Microplastic pollution in different environmental matrices is a serious concern in the recent times. Personal care products and washing of synthetic fabrics are some of the main sources of microplastic pollution. In this work, a novel simplified, effective and sustainable method for extraction of microplastic particles from face scrub and laundry wastewater was developed. Different parameters affecting the extraction were analysed and the extraction process was optimised. The extraction efficiency of the proposed method was found to be ~ 94.1 ± 1.65%, which was slightly better than the previously available method with an advantage of ease in extraction and lesser time and resource consuming. The developed method was used to demonstrate the extraction of microplastic particles from 12 face scrub samples with different brands. It was found that the samples contained microplastic particles of varying size. The physical and chemical structure intactness of microplastic particles during the extraction was also analysed and found to be acceptable. The developed extraction method was also applied for the extraction of microfibers from the laundry wastewater. It was found that this proposed method is suitable to make the cleaner extracted samples for an easy and more effective qualitative and quantitative analysis of MPs. User friendliness and easy availability of plastic products made their way to the daily life. Production of plastic and similar materials are exceeds 320–335 million tonnes/year3. This made the plastic pollution a pressing environment issue all over the globe and the impact is much higher in countries with low recycling rates of plastic products. Plastics which are small, with a size of < 5 mm are defined as microplastics (MPs)5. Based on the source of generation, MPs are classified into primary and secondary microplastics6. The presence of MPs in different aquatic systems  at varying concentration levels has already been reported10. Also, it was found that MPs can be in various shapes like film, fragment, spherical, fiber and others11.The volume of waste materials generated as a result of human activities is increasing in the recent decades. Plastic is the most abundant waste material among those and it contributes about 60–80% of the total waste generated14. This lay the way to exposure with different organisms including human being. Major route of MPs exposure to human body are through inhalation and ingestion. It was reported that the possible ingestion rate can be 39,000–52,000 particles/person/year15. Also, the exposure through inhalation is estimated at about 48,000 MPs/day16. Thus exposed MPs were reported to be potential toxic to organisms18 and may cause oxidative stress and cytotoxicity due to its high surface area, chronic inflammation and cancer due to its persistence in the body19. Certain studies reported that MPs may release chemicals from its matrix or adsorbed chemicals from the environment20. Also, it may can act as a vector of pathogens which may can cause illness to humans21.Generally, MPs got long-term stability in different environmental matrices due to their physical and chemical properties22. Use of MPs in personal care products were started long back in 1972 when the first patent was granted for the use of plastic synthetic resins as scrubber in skin cleaners23. Generally, MPs in personal care products are in the form of “microbeads” which are plastic particles of less than 500 nm in diameter24. About 90% of the microbeads used in personal care products are made of polyethylene; but polypropylene and polystyrene are also being used25. It was proven that these microbeads from personal care products are rinsed off during usage and end up in drainage systems and then to wastewater treatment systems where it will pass through and find its way to ingestion by animals and human26.Usage of personal care products are one of the primary sources of microplastic generation and washing process of synthetic textiles may contribute to the pollution by means of being a secondary source. A study from China in 2021 estimated that more than 37 billion microbeads was reaching the environment through the wastewater treatment plants27. Among different MPs, these microfibers were found to be abundant in water bodies and aquatic organisms29. It was estimated that about 150,000 fiber particles were got released during the washing process of a kilogram of cloth load30. This form of MPs are considered to be imposed the highest threat to the marine biota than MPs in any other shapes32. Also, microfiber can act as a carrier of contaminants due to large surface area to volume ratio and high affinity to hydrophobic organic chemicals33. Microfibers are most commonly found MPs in the digestive system of aquatic animals34 and it cause intestinal blockage and other damages to these organisms36.Microfiber generation during the washing and processing of fabrics made of synthetic fiber greatly contribute to the microplastic pollution in wastewater37. Even if the ban of products in the market was implemented, the usage of the already purchased products by the customers will be continued. Thus, the daily generation of MPs continue and contribute to the pollution. The fact is that this usage and MP pollution during this transition period between the complete ban of microbeads and the complete usage of the already purchased products will be unaccounted. This indicates the need of constant analysis for the MPs occurrence in daily use products even when the legal ban is implemented.Many countries like United states, South Korea, Canada, Australia enforced the ban of the usage of MPs in daily usage products earlier. Later, many countries have initiated the same policy and recently China and Argentina also banned the usage of microbeads by the end of 2022. A recent study reported that the non-degradable MPs usage in daily use products are only implemented by a limited number countries and majority of the countries not banned the MPs usage yetIn order to effectively manage the consequences of microplastic on human being and environmental, it is needed to have periodic assessment of microplastic particles present in personal care products and discharged laundry wastewater. Analysis of microplastic includes the collection of samples, extraction of particles and analysing using analytical techniques like FTIR, Raman and similar methods. At present, the methods which are being utilised to extract microplastics involve the usage of different apparatus including heating device, vacuum filtration and membranes. Moreover, these methods are also time consuming. This demands the development of a novel extraction process in which there is simple, less time consuming and less reagents/devices needed. It is interesting to notice that the usage of acetone as an extraction agent for the extraction of MPs from face scrub or related products has not been reported. The thought of making use of the effectiveness of acetone as a solvent to dissolve the face scrub products to separate out the microplastic particles was the driving force for the present work. This research aimed at developing a novel simple and less time consuming, less resource required extraction method for the effective extraction of microplastic particles from commercially available facial scrub using acetone as the extraction agent. The developed method was also used for the extraction of the microfibers from the laundry wastewater. Moreover, this proposed extraction method intended to derive a much cleaner microplastic particle sample by using acetone as an agent for organic matter clean up so that the identification and quantification of the final MP samples with the micro-FTIR analysis will be easier and convenient.A commercially available face scrub was purchased from the local shop and made use as the study material for the development of MP extraction method. This product was intentionally chosen from the left over of old stock since the products which are being marketed in Thailand in recent times are free of MP due to the revised norms. This sample was named as “FS1”. Laundry wastewater samples were collected from the student’s dormitory of Chulalongkorn University, Bangkok and made use for studies on the microfiber extraction. Briefly, a volume of five litres of laundry wastewater was collected three times during the operational hours in a clean glass vessel and mixed together to form a representative sample. The samples are stored in 4 °C before usage.An extraction protocol was developed to separate and quantify the MP in face scrub and laundry wastewater. Acetone was used in the extraction process and a schematic representation of the protocol is illustrated in Fig. Laundry wastewater sample is thoroughly mixed to ensure the even distribution of the microfibers. A 10 mL-laundry wastewater was immediately measured out into a clean glass beaker. The usage of this volume of wastewater was used to intentionally avoid the crowding of microfibers on the glass filter surface. A volume of 20 mL of acetone was added it the beaker and mixed well. The resultant mixture was poured into a glass beaker containing 50 mL of deionised water. The solution was further filtered through a 0.2 µm glass fiber filter using a vacuum filter device. The membranes were stored in a glass petri dish until analysis.−1 and an average spectrum of 32 scans was derived by using the Bruker OPUS 7.2 software. The polymer type was identified by comparing the spectra with the standard database library of Bruker. The size of extracted particles was determined by using the dimension measuring tool in OPUS 7.2 software.The size and polymer type of the extracted particles were analysed using micro-FTIR in attenuated total reflectance mode (ATR) with the help of a micro-FTIR spectroscope from Bruker, Germany (Bruker-Lumos II). The extracted particles were analysed by the micro-FTIR using Focal plane array (FPA) detector in ATR mode. Spectra were recorded in the wavelength range of 4000–400 cm−1 of scan rate was used to characterize the crystal structure of MPs extracted from the sample FS1 using the newly proposed method and the previous method. The characteristic XRD peaks of polyethylene particles were compared to elucidate if there is any change in crystalline structure of the particles due the extraction using acetone. Chemical functionality of the polyethylene particles was compared using the FTIR spectra derived in ATR mode . Zeta potential of the polyethylene particles was analysed using Dynamic Light Scattering (DLS) analyzer  and compared to elucidate any possible changes in the surface charge of the particles. Scanning Electron Microscopy (SEM) was used to analyse the surface structure of the MPs extracted from the sample FS1. High resolution microscopic images of the particles extracted in the traditional method and the newly derived method were captured using a scanning electron microscope . Images of particles extracted using two different methods were compared to figure out there is any differences in the surface structure of the particles which may possibly can happen due to the usage