles and membranes were characterized using a variety of techniques, including transmission electron microscopy (TEM), scanning electron microscopy , Fourier transform infrared spectroscopy , surface area analysis, atomic force microscopy, X-ray diffraction , and contact angle measurements. Finally, the contact angle of the membranes was measured using the sessile drop method using a contact angle goniometer . Brunauer–Emmett–Teller (BET) and Barrett–Joyner–Halenda (BJH) tests were conducted to determine the surface area and the pore size distribution of the nanoporous TiO2 area with a 512 × 512-pixel-width resolution and an adaptive scan rate ranging from 0.2 to 2 Hz. The surface topography of the membranes was evaluated in terms of several parameters, including the root mean square of height deviations (Rq), the average plane roughness (Ra), the maximum peak-to-valley distance (Rz), and the ratio of the actual surface area for a rough surface to the planar area (r). A value of r=1 indicates a perfectly smooth surface. Two samples of each membrane were analyzed using AFM. The morphology of the resulting nanocomposite membranes was analyzed using atomic force microscopy . AFM images were acquired in tapping mode, scanning over a 10 × 10 μmSLG) was determined, since the apparent contact angle can be influenced by surface roughness [2) was calculated using the Young–Dupre equation:θ is the average water contact angle, which decreases as surface hydrophilicity increases , and the water surface tension (2 at 25 °C. The roughness area ratio (r), obtained from the AFM topographical images, is also included in the equation. The minimum value of r for an ideally smooth surface is unity, which maximizes To characterize the intrinsic surface hydrophilicity of the membranes, the solid–liquid interfacial free energy (Δoughness . The ΔGS as well . The two main methods for determining nanoporous nanoparticles’ pore size distribution and surface area parameters are the BET (Brunauer–Emmett–Teller) and BJH  methods. The BET method is used to calculate the specific surface area of a material, which is a measure of the total surface area of the pores per unit mass of the material. The BJH method, on the other hand, is used to determine the pore size distribution of a material .2 are shown in 2 nanoparticles have a specific surface area of 112.84 m2/g, as measured by BET . The SEM images of the pristine and nanocomposite membranes are shown in −1 (symmetric O-S-O stretching), 1300 cm−1 (asymmetric O-S-O stretching), 1250 cm−1 (asymmetric C-O-C stretching), 1500 cm−1 (CH3 bending vibration), and 1405 cm−1 (C-C aromatic ring stretching) correspond to the specific functional groups of the PSf polymer substrate [−1 compared to the unmodified membrane, indicating the presence of titanium dioxide nanoparticles and their possible hydrogen interaction with functional OH groups of the PTis and active site of nanocomposite membrane.ubstrate . The FTIThe smoothness and roughness of the membrane surfaces were analyzed using atomic force microscopy (AFM).  surface . In factIn addition to this, the contact angle, determined using the sticky drop approach, is also shown in The elemental composition of the pure polysulfone membrane and the modified polysulfone membrane with added porous titanium dioxide nanoparticles was analyzed using X-ray photoelectron spectroscopy (XPS), and the results are presented in 2h for 2% TPs, with the highest flux observed in the membrane containing 2% nanoparticles. These findings suggest that modifying the nanocomposite membrane with nanoparticles can enhance OsMBR performance. In fact, the use of these nanoparticles increases the surface-area-to-volume ratio of the membrane, which creates more active sites for water adsorption and results in improved water flux. Moreover, the use of nanoparticles with enhanced hydrophilicity can modify the membrane’s surface properties and increase its water flux [This section reports on the water and reverse solute flux through PSf and nanocomposite membranes during the UF/FO hybrid process as the simulated OsMBR. The addition of porous titanium dioxide nanoparticles to the nanocomposite membrane resulted in significantly higher fluxes compared to the PSf membrane. ter flux . 2h, as demonstrated in The UF membrane, made of 18 wt% concentration of PSF polymer, is specifically engineered to allow only molecules with a molecular weight less than or equal to 125 kDa to pass through while retaining larger molecules. This selective filtration process is achieved by designing the pore size of the membrane in a way that separates molecules based on their size. Remarkably, the PSF UF membrane with a 125 kDa molecular cutoff is also able to separate molecules that are smaller than its molecular cutoff. For example, the membrane effectively retains molecules with a molecular weight of 66 kDa or 70 kDa, such as BSA in the feed solution and poly (sodium 4-styrene-sulfonate) in the draw solution, respectively. This selectivity is possible because of the size and structure of the membrane’s pores, which are specifically designed to be highly selective for certain molecular weights. With an increase in the concentration of nanoparticles in the membrane, the surface pores (as shown in SEM 5) decreased in size , leading2 nanoparticles into a membrane on the membrane’s antifouling properties. Typically, high-quality membranes display high flux, a low fouling propensity, and consistent selectivity over time [This section examines the impact of incorporating porous TiOver time . The behver time . Incorpover time . Additiover time . TherefoRegarding the type of fouling, membrane fouling can be classified as reversible or irreversible, where reversible fouling occurs when fouling materials can be easily removed by washing with water, whereas irreversible fouling occurs when fouling materials cannot be removed by washing . Backwas surface . The nanoparticles can also modify the surface chemistry and morphology of the membrane, creating a more hydrophilic surface, as reported in 2 were higher than that of the neat PSf membrane (58%). The 0.5% PTi composite membrane demonstrated an FRR of 80%, while the 1% and 2% PTi composite membranes exhibited almost 96 and 98% FRRs, respectively. This indicates that the addition of PTi enhanced the fouling resistance of the membrane. The trend in the FRR is consistent with the hydrophilicity of the membranes, as indicated in 2 NPs [3O4 [The FRRs (flux recovery ratios) of the membrane samples are presented in in 2 NPs , silica in 2 NPs , and Fe3NPs [3O4 . FurtherNPs [3O4 . Nanopar2 nanoparticles demonstrated better performance in terms of water flux and antifouling properties. In this study, the modified porous PSf ultrafiltration membranes showed about a 100% increase in water flux. The modified membranes also achieved a 96% flux recovery after the filtration of the bovine serum albumin (BSA) solution, which is among the top 5% of the best-performing membranes listed in The water flux and antifouling properties of various composite membranes are compared in 2/g) into nanocomposite polysulfone ultrafiltration membranes was found to significantly enhance membrane performance, particularly for membrane bioreactor applications. In this study, a hybrid UF/FO process was employed to simulate the osmosis membrane bioreactor process, which has advantages over pressure-driven membrane systems, including lower energy consumption and a reduced fouling tendency. The addition of PTi to the PSF polymer enhanced the membrane’s hydrophilicity and pure water flux while also improving its fouling resistance due to its high hydrophilicity and lower roughness area ratio (r). The optimized membrane, containing 1% PTi, exhibited a water flux of 31.5 L/m2h, compared to the neat membrane’s 13.7 L/m2h, and a lower solute flux of 0.5 g/m2h compared to the other membranes tested. This reduction in reverse solute flux is important since high solute flux can decrease driving forces and, consequently, membrane performance. The MT1 membrane also demonstrated excellent antifouling properties, with a flux recovery ratio of 96%, because it reduced irreversible fouling to less than 5%. These findings highlight the potential of the PTi-infused membrane as a membrane bioreactor (MBR) for wastewater treatment applications.To summarize, the incorporation of 0.5 to 2% porous titanium dioxide powder (with a high surface area of 112.84 m