of acetone for the extraction.X-ray diffractometer  with Cu Kα radiation (λ = 0.1542 nm) in the 2θ angle range between 5° and 80° using 0.02° min38 and the newly developed extraction protocol from this research to elucidate the recovery percentages for the efficiency comparison. Pre-optimised extraction protocol was further validated by applying the method with other 12 samples of face scrubs produced by other brands. These products were collected from the customers and collected based on the assumption that these products were produced before the MPs ban in Thailand and still being used by the public. Collected samples were extracted using the new developed method and quantified. Experiments were conducted in duplicates and the statistical analysis in terms of mean ± standard deviation was reported. Microfiber extraction and analysis from laundry wastewater was also compared with the tradition membrane filtration method described elsewhere39.Extraction efficiency of the developed extraction protocol was validated by comparing with a standard method available in the literature. For this, a known amount (20 mg) of the polyethylene MPs were added to the face scrub sample. The extraction was conducted using the method reported by Enfrin et al.The present study was done under a set of precautionary measures to keep the quality control. All the sample collection and processing were done by using clean glass vessels. Usage of any kind of plastics vessels or tools was avoided intentionally to avoid the contamination. The vessels and tools were washed and rinsed with ultrapure water before usage. Cotton clothes and lab coat were used during the experiments. The processed samples were stored in glass petri dishes and covered with aluminium wrap to avoid the possible contamination. Blank samples were run during the analysis and the experimental values were subtracted from the values obtained for the blank sample.All the experiments were conducted in duplicates and the mean and standard deviation were calculated. The results were expressed as mean ± standard deviation.Samples of a commercially available face scrub (FS1) were used to extract the microplastics using acetone as an extraction agent. It was found that the face scrub dissolved well in acetone and leaves out the non-soluble part and MPs. Other contents of the face scrub were got dissolved or precipitated upon addition of acetone. From Fig. The selected commercially available face scrub was found to contain 3 types of particles with different colours and size as red, brown and white particles Fig. a–c. Red 42. The reason for wide usage of polyethylene beads in face scrub is due to their surface smoothness which causes less damage to the skin and the resultant redness43. Size distribution of each different type of MPs is expressed in Fig. The micro-FTIR analysis found that all the MPs were polyethylene particles. It has been reported that polyethylene is one of the most used MPs in the face scrubEffect of different parameters on the extraction efficiency of the extraction protocol was studied. The efficiency was elucidated in terms of the recovery % of spiked MPs in 1 g of the face scrub sample FS1 Fig. . All theEffect of sample to acetone ratio was analysed by varying the volume of acetone from 1 to 10 ml  to extract 1 g of face scrub. It was found that the acetone volume has considerable effect of the extraction efficiency of the method Fig. a. The hiEffect of varying concentrations of acetone as 40, 60, 80 and 100% on extraction efficiency was studied and the results are given in Fig. A study on the effect of extraction time on the extraction efficiency was also studied by varying acetone exposure from 1 to 15 min. As evident from Fig. The effect of repeated extraction steps on for the complete extraction of MP was studied by comparing one and two cycles of acetone addition and extraction Fig. d. It is 38 for the microplastic particle extraction from the face scrub by estimating the extraction efficiency from 1 g of the face scrub spiked with 20 mg of MPs, through each method and the results are given in Fig. Newly developed method in this study was investigated and compared with the previous method of MPs extraction by Enfrin et al.36, whereas the new proposed method of extraction with acetone was able to extract ~ 94.1 ± 1.65% of MPs. This shows that the newly developed method is efficient enough to perform the extraction more or less equal to the previous method. On the other hand, the new proposed method need very less time (1 min) for the extraction and does not require any heating process or membrane devices.About 93.6 ± 0.5% extraction efficiency was observed for the previous method12 face scrub samples belonging to different brands were collected from the users to be used in the validation of new extraction protocol. 1 g of each of the products was subjected for the extraction process and the resultant yield of MPs was expressed in Fig. The chemical functionality, crystallinity, zeta potential and morphology of the Polyethylene MPs extracted in previous method and newly proposed method were analysed and compared to see any physical or chemical aberration were happened during the extraction process with acetone Fig. . High reSEM images Fig. a,b of th−1, 2847 cm−1 and 718 cm−1, which corresponding to the CH2 asymmetric C–H stretching, CH2 symmetric C–H stretching and C–CH2 bending vibrations, respectively49, were observed in FTIR spectra of the particles extracted using previously reported and newly proposed methods. The presence of these characteristic peaks in both spectra shows that the chemical functionality is being preserved and no chemical functionality changes were happened even during the extraction using acetone as an extraction agent. The crystallinity of the MPs was analysed using the XRD spectroscopy  guidelines)52 with heating and filtration device. Moreover, the extraction efficiency of our proposed method in terms of recovery percentage could achieve highly at 94.1 ± 1.65%.The advantage of the proposed method is that it is lesser time (< 5 min), chemical consumption and it does not need any heating and filtration devices unlike the existing method. For instance, a previous method employed for the extraction of MPs from face cleansers utilised the digestion of the samples for a longer period of 5 days at 55 °C in 10% KOH solutionA novel simple, less time consuming and easier method of MPs extraction from face scrub and laundry wastewater was demonstrated in this study. Thus, the derived method was made use to extract, quantify, analyse and characterise the MPs based on its type and size. The present study demonstrated a novel method of extraction with minimal reagents and set up to do the extraction of MPs more sustainable way. There are few countries initiated the ban of MPs usage in different products which are being used in daily life. Nevertheless, many countries are still not legally banned the usage. Hence, there is a need for simpler and easier extraction and analysis methods and a periodic assessment of MPs usage in all possible daily use products to limit their release to the environment and possible contamination.


Such a low LOD can more effectively avoid the accuracy degradation of detection results due to measurement errors. Human serum samples have also been detected, with the good precision achieved. This work shows promising prospects in applications of label‐free, low‐cost, rapid, and convenient early screening of tumors.The detection of trace biomarkers is an important supplementary approach for early screening and diagnoses of tumors. An optical fiber near‐field enhanced plasmonic resonance immunoprobe is developed for the detection of the hepatocellular carcinoma biomarker, i.e., the alpha‐fetoprotein. Generic principles based on dispersion models and finite element analysis (FEA) models are developed to realize the optimized configuration of spectral characteristics of the immunoprobe. Dispersion models provide theoretical guidance for the design of the multilayer sensing structure from the perspective of the ray optics theory. FEA models provide theoretical guidance for the selection of coating materials from the perspective of the self‐defined dielectric constant ratio, i.e., the ratio of the real part to the imaginary part. The optimized configuration of the antibody coupling further improves the biosensing performance of the immunoprobe. The limit of detection (LOD) can reach down to 0.01 ng mL −1. Human serum samples are also been detected, with the good precision achieved. This work shows promising prospects in applications of label‐free, low‐cost, plug‐and‐play early screening of tumors.The design and the development of an optimized immunoprobe for the detection of the hepatocellular carcinoma biomarker are presented. The detection limit of the immunoprobe can reach down to 0.01 ng mL This makes it difficult to distinguish sub‐centimeter‐sized tumors, and thereby their application in the early screening for tumors is limited. Therefore, the detection of tumor markers has gradually become an important supplementary method to imageological diagnoses. Clinically high‐specific tumor markers include the alpha‐fetoprotein (AFP), the prostate specific antigen (PSA), the carcino‐embryonic antigen, the cancer antigen 15–3, and the cancer antigen 12–5, etc. Among them, AFP (molecular weight 70 kDa) is a typical serological biomarker glycoprotein with high specificity and sensitivity for the diagnosis and the treatment monitoring of the primary hepatocellular carcinoma (HCC). HCC is the sixth most common malignant tumor, and its mortality is ranked in the fourth place over the world. The earlier the tumor is detected, the greater the chance of a cure and the better the prognosis for patients. Currently popular approaches for the detection of the AFP include the radioimmunoassay, the enzyme‐linked immunosorbent assay, the electro‐chemiluminescence immunoassay, the nanoparticle magnetic chemiluminescent immunoassay, etc. These methods can achieve the detection of the AFP at the level of ng mL−1 to fg mL−1, and relevant kits have been applied in the clinical medicine. However, these methods require the large and expensive equipment and will also introduce the radioactivity as well as the labeling of the fluorescence, the enzyme and the nanoparticle. These could lead to high costs and degradations in activities of antibodies.The approaches for the tumor screening and diagnosis are mainly categorized into the imageological examination and the detection of serological tumor markers. The imageological examination methods such as the ultrasonic diagnosis1 can  The performance improvement approaches for the optical fiber plasmonic resonance based on multilayer sensing structures and various coating materials  have been widely reported. Aiming at the complexity of designing sensing structures and the diversity of selecting coating materials, a generic principle is required to guide the development of optical fiber plasmonic resonance sensors to achieve the optimization of spectral characteristics. Optical fiber plasmonic resonance sensors can realize the detection of multiple types of protein biomarkers by immobilizing corresponding types of antibodies on surfaces of the sensors via antibody coupling agents. The biocompatibility of the antibody coupling agent is the medium for converting the high sensitivity of the sensor into the spectral signal representing the microscopic coupling between protein molecules. Therefore, the optimization of types of antibody coupling agents and coupling conditions needs to be explored. The optimized configuration of spectral characteristics and the antibody coupling is aimed at improving the biosensing performance of the optical fiber plasmonic resonance sensor to realize the detection of trace biomarkers such as AFP.Optical fiber plasmonic resonance technologies have advantages of high sensitivity, compact size, low cost, good flexibility, and simple platform, and can achieve the remote, on‐line, real‐time, label‐free, and multiparameter detection.8, 92@Au) nanostars (AuNSs) layer/Au layer is developed for the detection of the AFP. The high carrier mobility of the WSe2 layer and the electronic coupling arising from the tip hot spot in the nanogap between the Au nanostar and the Au layer synergistically realize the near‐field enhancement. This thereby improves the refractive index (RI) sensitivity of the immunoprobe. The designed sensing structure of fiber core/intermediate dielectric layer/metal layer confines the resonance dip within a short wavelength band and narrows the full width at half‐maximum (FWHM). This allows a larger amount of dopamine with better biocompatibility to be modified and this thereby further improves the biosensing performance of the immunoprobe. This work is focused on (1) generic principles for the optimized configuration of spectral characteristics of the optical fiber plasmonic resonance and (2) the biosensing performance of the immunoprobe. The developed immunoprobe exhibits a better performance and an easier operation in the detection of the AFP compared to reported works based on the fiber‐optic plasmon resonance.Based on above discussions, generic principles based on dispersion models and finite element analysis (FEA) models are proposed to provide theoretical guidance for the design of multilayer sensing structures and the selection of coating materials, respectively. According to the guidance, a near‐field electronic transfer and coupling enhanced optical fiber plasmonic immunoprobe with a sensing structure of fiber core/WSe2 doped with AuNSs . This is hereafter referred to as the optical fiber NFE‐PR immunoprobe. In order to further enhance the sensitivity of the immunoprobe without significantly widening the FWHM, a small proportion of metal nanoparticles can be doped into the intermediate dielectric layer. The near‐field electronic coupling15 in 2.1.2FEA models have been implemented to investigate the influence of electromagnetic properties of coating materials on spectral characteristics of the optical fiber plasmonic resonance, including the resonance wavelength, the FWHM, the sensitivity, and the figure of merit (FOM). Detailed discussions can be found in Section 16The FOM of the Ag layer‐based fiber plasmonic resonance is the highest due to the DCR of the Ag layer being farthest from 1, as shown in Figure 2‐modified fiber plasmonic resonance is the highest due to the largest DCR of the WS2 layer, as shown in Figure 2‐modified fiber plasmonic resonance is the highest in the RI range of 1.33–1.34 corresponding to that of the biobuffer‐prepared protein sample with a concentration on the order of nanogram per milliliter, as shown by enlarged columns in Figure 2‐modified fiber plasmonic resonance is similar to that of the WS2‐modified fiber plasmonic resonance. In addition, the carrier concentration and the carrier mobility of the WSe2 layer are 1–2 orders of magnitude higher than those of other transition metal dichalcogenide (TMDC) layers. Compared to other types of metal nanoparticles, AuNSs possess good stability and can produce more significant electric‐field enhancement due to the tip hot spot effect. Therefore, the composite layer of WSe2 and AuNSs (WSe2@AuNSs layer) is more suitable as the dielectric layer in the development of the immunoprobe. The selection of coating materials and the tip hot spot effect are shown in Figure In the wide RI range of 1.33–1.38, the FOM of the WS) layers.14 Com2.2Based on above principles for guiding the design of multi‐layer sensing structures and the selection of coating materials, optical fiber NFE‐PR immunoprobes are practically fabricated for the detection of the AFP.2.2.1Figure Figure  This thereby further improves the limit of detection (LOD). The LOD of the immunoprobe aims to meet the requirement of the low‐cost early screening of tumors. It is promising to achieve an ultralow LOD by using a low dimensional nanomaterial‐labeled secondary antibody as a signal enhancer. This will also increase the difficulty and the cost in the development of the sensor. Therefore, the secondary antibody ) in our work is unlabeled, and it will not significantly increase the complexity of the detection due to the plug‐and‐play probe structure.The detection of the AFP via the immunoprobe is based on the double‐antibody sandwich immunoassay, and the proposed scheme is shown in e Figure 19 Thi−1, and the initial resonance wavelength is immediately recorded −1. The LOD of the immunoprobe is calculated by Equation f(x) is the calibration function. y¯blank is the mean value of the resonance wavelength redshift obtained by detecting the blank sample  and 0.0549 ng mL−1 (0.7843 × 10−12m), respectively.The calibration for the optical fiber NFE‐PR immunoprobe can be achieved via the detection of a finite number of PBS samples. Both the Logistic model22 and−1. The demodulated redshift of the resonance wavelength is 0.0358 nm, as shown in Figure In order to investigate the influence of the nonspecific deposition of the antigen on above detection results, a fully bovine serum albumin (BSA)‐passivated immunoprobe has been fabricated to detect the AFP sample. After the self‐polymerization of the dopamine onto the surface of the immunoprobe, the surface of the immunoprobe is directly blocked by the BSA and unmodified with the antibody. In order to observe a more obvious redshift signal, the concentration of the AFP sample is 80 ng mLTable −1. The LOD of the immunoprobe is two orders of magnitude lower than the upper limit. Such a low LOD can avoid the risk of reducing the detection accuracy due to the temperature fluctuation, the intrabatch difference of the immunoprobe, etc. Additionally, the low‐cost and mass‐fabricated probe structure realizes the one‐off plug and play. The comparison demonstrates that the developed immunoprobe combines merits of the low LOD, the label‐free, and the easy operation.Previously reported works on the detection of the AFP using fiber plasmonic resonance technologies associated with our developed method have been listed in 2.2.2−1, and experimental results are shown in Figure The repeatability of the plasmonic resonance biosensor is measured via the consistency of the results obtained from detecting the same target sample. The good repeatability represents the low cost of the biosensor and the high reliability of detection results. Five immunoprobes, fabricated from the same batch, with similar performance are used to detect the same AFP sample of 10 ng mL), the human PSA (a biomarker of the prostate cancer), the human cardiac troponin I‐C , and the human c‐reactive protein  are selected as controls. The PBS sample is used as the blank control, and the control sample of the mixture including the AFP, the IgG, the PSA, the cTnI‐C, and the CRP is employed to emulate complex components in the human serum. Seven immunoprobes, fabricated in the same batch, with similar performance are used to detect the target sample and control samples, and experimental results are shown in Figure −1), and the redshifts corresponding to the samples without AFP are close to zero. This indicates that the control biomarkers will not have a significant interference on the detection of the AFP and the immunoprobes have good specificity.The specificity of the plasmonic resonance biosensor is measured by detecting the target sample and control samples. This aims to investigate the interference degree of control samples to the detection of the target sample. The good specificity represents the high precision of detection results. The topic of our work is focused on the detection of biomarkers for the early screening of human diseases. Therefore, the human immunoglobulin G , tBased on the good specificity of the immunoprobe, the detection of other types of high‐specific biomarkers can be achieved by modifying corresponding types of antibodies on the immunoprobe. This thereby will provide auxiliary approaches for the early screening of other types of tumors.2.2.3−1, and these levels can be elevated to more than 400 ng mL−1 in patients with HCC. The calibration curves of the fiber immunoprobe are obtained in the concentration range of 0.1–100 ng mL−1 only. The detection of the serum samples with higher concentrations of the AFP can be achieved by diluting the samples with the normal saline. The high specificity of the fiber immunoprobe ensures the applicability of the calibration curves in the detection of serum samples due to the marginal influence of complex components in the human serum. The serum from healthy individuals with an original AFP concentration of 3.74 ng mL−1 has been collected from NovoBiotechnology Co., Ltd. Beijing, China. Five serum samples with concentrations ranging from ≈2 to 40 ng mL−1 are prepared by diluting the serum or spiking AFP into the serum. The developed optical fiber NFE‐PR immunoprobes in this work are used to detect the serum samples, and the redshifts of the resonance wavelengths are shown in Figure −1, purchased from Autobio Diagnostics Co., Ltd. Zhengzhou, China, has also been used to detect the serum samples. The linear regression analyses  and 0.05 ng mL−1 (Langmuir fitting). Results demonstrate a better LOD level compared to reported similar studies and achieve good precision in prepared clinical samples. This work provides novel and insightful visions on the application of the plasmonic resonance optics method in the field of early screening and early diagnosis of tumors.This work presents the design and the development of an optimized tool, i.e., an optical fiber near‐field enhanced plasmonic resonance immunoprobe, for the detection of the AFP which is a biomarker for the hepatocellular carcinoma. According to the dispersion models and the FEA models, the optimally configured spectral characteristics of the immunoprobe arise from (1) the near‐field electronic transfer and coupling in coating materials, (2) the better biosensing performance of the thicker polydopamine (PDA) layer, and (3) the accurate demodulation of spectral signals. The LODs of the immunoprobe can achieve down to 0.01 ng mL4N‐(3‐dimethylaminopropyl)‐N′‐ethylcarbodiimide hydrochloride (EDC), and N‐hydroxysuccinimide (NHS) were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. Shanghai, China. Hydroxylated and carboxylated WSe2 nanosheet dispersion  and AuNSs dispersion  were purchased from Nanjing MKNANO Tech. Co., Ltd. Nanjing, China (www.mukenano.com). PBS , BSA, human AFP, polyclonal rabbit anti‐AFP (serving as the CAb), monoclonal mouse anti‐AFP (serving as the DAb), human IgG, human PSA, human cTnI‐C, and human CRP were purchased from Wuhan Huamei Biotech Co., Ltd. Wuhan, China.The multimode fiber with a silica core of 600 µm, a plastic cladding of 630 µm, and a numerical aperture of 0.37 was purchased from Beijing Scitlion Technology Co., Ltd. Beijing, China. Dopamine hydrochloride (98% wt%), tris (hydroxymethyl) aminomethane (99% wt%), (1)2@AuNSs  with a thickness of ≈10 nm was coated on the surface of the sensing region by the electrostatic self‐assembly, as seen in Figure Coating of the dielectric layer and the metal layer—The end face of an optical fiber with a length of 10 cm was ground into a flat, and a part of the cladding of the optical fiber was removed, as seen in Figure assembly,14 as (2)2@AuNSs layer—The transmission electron microscope (TEM) image and the Raman spectrum of the composite layer are shown in Figure Characterization analyses of the WSe(3)−1, pH = 8.5) for 120 min, which was carried out in a thermostat steam bath vibrator. Under the alkaline condition, a PDA layer with active functional groups of the catechol quinone can be formed by the self‐polymerization of the dopamine. The PDA layer with strong hydrolysis resistance adhered tightly to the surface of the Au layer through complex physicochemical effects, e.g., hydrogen bonding, chelation, π–π interaction, and covalent bonding, as seen in Figure −1 for 24 h at 4 °C. Quinones can be covalently coupled with amino‐terminated CAbs by the Schiff base or the Michael addition reaction, as seen in Figure −1 to block nonspecific binding sites, as seen in Figure Immobilization of the antibody—The sensing region of the fiber probe was immersed in a Tris‐prepared dopamine solution (2 mg mL bonding,35 as  as shown in Figure  and denoised by the wavelet packet decomposition, as shown in Figure The experimental setup is shown in Figure 37 as The authors declare no conflict of interest.Supporting InformationClick here for additional data file.


A planar stick balancing task was investigated using stabilometry parameters (SP); a concept initially developed to assess the stability of human postural sway. Two subject groups were investigated: 6 subjects (MD) with many days of balancing a 90 cm stick on a linear track and 25 subjects (OD) with only one day of balancing experience. The underlying mechanical model is a pendulum-cart system. Two control force models were investigated by means of numerical simulations: (1) delayed state feedback (DSF); and (2) delay-compensating predictor feedback (PF). Both models require an internal model and are subject to certainty thresholds with delayed switching. Measured and simulated time histories were compared quantitatively using a cost function in terms of some essential SPs for all subjects. Minimization of the cost function showed that the control strategy of both OD and MD subjects can better be described by DSF. The control mechanism for the MD subjects was superior in two aspects: (1) they devoted less energy to controlling the cart’s position; and (2) their perception threshold for the stick’s angular velocity was found to be smaller. Findings support the concept that when sufficient sensory information is readily available, a delay-compensating PF strategy is not necessary. Neuroscientists often take for granted the hypothesis that the nervous system uses an internal model to predict the sensory consequences of a movement Jordan  InternalStick balancing is a voluntary motor skill in which theoretical predictions for the stabilization of an inverted pendulum can be evaluated experimentally  controllers feed back information about the state as determined by its position, velocity and acceleration measured at time What are the factors that determine whether the nervous system uses a PF strategy or DSF to control a motor task? A simple hypothesis is that the nervous system uses PF to control a motor task whenever the relevant sensory information cannot adequately be measured. Here we examine the hypothesis in the context of human stick balancing , namely feedback that depends on the displacement angle and its rate of change (Stepan predictor feedback (PF), which includes a perfect internal model that incorporates a time delay y Krstic . We detey Krstic .Fig. 1a Our results demonstrate that humans use DSF to control stick balancing in this task. Taken together the feedback identifications for stick balancing at the fingertip and on a linear track suggest that a PF strategy may be required in situations in which a significant amount of sensory information is unavailable.The outline of the paper is as follows. Section Figure mentclass2pt{minimmentclass2pt{minimx and F(t) is the control force exerted by the human subject and g is the gravitational acceleration. In this study, the length and the mass of the stick were The equation of motion was derived using the generalized coordinates y et al. . This dey et al. . Using ay et al. , we obtaF(t) is zero, then the state variables can be separated and the motion of the stick is governed purely byIf the control force In order to keep the stick in the vertical position, active feedback control is required. It is assumed that the control action is determined by the linear combination of the state variables Delayed state feedback (DSF);Predictor feedback (PF).In case of DSF, the delayed state variables are directly fed back and the control force readsA key feature of human balancing is the delay in the feedback due to the fact that for the nervous system it takes finite time to process sensory information and initiate action. However, there is evidence that humans are capable of making a prediction by integrating motor efference copies using an internal model in order to compensate this delay  subjects. Six subjects exercised regularly for 15 weeks (at least 15 min/week) with the experimental setup before carrying out the measurements, which gave 5 h of accumulated balancing time for each subject. These subjects are referred to as many-day (MD) subjects. All subjects were free from neurological disease or upper limb injuries during the study, and did not take any medication which could affect motor control. Upon arrival to the laboratory, the subjects were informed about the experimental setup and protocol. The research was carried out following the principles of the Declaration of Helsinki and subjects were allowed to withdraw from the study at any time. The measurement lasted for an average of 15 min and subjects were allowed to rest if needed.Thirty-one healthy individuals aged from 19 to 42 participated voluntarily in the study. Twenty-five subjects had no experience with this balancing task: they performed the balancing trials on the same day they first met the balancing device. These subjects are called The measurement setup is shown in Fig. ® motion capturing system. The sampling frequency was set to 120 Hz. Two markers were attached to the stick, one close to the bottom and one at 40 cm distance from the bottom of the stick and, additionally, one marker was attached to the cart. Figure x and The motion of the stick was captured using an OptiTrackx and Figure x, altogether The high dimensionality and the sensitive dependence of the dynamics on the choice of initial condition make it difficult to estimate model parameters by comparing time series generated by the model to those observed experimentally. Here we assume that the model for pole balancing is acceptable if it reproduces the stabilometry properties shown in Table J was defined, which compares the SPs for the simulated and measured time signals. The minimum of the cost function is determined in the 7 dimensional parameter space N is the number of SPs selected for the parameter identification and Parameter identification was performed for all the 31 subjects for the DSF and PF feedback models. The SPs were used to compare the models and the measurements in order to identify the model parameters, which give time histories best matching the recorded balancing trials. For the parameter identification, a cost function The weights applied in the cost function were determined based on the measurements as follows. Each SP was obtained for each of the 31 subjects individually, and the mean and standard deviation of each SP over the 31 subjects were evaluated. The coefficient of variation  and from simulations with the identified parameters (red) are shown in Fig. J in Eq.  and (d) of Figs. The variation of the cost function J is minimal are shown in Table The average of the identified parameters J. However, there is significant difference for ANOVA detects no significant differences between parameters identified for MD and OD subjects in terms of In order to validate the results, the same parameter identification method was performed for the DSF and PF models using time-domain simulations with the mean OD parameters in Table As an additional validation, the stabilometry parameters were determined for time domain simulations generated by employing the identified parameters for all subjects. The resulted SPs are shown in Fig. The difference between the control gains of OD and MD subjects can be visualized in stability diagrams shown in Fig. Stick balancing on a linear track was investigated via stabilometry parameters, where subjects with many-day (MD) and one-day (OD) experience were asked to carry out the balancing task. Standard deviations of stick angle and cart positions were smaller for MD than for OD subjects. The frequency power was also smaller for MDs which is related to the fact that MD subjects can perform the balancing on a lower energy level, i.e., more effortlessly  were investigated via numerical brute force method, and stabilometry parameters of the resulting time histories were analyzed. Both control force models apply a proportional-derivative (PD) controller with respect to the stick and the cart, which is switched on and off based on threshold crossing conditions. The domain of stable control gains for the stick’s position and velocity shrinks with increasing gains for the cart position and velocity.The control parameters  term in .For the DSF model it was found that MD subjects have significantly smaller certainty threshold, i.e., when the sensory variable is above this threshold, the nervous system must certainly make a control action.A surprising observation is that balancing a stick on a linear track reduces Track balancing in the ML direction can be linked to virtual balancing tasks, where subjects control the movement of an object represented on a computer screen inherently in the ML direction (Loram et al. It shall be mentioned that several other types of control concepts exist in the literature to capture the dynamics of human stick balancing. The intermittent control developed for human quiet standing in Asai et al.  was adopTaken together, our results suggest that when important sensory information is missing the only recourse for controlling balance is to predict the sensory consequences of the movement. However, this is not the case in track balancing, where the sensory dead zones for the stick’s position are much smaller than for stick balancing at the fingertip. Our studies show that these dead zones are small enough so that track balancing of a long enough stick (


Organic–inorganic hybrid perovskite (OIHP) polycrystalline thin films are attractive due to their outstanding photoelectronic properties. The anti‐solvent spin coating method is the most widely used to synthesize these thin films, and the residual strain is inevitably originates and evolves during the process. However, this residual strain evolution induced by crystallization kinetics is still poorly understood. In this work, the in situ and ex situ synchrotron grazing‐incidence wide‐angle X‐ray scattering (GIWAXS) are utilized to characterize the evolution and distribution of the residual strain in the OIHP polycrystalline thin film during the anti‐solvent spin coating process. A mechanical model is established and the mechanism of the crystallization kinetics‐induced residual strain evolution process is discussed. This work reveals a comprehensive understanding of the residual strain evolution during the anti‐solvent spin coating process in the OIHP polycrystalline thin films and provides important guidelines for the residual strain‐related strain engineering, morphology control, and performance enhancement. The residual strain evolution during anti‐solvent spin coating of organic–inorganic hybrid perovskite thin‐film significantly affects its morphology and performance but is poorly understood. Based on the in situ and ex situ synchrotron grazing‐incidence wide‐angle X‐ray scattering, this work combines crystallization kinetics and thin‐film mechanics to establish a model and explore the mechanism underlying the residual strain distribution and evolution. In the one‐step spin coating methods, the anti‐solvent spin coating method is most widely used, owing to the high crystallinity in the OIHP polycrystalline thin film and the great coverage percentage on the substrate. In this method, the OIHP undergoes a series of procedures driven by crystallization kinetics to completely transform into a polycrystalline thin film, which inevitably possesses the residual strain inside itself.Organic–inorganic hybrid perovskite (OIHP) polycrystalline thin films, with their outstanding electronic and photoelectronic properties, are hot candidate materials for various applications, such as solar cells, light emitting diodes, gas/light detectors and artificial synapses, etc.5, 6 On the one hand, a moderate compressive strain can increase its photoelectronic properties, stability and flexibility. On the other hand, an excessive tensile or compressive strain could lead to the consequential wrinkle, crack or fracture of the film. Currently, the residual strain can be intentionally regulated via the well‐designed epitaxial growth or atom substitution processes. Besides, it can also be naturally generated as a by‐product during the solidification and thermal treatment processes of the thin film. To date, many researchers have focused on the residual strain generated by epitaxial growth, atom substitution, and thermal treatment in the OIHP polycrystalline thin films and discussed the related residual strain evolution processes and strain engineering strategies. However, the residual strain evolution inside the film during the crystallization process in the anti‐solvent spin coating method is still poorly understood. Practically, the crystallization process can generate a significant residual strain, and this residual strain could be the starting point for its evolution in the following thermal treatment. Furthermore, the residual strain evolution mechanism in the OIHP polycrystalline thin films fabricated by the anti‐solvent spin coating method could be different from that in the traditional chemical vapor deposition (CVD) or electroplating method. Current studies have shown that the solidification or crystallization of the OIHP polycrystalline thin films in the anti‐solvent spin coating method is based on the removal of the precursor solvent(s) and initiate crystallization of the perovskite film. As a result, rather than the traditional “bottom‐up” growth mode of the crystals from the precursor‐substrate interface to the surface in the CVD or electroplating method, the OIHP polycrystalline thin films via the anti‐solvent spin coating method tend to nucleate at the precursor‐gas interface and grow “downward” to the substrate (“top‐down” growth mode), where the mechanism of the residual strain evolution should be completely different from that in the “bottom‐up” growth mode. In addition, the process of the anti‐solvent spin coating method is very fast and usually lasts only tens of seconds, and the fabricated thin film is about several hundreds of nanometers. The time‐sensitive feature and the thin film structure make it challengeable to comprehensively and thoroughly investigate the residual strain evolution mechanism in the anti‐solvent spin coating process. So, it is necessary to employ experimental characterization techniques with high spatial and temporal resolutions to facilitate the related researches.The residual strain inside the OIHP polycrystalline thin film has advantages and disadvantages.16 On  to characterize the residual strain evolution and distribution during and after the anti‐solvent spin coating process. The ex situ GIWAXS results show that the residual strain exhibits different distributions inside the OIHP polycrystalline thin films with different fabrication conditions. The in situ GIWAXS results collected during the anti‐solvent spin coating process show that the crystallization speed plays an important role in affecting the residual strain and film morphology. A mechanical model is established based on the experimental results to explore the influence of crystallization speed on the residual strain evolution and the film morphology. Furthermore, based on crystallization kinetics, the mechanism of the residual strain evolution during the anti‐solvent spin coating process is discussed. This work reveals a deeper and more comprehensive understanding of the residual strain evolution induced by crystallization kinetics during the anti‐solvent spin coating process of the OIHP polycrystalline thin films and provides important guidelines for the residual strain‐related strain engineering, morphology control and photo‐electronic performance improvement of the thin films.In this work, we utilized the ex situ and in situ synchrotron grazing‐incidence wide‐angle X‐ray scattering (GIWAXS)43 to 22.13NH3PbBr3 (MAPbBr3) polycrystalline thin film as the typical material was selected in this work. The MAPbBr3 film can fully crystallize during the anti‐solvent spin coating process and maintain the same morphology and optoelectronic properties without any further annealing process. This feature can remove the influence of the thermal treatment on the residual stain and ensure that the difference in the residual strain only results from the variety in the crystallization process.To study the residual strain distribution and evolution during the anti‐solvent spin coating process, the CH process.9 This3 polycrystalline thin film is illustrated in Figure2 or other secondary product in the final wrinkled MAPbBr3 thin film.The main steps of the anti‐solvent spin coating method to fabricate the MAPbBr° and 2 ° were chosen to ascertain the crystal structures of the surface and interior of the thin film, respectively. The corresponding 2D GIWAXS patterns are shown in Figure  These 2D GIWAXS patterns show that both the flat film and wrinkled film exhibit the same MAPbBr3 cubic structure with a slight difference in the crystal orientation uniformity. The average grain size of the flat film is larger than that of the wrinkled film, as shown in Figure τave) were ≈15 and 29 ns for the wrinkled film and flat film, respectively.The ex situ synchrotron GIWAXS was used to examine the difference in phase or crystal structure of the films with different morphologies. Two incident angles of 0.1 n Figure 45 TheFigureTo explore the detailed residual strain distributions in the thin films with different morphologies, more ex situ GIWAXS characterizations were performed. Three kinds of film morphologies including flat one, flat‐wrinkled transition one and wrinkled one are selected. To quantitatively explain the relationship between the anti‐solvent dripping time and the film morphology, we fixed other factors, like the constitution of the precursor solution and the spin coating speed. The precursor solution consisted of N,N‐Dimethylformamide and Dimethyl sulfoxide (DMSO) with a volume ratio of 3:1, and the spin coating rate kept as 3000 rpm. In this scenario, the relationship between the anti‐solvent dripping time and the film morphology can be experimentally obtained and shown in Figure 3 crystalline peak to the positive q vector axis, no matter the direction of the orientation integral is in‐plane or out‐of‐plane. However, in Figure As shown in Figure In the in‐plane direction, except for the flat film, the transition film and wrinkled film has double or even triple peaks. The multi‐peaks indicate that the corresponding lattices are deformed with different strain states. In the flat film, the stress level is relatively low to generate a uniform strain, while in transition and wrinkled films, the stress levels could be higher to introduce different strain states. This split of the strain state also varies along the thickness direction. For the transition film, the multi‐peak is more obvious when closer to the surface. This is because the film‐substrate interface is still constrained by the substrate and maintains a relatively uniform strain state, which presents as a single peak in the 1D integral patterns. Nevertheless, the film surface is constrained less and the corresponding lattices have more deformation freedom, resulting in a more diverse strain states and multi‐peaks. On the other hand, for the wrinkled film, the multi‐peaks appear in all the film regions from the film‐substrate interface to the surface. This is because in the buckled area, both the top and the bottom of the film are free and with less constrains, leading to the split of the strain state across the entire thickness of the film.q value. Therefore, with the increasing incident angle, the compressive strain will become higher, and the peak will shift to the right, which is consistent with the observation in Figure Besides, the locations of the multi‐peaks also can be used to compare the strain magnitudes. Generally, in either the flat or transition film, the film‐substrate interface is constrained by the substrate to possess the highest compressive strain, while the film surface is free and in the lowest compressive strain state. The higher the compressive strain, the less the lattice constant, while the higher the Because of the difference in grain size, the peak broadening is more apparent in the wrinkled film than that in the flat one. The average grain size of the wrinkled film is 239.3 ± 75.7 nm, while the average grain size of the flat film is 341.2 ± 140.5 nm as shown in Figure q values of the MAPbBr3 crystalline peak, the lattice constants of the different films were equal to 2π/q and illustrated in Figure q values are corrected by considering the changing of incident angles  and the MAPbBr3 powder (5.92 Å) and theoretically estimated lattice constant via the Density Functional Theory simulation (5.96 Å). This comparison indicates that the residual strains in all the different thin films are compressive. Generally, for all the different films, the surface possesses a larger lattice constant than that of the interior, and the lattice constant in the out‐of‐plane direction is much larger than that in the in‐plane direction. Therefore, the residual compressive strain rises when the depth from the surface increases, meanwhile the crystal lattice in the in‐plane direction has a higher residual compressive strain when compared with that of in the out‐of‐plane direction. In addition, if the increase of the incident angle in the wrinkled part is taken into consideration, the X‐ray could penetrate more than the depth supposed. Since the deeper the material layer, the higher the compressive strain, the compressive strains near the film surface are overestimated, and the difference between the compressive strains of the top and the bottom parts is underestimated, which can strengthen the above conclusion.According to the nt angles49  concentration in the anti‐solvent, were also considered as the factors to change the residual strain in the thin film. Figures 2.2 This process can either naturally proceed in any open system or be accelerated by an additional anti‐solvent. The anti‐solvent can dramatically extract the precursor solvent and make the precursor solution saturated or supersaturated in a short time. On the other hand, before the adding of the anti‐solvent, the precursor solution can also evaporate continuously, once it is dripped on the substrate. Therefore, the later anti‐solvent dripping time, the higher solute concentration of the precursor, when it encounters with the anti‐solvent. Different times of the anti‐solvent dripping result in different solute concentrations of the precursor to kick off different crystallization processes. The other two aforementioned parameters, the CB concentration and amount of the anti‐solvent can tune the acceleration effect of the anti‐solvent, leading to different crystallization processes as well. Hence, all three parameters can influence the crystallization process of the thin film and further result in different residual strains and film morphologies. Thus, it is rationally deduced that the crystallization process affected by the process parameters plays a significant in the distribution and evolution of the residual strain during the thin film formation.In general, the crystallization of the OIHP polycrystalline thin film is triggered by the evaporation of the precursor solvent, which can increase the concentration of the solute until to a saturated or supersaturated state.53 Thiq‐time plane are for thin film samples with different anti‐solvent dripping times are displayed in FigureTo investigate the role of the crystallization kinetics in the residual strain evolution, 2D GIWAXS patterns were acquired 0.2 s per frame to observe of the real‐time crystallization process of the OIHP polycrystalline thin film during the anti‐solvent spin coating  appearing. In this stage, the broad scattering intensity decreases at first, due to the sudden dilution of the precursor solution by the anti‐solvent dripping. Then, the broad scattering intensity resumes immediately and a noticeable signal (red color) appears at around 10 nm−1, which indicates the formation of the OIHP crystals. The final stage is from the appearance of the OIHP crystal peak to the time when it reaches the highest intensity, and at this time, the film is fully dried and crystallized.For a thorough analysis, the entire fabrication process can be divided into three stages. The first stage is from the start time of the spin coating to the dripping time of the anti‐solvent. In this stage, both samples exhibit a broad scattering intensity (green color), which could originate from the colloids established in the precursor solution. The second stage starts with the anti‐solvent dripping and ends with the MAPbBrnd s  was dripped at the 22χ. The area under each curve is integrated to obtain the MQ. These areas can be analyzed over the entire spin coating process from the in situ GIWAXS data , the azimuthal tube cut was performed, and the intensity is Lorentz‐corrected by a factor of sin n the MQ.56 Thea Figure . The AvrAs a summary, the anti‐solvent dripping time and the CB concentration in the anti‐solvent can influence the crystallization process of the OIHP polycrystalline thin film in two ways: 1) changing the time of the second stage, which is the induction period of the crystal nucleation; 2) changing the time of the third stage, where the intensity of the OIHP crystal peak rises from its appearance to its maximum value. These stages are both highly related to the crystallization kinetics in the form of the nucleation rate and crystal growth rate, which can influence the conversion percentage of the crystals in the thin film. Thus, we can define the crystallization speed as the changing rate of the conversion percentage of the crystals in the solution system. Therefore, the later dripping the anti‐solvent and the increase of the CB concentration in the anti‐solvent can both boost the crystallization speed, which enhances the residual compressive strain inside the thin film and eventually leads to the wrinkled morphology of the thin film.2.3 and crystallization kinetics. FigureTo ascertain the relationships among the residual strain, crystallization speed and thin film morphology, we establish a mechanical model based on thin‐film mechanics60 and was adopted to establish the mechanical model to analyze the crystallization speed‐dependent residual strain evolution. The detailed calculation of the total energy is shown in note αc, the two energy curves intersect as depicted in Figure αc, the wrinkled morphology has lower total energy, and the equilibrated film should be wrinkled. Therefore, driven by the minimum process of the total energy, the film morphology changes from the flat one to the flat‐wrinkled transition one and finally to the wrinkled one, with an increasing crystallization speed.In this work, the energy minimization method60 wasεr) during the morphology changing can be analyzed as well. Since the Ustrain is proportional to the εr2  at the flat‐wrinkled transition point, the εr reaches its maximum values; 3) when the α is larger than the critical value, the film becomes wrinkled, and the εr is released to maintain a relatively low level. This calculated trend is highly consistent with the ex situ GIWAXS results in Figure Based on this mechanical model, the evolution of the residual compressive strain  to the smaller values, which means that the morphology transits from the flat one to the obviously wrinkled one. Four representative points in Figure 1–x4) to present the corresponding film morphologies with different parameter combinations  in the crystallized surface layer are the main sites to absorb extra free atoms from the precursor solution, and the number of the absorbed free atoms can be influenced by the crystallization speed, further resulting in different residual compressive strain levels. Four steps of the crystal growth and GB formation in the OIHP polycrystalline thin film are depicted in the middle part of Figure 65 WitBased on the above experimental results and mechanism analysis via combining thin film mechanics and crystallization kinetics, we revealed the formation and evolution of the compressive strain in the OIHP polycrystalline thin films during crystallization and suggested that the crystallization speed of the film could be the key influencing factor. In practical, the residual strain inside the OIHP polycrystalline thin film can considerably influence the energy conversion efficiency and stability of the device. An appropriate compressive strain can improve the energy conversion efficiency and stability of the thin film perovskite solar cell. However, an excessive compressive strain can make the film wrinkled or even fractured, which will significantly deteriorate the device performance. Base on this work, researchers could introduce a suitable compressive strain level via tuning the crystallization speed to enhance the device performance but without any unfavorable morphology, for examples, wrinkles or cracks. Therefore, leveraging on this study, the researchers could have gained a practical approach to optimize the residual strain in their films effectively and efficiently and further important the performance of their device.4In summary, we investigate the residual strain distribution and evolution of the OIHP polycrystalline thin films during the anti‐solvent spin coating process and reveal that the crystallization speed is an important factor to influence the residual strain inside the film and the film morphology. Based on the GIWAXS experiments and the mechanical model, we illustrate the relationship between the crystallization speed and the residual strain evolution, which can determine the final film morphology as well. With the increase of the crystallization speed, the residual compressive strain inside the OIHP polycrystalline thin film continuously increases and forms a residual strain gradient from the film surface to the film interior. There is a critical point of the crystallization speed, where the residual compressive strain reaches the maximum value, and the film morphology changes from the flat one to the wrinkled one. From the perspective of crystallization kinetics, in the “downward” process triggered by the anti‐solvent dripping, the crystallization speed can influence the number of the excessive absorbed free atoms from the precursor solution by the crystallized surface layer, further resulting in different residual compressive strain levels. The GBs in the crystallized surface layer are the main absorption sites of the free atoms. Normally, the faster the crystallization process, the more free atoms inserted into the GBs, and the higher the residual compressive strain in the OIHP polycrystalline thin film. Based on advanced characterization techniques, the mechanical model and crystallization kinetics, this work reveals a deeper and more comprehensive understanding of the residual strain evolution during the anti‐solvent spin coating process in the OIHP polycrystalline thin films and provides important guidelines for the residual strain‐related strain engineering, morphology control and photo‐electronic performance enhancement of the thin films.The authors declare no conflict of interest.Supporting InformationClick here for additional data file.


G is called C-quasirandom (by Gowers) if all non-trivial irreducible complex representations of G have dimension at least C. For any unit quantum probability measure on the group given by the absolute value squared of the function. We show that if a group is highly quasirandom, in the above sense, then any Cayley graph of this group has an orthonormal eigenbasis of the adjacency operator such that the quantum probability measures of the eigenfunctions put close to the correct proportion of their mass on suitably selected subsets of the group that are not too small.A finite group  The main question of quantum chaos is to what extent ‘chaotic’ features of the geodesic flow on a manifold  manifest themselves in the corresponding quantized system; that is, the graphs instead of manifolds and prove results in the spirit of quantum unique ergodicity for certain families d-regular graphs, with x, y) if there is an edge between x and y, and 0 otherwise; we view this as an operator on In this paper,  norms will be defined with respect to the counting measure.In this paper, we work with quantum probability measurev. Note that Given an element mentclass2pt{minimquantum unique ergodicity (QUE) holds for a sequence of adjacency operator eigenfunctions We will say G is a finite group and S is a symmetric subset of G, then we will denote the Cayley graph associated to the pair  by G, and defineG. Then in the language of Gowers from [G is quasirandom.Suppose that ers from , G is \dentclass1pt{minimaLet The proof of Theorem The condition  involvinbound in , and theThe most simple case of this is as follows. For groups with ement in  while taG is a finite group satisfying Let The proof of Theorem If G|, we can also obtain a quantum unique ergodicity result for partitions of the group into sets whose sizes are on scales of the order When G be a finite group, G with sizes satisfying G| sufficiently large  using eproof of , SZ93 threfer to , §5.2 foLet us now discuss the strength of the upper bound obtained in Theorems  side of  or 1.6)\document and Yau  obtainedd-regular graph on n vertices. Suppose Bauerschmidt–Huang–Yau , Cor. 13The first result about equidistribution of quantum probability measures of eigenfunctionsd-regular, d-regular tree in the sense of Benjamini and Schramm [, Thm. 1. Schramm . Let \doFor related results of quantum ergodicity on quantum graphs, see for example , AISW21.Because the type of results of the current paper draw their inspiration from analogous questions about manifolds, we include a brief discussion of the state of the art results in that setting.M be a closed and connected Riemannian manifold and let quantum unique ergodicity conjecture of Rudnick and Sarnak [M is negatively curved, then the quantum probability measures of the eigenfunctions weak-Let d Sarnak . This saf Sarnak . For manf Sarnak  for certf Sarnak .Despite counterexamples demonstrating that ergodicity alone is insufficient for quantum unique ergodicity, there is numerical evidence to support the conjecture in the presence of negative curvature , HR92. IFor closed Riemannian manifolds in general, ergodicity of the geodesic flow alone is sufficient to prove a weaker result known as quantum ergodicity. This result exhibits the existence of a density one subsequence of the quantum probability measures that weak- measure , Col85.  measure  it is shicity in . Similaricity in , Zel14 aicity in .The remainder of the paper proceeds as follows. In Sect. We begin by outlining basic concepts in representation theory. A more complete background can be found in .G denote a finite group. We consider unitary representations of G. These are pairs V is a finite-dimensional complex Hilbert space and V as a representation. We will denote the trivial representation of G by Let G. We identify G linearly extends to V a The group algebra V that are invariant under G by G. We will not make any distinction between an equivalence class in Recall that a representation G, the dual representation will be denoted by V equipped with the inner product arising from that of V on the corresponding Riesz representation vectors, and Given a representation Given G be a finite group and let G such that g and h if d-regular graph. The adjacency operator on Let In this section we will outline the construction of the bases of eigenfunctions for the adjacency operator. The idea is to exploit the decomposition of V consisting of eigenvectors of V. Then the collectionWe start with the case that V and then given a Haar random unitary operator To randomize this basis, we randomize the choice of the basis real and in case quaternionic [. It is not hard to check using uniqueness  of the V that for all A complex irreducible representation that is equivalent to its dual has a conjugate-linear intertwining map ernionic . It is nJ defines a real structure for V. That is, V is obtained from this one by extension of scalars from In this case, ows from  that \doJ intertwines with V.Since Fix an orthonormal basis G. In this case,  of V, and O(V) of V.Putting together all of the different cases for the type of the representation f is a real-valued function on the group G. In fact, we will further make the assumption thatf is just the Fourier component of f corresponding to the constant eigenfunction, we have In this section we will derive an upper bound forType 1—Real Part Type 2—Imaginary Part In the case of a complex irreducible representation that is real we have the following type of basis elementType 3—Real Matrix Coefficient In each of the above types, Now, recall that there were three types of functions in the eigenbasis dependent upon the type of irreducible representation that they come from. In the case of complex irreducible representations that are not real we have the following two types given by real and imaginary parts of matrix coefficientsWe will show the following.Let f is real-valued. The proof for type 2 functions is essentially the same, and the proof for type 3 is even simpler If (ii)If Suppose that Note that condition (3) in Lemma We start with (i). Notice that formula  implies The proof of (ii) follows identically but using the probability density function V is an n-dimensional complex Hermitian inner product space with orthonormal basis u is a Haar random unitary matrix in U(V). Then, For any fixed vector For any fixed vector Suppose that Proof of Part 1. We have M has zero trace and Hilbert-Schmidt norm bounded by Write Proof of Part 2. This is similar except here we let R makes no contribution to S by orthogonal (unitary) matrices. This leads to bounding The rest of the proof follows analogous lines to the proof of part 1, diagonalizing the real and imaginary parts of V is a real inner product space with Suppose that This is just the real version of Lemma G, x and y satisfy the following properties.Let x be defined as in ((i)(ii)Let ed as in  and  in Proposition G that is either complex non-self-dual or quaternionic. Then, for any Let This follows by combining the respective parts of Lemma The next bound applies to the other error terms coming from real representations.G and Let x over the basis LetWe are now ready to combine the probabilistic estimates of Propositions V is not real and in V is a real representation. In all cases, we may assume that Recall the probability space Let The proof of Theorem G, let V consisting of V. The collectionV and then given a Haar random unitary operator The proofs of Theorems The upper bound obtained in Proposition M for a basis satisfying (We use Theorem tisfying  to existG| is sufficiently large (dependent only upon c and Since the collection of subsets For at 1. By , Gam06 itisfying .In the case of


The use of intensity based cross-correlation coefficients between half datasets is compared for near atomic resolution protein structure determination and X-ray imaging of cells and tissues. The relationship between the contrast to noise ratio and intensity based cross-correlation coefficients for both protein crystallography and X-ray imaging are compared. It is concluded that, for protein crystallography at near atomic resolution, the intensity based cross-correlation coefficients give a reasonable indication of the quality of the corresponding electron density. For X-ray imaging of biological materials such as cells and soft tissue, the wide range of contrast of the features means that intensity based correlation coefficients can give a poor indication of the interpretability of an image. Rather than the term resolution, it is the contrast to noise ratio for a feature of interest at the relevant spatial frequency that is more relevant. Additional metrics are required to describe the quality of an image, and these are discussed. A threshold beyond which the data merges into the noise can then be determined and the resolution can thus be used to judge the usefulness of the data for a particular purpose. Following this, resolution estimates based on the correlation coefficient have also been adopted for X-ray imaging of cells and biological tissue. However, the range of contrast for such specimens varies much more than for protein structure determination. In these circumstances, the usefulness of a single threshold for estimating the resolution of the data is much more limited.1/2 values to obtain resolution estimates in single-particle electron microscopy and protein crystallography, respectively, is given by Dubach & Guskov  and CCskov 2020.Many papers acknowledge that any single number is insufficient to describe the interpretability of an image obtained from an instrument. Similarly, it is widely recognized that the term resolution should be regarded as a property of the instrument, defined by parameters such as the aperture of a lens, the size of a focal spot, or the size and distance of a detector for X-ray diffraction/scattering. For an image, the aim is to obtain the desired contrast to noise ratio (CNR) at the relevant spatial frequency for a feature of interest. This is a better although more complex criterion than resolution and emphasizes the visibility of a feature of known dimensions. The relationship between this and the intensity based CCC will depend on the distribution of density for the overall specimen.The questions being addressed are:(1) What CNR at a necessary spatial frequency does one need to identify a particular feature in an electron density map with a desired confidence level?(2) What is the required metric in the intensity data, corresponding to this spatial frequency, to support the interpretation at the desired confidence level?(3) What additional concise information would be useful to give an estimate of the interpretability of an image?I/σ〉 being close to 〈I〉/〈σ〉 were made. This allowed the derivation of approximate expressions relating the average signal to noise ratio for the intensities, 〈I/σ〉merged, to the correlation coefficient CC1/2 between half datasets . The procedure for a simple case is described in Section 51/2 values are obtained at a particular spatial frequency, it should be possible to estimate the CNR for features of interest. This would cover the case where strong sharp features dominate the distribution, perhaps leaving the features of interest with very poor statistics. An example could be imaging cells at higher energies where high-density features such a polyphosphate bodies and starch granules can dominate over the membrane features which might be of primary interest. Comparison of an image of a biological cell obtained in the water window with one at higher energy shows that the observable features are dramatically different even though the claimed resolution, obtained via correlation coefficient thresholds, might be the same.For the case of X-ray imaging of cells and tissues, a wide range of electron density distributions are possible. If a prior estimate for the electron density distribution can be obtained from similar samples then, in principle, it is possible to go from a desired CNR for a particular feature to a target correlation coefficient at the relevant spatial frequency  A prior estimate of the electron density probability distribution at the relevant spatial frequency. This would be affected by the proportion of heavy to light atoms when operating at near atomic resolution or the proportions of condensed chromatin (heterochromatin) to uncondensed chromatin within a biological cell.(ii) Prior knowledge of any characteristic distances between features. Examples include the repeat distances of helices within proteins/nucleic acids or the bilayer spacing within membranes.(iii) The distribution of intensities at a relevant spatial frequency. For a repeating sample, as in crystallography, this is obtained from the measured intensity distribution. For an image it is obtained from the Fourier transform of the image. In both cases there will be a measurement error, leading to a correlation coefficient less than 1 between half datasets.(iv) A model for the measurement errors. This could assume that all errors are derived from Poisson statistics or that the value of sigma is nearly the same for each measurement (applicable where the background is the main source of noise).2.1.f − ρs), where ρf is the density of the feature and ρs is the density of the surroundings.For the crystallographic case, measurements normally consist of the integrated intensity of the diffraction spots. This means that no information is obtained concerning the non-repeating parts of the crystal. Differences between adjacent unit cells give short-range disorder resulting in diffuse scatter between the diffraction spots. This diffuse scatter can be the main source of background and, for weak spots, can therefore be the main source of noise in the data. For a non-crystallographic case, the measurements are made over the complete scattering pattern and the material surrounding any feature of interest gives the contrast  in Section 2e.g. Penczek, 2020Q values  distribution function of a particle is obtained from the scattered intensities without loss of information. It is possible to extend this concept to three dimensions with an approximate model of a biological cell. This only contains features giving a distribution of electron density (positive and negative contrast) at a particular spatial frequency and random distances between the features [satisfying both criteria (i) and (ii)]. The square of the electron density distribution can then be used to give the intensity distribution [criteria (iii)] as inter particle interference does not occur. Finally, if it is assumed that only Poisson noise is present, an estimate can be made for the errors from the square root of the intensities [satisfying criteria (iv)], noting that this is different from the distribution of contrasted electron densities which can be positive or negative.et al.  cannot be defined by a single voxel as any feature has an associated form factor. To accurately represent this in an electron density map, several voxels will be needed for each feature. Taken together, these voxels can be represented by a single ‘Shannon’ sample of the density, centred on the feature, with a form factor corresponding to that for the feature. However, it is assumed for the present purpose that a single feature can be represented by a single voxel centred on the feature, following Howells et al.  defined by the feature size and ρs is the density of the surroundings. The ratio |ρf − ρs|/(σf + σs)1/2, defined as the CNR . Note that the equal-width line and space grating is a poor model for distinguishing individual atoms as most of the electrons are concentrated near the atomic centre, over a distance much less than interatomic distances.To calculate a defined probability e.g. beam instabilities) and errors from the model (e.g. inability to interpret disordered solvent) can contribute to the CNR for a particular feature. These errors will also contribute to difference maps (e.g.mFo − DFc maps) used in protein crystallography.As well as the shot noise, errors from the instrument  should scatter 25 photons. Although a signal to noise ratio of 5 might seem to be a severe requirement, there can be many voxels in a three-dimensional image, increasing the probability of a false positive. Starodub et al. . A special procedure (CCanom) has been developed for the case of small changes due to anomalous scattering  and some of which can be almost contrast matched . Specifying a resolution for a particular image (e.g. via some correlation coefficient threshold or making measurements across some boundary) demonstrates that a particular instrument is capable of reaching this resolution. It does not mean that any feature of a particular size can be reliably identified.At spatial frequencies common in X-ray imaging, the concept of contrast is more important. This is because there are a wide range of different features, some of which have densities well above the surrounding solvent  of occurring. The contrast for a particular feature (ρfc) can be defined as ρf − ρcytosol where ρf is the electron density of the feature and ρcytosol is the electron density of the surrounding cytosol. The contrast can also be defined as ρf − 〈ρ〉, where 〈ρ〉 is the average density. The latter definition of the contrast is used in the example below. The size of the voxel (dv) is chosen and the calculation is made for the distribution of the total scattered X-rays from each voxel. In the calculation below, the corresponding value of CC1/2 therefore corresponds to the total intensity distribution from voxels of a defined size. The calculation could then be repeated with different sized voxels to obtain an estimate of CC1/2 for different spatial frequencies.To illustrate the process of linking the desired CNR with CC1/2 values and other intensity statistics is derived in the appendix of Karplus & Diederichs  in the feature above or below the average (or above or below the cytosol) is given by The number of electrons , and K is a constant of proportionality with a value dependent on the X-ray exposure.To obtain an estimate for the distribution of scattered photons, it is assumed that each feature is randomly positioned in the cell so the features can be considered to scatter independently. This assumption is an approximation for a real sample. The assumption means that the scattered intensity from each feature is proportional to the square of the contrast. This gives et al.  above the surroundings giving the probability of a false identification of approximately 0.1% (depending on the exact distribution). The probability of a false identification will be higher for the 10 nm protein. It will also be much higher for the plasma membrane unless one is able to correlate the contrast across several voxels.With the exposure and corresponding CCet al. (2020Contrast can be improved by heavy atom stains, and this is commonly used for imaging of brain tissue where optimizing the stain density is often necessary for obtaining the required information. Measurements of stain density can be found in the work by Fera  al. 2020. An anal6.Although the use of the term resolution could be considered a largely terminological issue, in the case of X-ray imaging of cells and tissue the use of the term is more misleading than illuminating.1/2 graph as a function of spatial frequency; intensity distributions at selected spatial frequencies; histograms of image electron densities or refractive indices, for both the real and the imaginary components, at selected spatial frequencies; calculating the entropy from the histogram of densities at selected spatial frequencies. Note however that this does ignore the spatial distribution of densities in the image.Given the limitations of a single resolution estimate for an image, several additional metrics could be calculated from the images or measured/derived intensities. In order to be useful, they should be either single numbers or one-dimensional graphs. Possibilities include a complete FSC or CCet al. (2021A discussion is required regarding which would be most useful. This could occur under existing initiatives such REMBI as described by Sarkans  al. 2021.Even for protein structure determination the established methods for determining a resolution are still under debate. The use of a single metric such as resolution for defining the quality of an electron density map is potentially misleading. It would be of some benefit if a second easily understood metric could be agreed or at least error estimates for the claimed resolution are provided. The problem for both X-ray imaging and protein structure determination is that any metrics must be reasonably robust, agreed by the experts and understandable by less-expert users.Finally, compiling a list of electron densities as a function of spatial resolution for typical cells would be a useful exercise for X-ray imaging.10.1107/S205225252300698X/mf5065sup1.pdfTable S1 to accompany Fig. 1. DOI: