Enhanced efficiency of AgAlO2/g-C3N4 binary composite to degrade organic pollutants for environmental remediation under visible light irradiation.

This study explores the utilization of semiconductor-based photocatalysts for environmental remediation through photocatalytic degradation, harnessing solar energy for effective treatment. The primary focus is on the application of photocatalytic technology for the degradation of 2-chlorophenol and methylene blue, critical pollutants requiring remediation. The research involves the synthesis of binary AgAlO2/g-C3N4 nanocomposites through an exchange ion method, subsequent calcination, and sonication. This process enhances the transfer of photogenerated electrons from AgAlO2 to g-C3N4, resulting in a significantly increased reductive electron charge on the surface of g-C3N4. The photocatalytic activity of the synthesized composites is comprehensively examined in the degradation of 2-chlorophenol and methylene blue through detailed crystallographic, electron-microscopy, photoemission spectroscopy, electrochemical, and spectroscopic characterizations. Among the various composites, AgAlO2/20% g-C3N4 emerges as the most active photocatalyst, achieving an impressive 98% degradation of methylene blue and 97% degradation of 2-chlorophenol under visible light. Notably, AgAlO2/20% g-C3N4 surpasses bare AgAlO2 and bare g-C3N4, exhibiting 1.66 times greater methylene blue degradation and constant rate (k) values of 20.17 × 10-3 min-1, 4.18 × 10-3 min-1 and 3.48 × 10-3 min-1, respectively. The heightened photocatalytic activity is attributed to the diminished recombination rate of electron-hole pairs. Scavenging evaluations confirm that O2•- and h+ are the primary photoactive species steering methylene blue photodegradation over AgAlO2/g-C3N4 in the visible region. These findings present new possibilities for the development of efficient binary photocatalysts for environmental remediation.

Photocurable Carbon Nanotube/Polymer Nanocomposite for the 3D Printing of Flexible Capacitive Pressure Sensors.

A photocurable resin/carbon nanotube (CNT) nanocomposite was fabricated from aligned CNTs in an acrylic matrix. The conductivity of the nanocomposite increased rapidly and then stabilized when the CNT content was increased up to and beyond the percolation threshold. Various structures were created using a digital light processing (DLP) 3D printer. Various polymeric dispersants (SMA-amide) were designed and synthesized to improve the CNT dispersion and prevent aggregation. The benzene rings and lone electron pairs on the dispersant interacted with aromatic groups on the CNTs, causing the former to wrap around the latter. This created steric hindrance, thereby stabilizing and dispersing the CNTs in the solvent. CNT/polymer nanocomposites were created by combining the dispersant, CNTs, and a photocurable resin. The CNT content of the nanocomposite and the 3D printing parameters were tuned to optimize the conductivity and printing quality. A touch-based human interface device (HID) that utilizes the intrinsic conductivity of the nanocomposite and reliably detects touch signals was fabricated, enabling the free design of sensors of various styles and shapes using a low-cost 3D printer. The production of sensors without complex circuitry was achieved, enabling novel innovations.

Integration of Multivariate Statistical Control Chart and Machine Learning to Identify the Abnormal Process Parameters for Polylactide with Glass Fiber Composites in Injection Molding; Part I: The Processing Parameter Optimization for Multiple Qualities of Polylactide/Glass Fiber Composites in Injection Molding.

This paper discusses the mixing of polylactide (PLA) and glass fiber which use injection molding to produce a functional composite material with glass fiber properties. The injection molding process explores the influence of glass fiber ratio, melt temperature, injection speed, packing pressure, packing time and cooling time on the mechanical properties of composite. Using the orthogonal table planning experiment of the Taguchi method, the optimal parameter level combination of a single quality process is obtained through main effect analysis (MEA) and Analysis of variance (ANOVA). Then, the optimal parameter level combination of multiple qualities is obtained through principal component analysis (PCA) and data envelopment analysis (DEA), respectively. It is observed that if all the quality characteristics of tensile strength, hardness, impact strength and bending strength are considered at the same time, the optimal process conditions are glass fiber addition 20 wt %, melt temperature 185 °C, injection speed 80 mm/s, holding pressure 60 MPa, holding time 1 s and cooling time 15 s, and the corresponding mechanical properties are tensile strength 95.04 MPa, hardness 86.52 Shore D, impact strength 4.4408 J/cm2, bending strength 119.89 MPa. This study effectively enhances multiple qualities of PLA/GF composite.

Synthesis of novel Type-II MnNb2O6/g-C3N4 Mott-Schottky heterojunction photocatalyst: Excellent photocatalytic performance and degradation mechanism of fluoroquinolone-based antibiotics.

Fluoroquinolone antibiotics have been encountered in aquatic environments in quantities giving rise to significant concern recently. To cope with this problem, it is necessary to design a semiconductor photocatalyst having excellent photocatalytic efficiency to eliminate the antibiotics. The heterojunction is a likely situate where the efficiency of relevant photocatalyst can be strengthened. In this study, the performance of MnNb2O6/g-C3N4 (MNO/g-CN) composites in the photocatalytic degradation of ciprofloxacin (CIP) and tetracycline-HCl (TCH) antibiotics was explored. Enhanced photocatalytic activity of MNO/g-CN was found to be owing to electron's shifting between the MNO, and g-CN sheets, which promotes the formation of photo-generated e⁻/h⁺ pairs. This shows a low-waste, high-performance material exists to eradicate CIP and TCH from wastewater. Further, the structural, photochemical and light interacted properties of the MNO/g-CN photocatalyst, prepared by solvothermal method and sonication, were described using photochemical, physiochemical and electrochemical approaches. The synthesized photocatalyst owes its particular efficiency to its methodical photo-degradation of CIP and TC using visible light. The optimum composite 15% MNO/g-CN evinced the greatest photocatalytic efficiency with CIP and TCH photo-degradation of 94.10%, and 98.50%, respectively, and degradation mechanism were investigated using LC-MS spectroscopy. The suitable photocatalytic activity is ascribed to lower the recombination's rate of e⁻/h⁺ pairs. The scavenging evaluations proved that the h+ and •O2- were two major photoactive species accomplishing the CIP and TCH photodegradation over MNO/g-CN under visible region. Our findings pave the way for the construction of efficient binary photocatalysts for antibiotic restitution.

Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing.

Breast cancer is the most common type of cancer in women, and early detection is important to significantly reduce its mortality rate. This study introduces a detection and diagnosis system that automatically detects and classifies breast tumors in CT scan images. First, the contours of the chest wall are extracted from computed chest tomography images, and two-dimensional image characteristics and three-dimensional image features, together with the application of active contours without edge and geodesic active contours methods, are used to detect, locate, and circle the tumor. Then, the computer-assisted diagnostic system extracts features, quantifying and classifying benign and malignant breast tumors using a greedy algorithm and a support vector machine. The study used 174 breast tumors for experiment and training and performed cross-validation 10 times (k-fold cross-validation) to evaluate performance of the system. The accuracy, sensitivity, specificity, and positive and negative predictive values of the system were 99.43%, 98.82%, 100%, 100%, and 98.89% respectively. This system supports the rapid extraction and classification of breast tumors as either benign or malignant, helping physicians to improve clinical diagnosis.

In Vivo Automatic and Quantitative Measurement of Adult Human Larynx and Vocal Fold Images.

INTRODUCTION: Qualitative laryngoscopy belongs to a diagnostic routine. Nevertheless, quantitative morphometric measurements of laryngeal structures remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted digitalized analysis and provide important quantitative information for diagnostics and treatment planning. MATERIALS AND METHODS: The laryngeal images were captured with our device, which contained two parallel laser beams in order to provide the scaling reference. The maximum length of the vocal fold during respiration and vibration (phonation), vocal width at midpoint, total fold area, maximum cross-sectional area of the glottic space, and maximum vocal fold angle were determined and calculated. These parameters were analyzed and compared on the basis of age, sex, body height, body weight and body mass index. RESULTS: A total of 87 subjects were enrolled in this study, comprising 39 males and 48 females. The age range for all subjects was 21 to 80 years old. The maximum value of the glottic area and vocal angle showed no significant gender difference. Both the respiration and vibration vocal fold length was significantly longer in males than in females. The vocal width revealed no gender difference, but the fold area during both respiration and phonation was significantly larger in men than in women. As for the respiration-to-vibration ratio of the vocal length, there was a trend, but without statistical significance (P = 0.06), toward a higher length compression ratio in men than in women. Meanwhile, age was found to have a strong relationship with vocal width during phonation. The width of vibration vocal fold decreased with aging significantly. CONCLUSION: Our innovative module can provide reference parameters, which makes it possible to directly estimate the objective absolute values of relevant laryngeal structures. Our non-invasive approach can be used during routine laryngoscopy and the findings easily documented. In future, we can extend its clinical application to measure subtle laryngeal or hypopharyngeal changes, which are difficult to objectively quantify.

Improvement in Injection Molding Quality Performance with Innovative Cyclone Mixers Used in Polypropylene with Spherical Silicon Dioxide Composites.

This research proposes an innovative design of a new cyclone mixer for the quality of polymer materials, and it presents a systematic optimization model of process parameters for plastic injection molding. Thermo gravimetric analysis (TGA) and differential scanning calorimetry (DSC) were used to determine the appropriate thermal properties of processing in order to select appropriate control factors and level values for a Taguchi orthogonal array. The injection molding machine was used to make sample test pieces for tensile strength, hardness and impact strength. Significant factors were found by the signal-to-noise (S/N) ratio with an analysis of variation (ANOVA), and the single-quality optimal parameter combination was obtained. The reproducibility of the experiment was evaluated, and various quality weights were evaluated by principal components analysis (PCA). The multi-quality optimal parameter combination was found, and the comprehensive scores were compared. Finally, the process capability indices were combined with a multi-process capability analysis chart (MPCAC) to compare the process yields of cyclone mixing and screw mixing. The mechanical properties of products were evaluated to verify the performance of cyclone mixing and to provide perfect information for the injection molding quality performance of cyclone mixing and screw mixing. It was concluded that the overall quality of the cyclone mixing products is 42.72, and the total quality of the screw mixing products is 41.85. The total number of defects for the cyclone mixing is 9659 ppm, and that of the screw mixing is 10688 ppm. It can be seen that, for the overall product quality performance, cyclone mixing can be applied in the plastic injection molding process instead of screw mixing.

Au Nanorods on Carbon-Based Nanomaterials as Nanohybrid Substrates for High-Efficiency Dynamic Surface-Enhanced Raman Scattering.

Gold nanorods (AuNRs) with different aspect ratios were prepared by the seed-mediated growth method and combined with three carbon-based nanomaterials of multiple dimensions (i.e., zero-dimensional (0D) carbon black (CB), one-dimensional (1D) carbon nanotubes (CNTs), and two-dimensional (2D) graphene oxide (GO)). The AuNR/carbon-based nanomaterial hybrids were utilized in dynamic surface-enhanced Raman scattering (D-SERS). First, cetyltrimethylammonium bromide (CTAB) was used to stabilize and coat the AuNRs, enabling them to be dispersed in water and conferring a positive charge to the surface. AuNR/carbon-based nanomaterial hybrids were then formed via electrostatic attraction with the negatively charged carbon-based nanomaterials. Subsequently, the AuNR/carbon-based nanomaterial hybrids were utilized as large-area and highly sensitive Raman spectroscopy substrates. The AuNR/GO hybrids afforded the best signal enhancement because the thickness of GO was less than 5 nm, which enabled the AuNRs adsorbed on GO to produce a good three-dimensional hotspot effect. The enhancement factor (EF) of the AuNR/GO hybrids for the dye molecule Rhodamine 6G (R6G) reached 1 × 107, where the limit of detection (LOD) was 10-8 M. The hybrids were further applied in D-SERS (detecting samples transitioning from the wet state to the dry state). During solvent evaporation, the system spontaneously formed many hotspots, which greatly enhanced the SERS signal. The final experimental results demonstrated that the AuNR/GO hybrids afforded the best D-SERS signal enhancement. The EF value for R6G reached 1.1 × 108 after 27 min, with a limit of detection of 10-9 M at 27 min. Therefore, the AuNR/GO nanohybrids have extremely high sensitivity as molecular sensing elements for SERS and are also very suitable for the rapid detection of single molecules in water quality and environmental management.

Design of Acrylate-Terminated Polyurethane for Nylon Seamless Bonding Fabric Part I: Design of the End-Capping Thermoplastic Polyurethane Adhesive with Acrylate Copolymer.

This series of studies aims to design acrylate-terminated polyurethanes for use in nylon seamless bonded fabrics. The first part used N,N-dimethylacrylamide (DMAA) and methyl methacrylate (MMA) to replace the chain extender in polyurethane synthesis as end-capping agent to synthesize thermoplastic polyurethane (TPU) adhesive. The molecular weight of the TPU is controlled to further influence the mechanical and processing properties of the polyurethane. Here, polytetramethylene ether glycol (PTMG) and 4,4-methylene diphenyl diisocyanate (MDI) were polymerized, and then a blocking agent was added thereto. The results show that the characteristic peaks of benzene ring and carbamate of TPU adhesive are at 1596 cm-1 and 1413 cm-1, respectively, while the characteristic peaks of DMAA are at 1644 cm-1 and 1642 cm-1 in the FT-IR spectrum. There is an absorption peak -N=C=O- which is not shown near 2268 cm-1, which proves that the structure of TPU contains the molecular structure of capping agent, PTMG and MDI. When the DMAA concentration in the capping agent was increased from 3.0 wt% to 10 wt%, the -C=O (H-bond) area percentage of hydrogen bonds formed at 1711 cm-1 increased from 41.7% to 57.6%, while the -NH (H bond) produced at 3330 cm-1 increased from 70% to 81%. These phenomena suggest that increasing the concentration of DMAA capping agent can effectively promote the formation of complex supramolecular network structures by hydrogen bonding in TPU. The content and concentration of the capping agent affects the molecular weight of the TPU. Chain growth is terminated when molecular weight growth can be effectively controlled and reduced. It was observed in thermal analysis that with increasing DMAA concentration in the molecular structure, the concentration of capping agent in TPU, hydrogen bonding force between hard segments, melting point (Tmh) and melting enthalpy (ΔH) all increased the capping agent. The pyrolysis temperature of TPU is increased by 10-20 °C.

Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines.

This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and classified. First, an image is captured by a CMOS industrial camera with a pixel size of 4600 × 600 above the batcher at 20 m/min. After that, the four-stage image processing procedure is applied to detect defects and for classification. Stage 1 is image pre-processing; the filtration of the image noise is carried out by a Gaussian filter. The light source is corrected to reduce the uneven brightness resulting from halo formation. The improved mask dodging algorithm is used to reduce the standard deviation of the corrected original image. Afterwards, the background texture is filtered by an averaging filter, and the mean value is corrected for histogram shifting, so that this system is robust to the texture and color changes of woven fabric. The binary segmentation threshold is determined using the mean value and standard deviation of an image with a normal sample. Stage 2 uses adaptive binarization for separation of the background and defects and to filter the noise. In Stage 3, the morphological processing is used before the defect contour is circled, i.e., four features of each block, including the defect area, the aspect ratio of the defect, the average gray level of the defect and the defect orientation, which are calculated according to the range of contour. The image defect recognition dataset consists of 2246 images. The results show that the detection success rate is 96.44%, and the false alarm rate is 3.21%. In Stage 4, the defect classification is implemented. The support vector machine (SVM) is used for classification, 230 defect images are used as training samples, and 206 are used as test samples. The experimental results show that the overall defect recognition rate is 96.60%, providing that the software and hardware equipment designed in this study can implement defect detection and classification for woven fabric effectively.

Development of Multifunctional Nano-Graphene-Grafted Polyester to Enhance Thermal Insulation and Performance of Modified Polyesters.

Nano-graphene materials have improved many thermal properties based on polymer systems. The additive polymers' thermal insulation cannot be significantly increased for use as a reinforcement in multifunctional thermally insulating polymer foam. Herein, we present the development of far-infrared emissivity and antistatic properties using multifunctional nano-graphene polyester fibers. Nano-graphene far-infrared thermal insulation polyester was synthesized with 2% nano-graphene and dispersant polypropylene wax-maleic anhydride (PP wax-MA) using the Taguchi method combined with grey relational analysis (GRA) to improve the thermal properties and the performance of the polymer composite. The thermogravimetric analysis (TGA) shows that the pyrolysis temperature of spinning-grade polyester was increased when the nano-graphene powder was added to the polyester. The differential scanning calorimeter (DSC) analysis confirmed the modification of polyester by nano-graphene, showing the effect of the nucleating agent, which ultimately improved the performance of the polyester. The physical properties of the optimized polyester fibers were improved with a yarn count of 76.5 d, tensile strength of 3.3 g/d, and an elongation at break increased from 23.5% to 26.7% compared with unmodified polymer yarn. These far-infrared emission rates increased from 78% to 83%, whereas the far-infrared temperature increased from 4.0 °C to 22 °C, and the surface resistance increased to 108 Ω. The performance of the optimized modified polyester yarn is far better than single-polypropylene-grafted maleic anhydride yarn. The performance of optimized modified polyester yarn, further confirmed using grey correlation analysis (GRA), can improve the yarns' mechanical properties and far-infrared functions. Our findings provide an alternative route for developing nano-graphene polyester fabrics suitable for the fabric industry.

Quality Prediction and Abnormal Processing Parameter Identification in Polypropylene Fiber Melt Spinning Using Artificial Intelligence Machine Learning and Deep Learning Algorithms.

Melt spinning machines must be set up according to the process parameters that result in the best end product quality. In this study, artificial intelligence algorithms were employed to create a system that detects abnormal processing parameters and suggests strategies to improve quality. Polypropylene (PP) was selected as the experimental material, and the quality achieved by adjusting the melt spinning machine's processing parameter settings was used as the basis for judgement. The processing parameters included screw temperature, gear pump temperature, die head temperature, screw speed, gear pump speed, and take-up speed as the six control factors. The four quality characteristics included fineness, breaking strength, elongation at break, and elastic energy modulus. In the first part of our study, we applied fast deep-learning characteristic grid calculations on a 440-item historical data set to train a deep learning neural network and determine methods for multi-quality optimization. In the second part, with the best processing parameters as a benchmark, and given abnormal quality data derived from processing parameter settings deviating from these optimal values, several machine learning and deep learning methods were compared in their ability to find the settings responsible for the abnormal data, which was randomly split into a 210-item training data set and a 210-item verification data set. The random forest method proved to be the best at identifying responsible parameter settings, with accuracy rates of single and double identification classifications together of 100%, for single factor classification of 98.3%, and for double factor classification of 96.0%, thereby confirming that the diagnostic method proposed in this study can effectively predict product abnormality and find the parameter settings responsible for product abnormality.

Quantitative Measurement of Adult Human Larynx post General Anesthesia with Intubation.

Introduction: Post-anaesthetic sore throat (PAST) is a well-recognized consequence of tracheal intubation; however, quantitative morphometric measurements remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted, digitalized analysis and provide important information on laryngeal mucosa change, pre and post-surgery under general anesthesia with intubation. Materials and methods: The laryngeal images were captured and divided into the control group and the intubation group. Image processing techniques were used to quantify the post-extubation laryngeal variation, with its distinct color space and texture features. Meanwhile, the maximum length of the vocal fold, vocal width at the midpoint, and maximum cross-sectional area of the glottic space were determined and calculated. These parameters were analyzed and compared pre and post-surgery. Results: A total of 69 subjects were enrolled in this study, comprising 32 subjects in the healthy group and 37 subjects in the intubation group. The color space and texture analysis with contrast and correlation profiles all shows trend toward higher measures in the intubation group than in the healthy group, with statistical significance and outstanding discrimination ability, especially in the interarytenoid region. The incidence of PAST was approximately 46% (17 patients). The gender difference, type of surgery, and the fixation position of the tube were not significantly related to the PAST occurrence. All the eigenvalues showed significant differences pre and post-surgery in the interarytenoid region and a significant trend toward red and increased contrast texture profiles was revealed. Furthermore, the glottic area showed a significant decrease of 25.29%, while the vocal width showed a significant increase post extubation. Conclusion: Our equipment and processing can measure subtle laryngeal changes that would allow a clinician to diagnose postoperative laryngeal inflammation in a simpler and less invasive way. The trend toward red, the increased contrast texture and vocal width, and the reduced glottic space were all compatible with post-intubation inflammatory response, especially in the interarytenoid region. This is important to know so that one can take appropriate steps to alleviate PAST in the future.

Quantitative Measurement of Throat and Larynx After Endotracheal Intubation for Palatoplasty.

Introduction: Quantitative morphometric measurements of living human upper airway remain challenging. This study aimed to introduce a special laser projection device that can facilitate computer-assisted, digitalized analysis and provide important information on airway mucosa change, before and after endotracheal intubation for palatoplasty. Materials and Methods: The laryngeal images were captured before and after surgery. Image processing techniques were used to quantize the post-operative laryngeal variation, with its distinct color space and texture features. Meanwhile, the maximum length of the vocal fold, vocal width at the midpoint, maximum cross-sectional area of the glottic space, maximum cross-sectional area of the oropharyngeal inlet (CSAOI) and the depth of the retropalatal space were determined and calculated. These parameters were analyzed and compared before and after surgery. Results: A total of 39 subjects were enrolled in this study. The color space and texture analysis all show trends toward higher measures in post-operative images than in pre-operative images, especially in the interarytenoid region. Furthermore, the glottic area showed a significant decrease of 31.2%, while the vocal width showed a significant increase after intubation. The post-operative retropalatal depth and CSAOI were significantly deeper and larger than the baseline, reaching their peak in the 4th week after the surgery, and then slightly reduced in the 12th week. Conclusion: For the first time we present a series of changes in upper airway after surgery, by using a laser module with quantitative measurement. Our equipment and processing can measure subtle mucosal changes that would allow a clinician to diagnose post-operative airway inflammation in a simpler and less invasive way. Here additional information collected by different imaging modalities would help to solve multiple current unmet needs in post-operative airway inflammation.

Semi-Supervised Deep Learning Semantic Segmentation for 3D Volumetric Computed Tomographic Scoring of Chronic Rhinosinusitis: Clinical Correlations and Comparison with Lund-Mackay Scoring.

BACKGROUND: The traditional Lund-Mackay score (TLMs) is unable to subgrade the volume of inflammatory disease. We aimed to propose an effective modification and calculated the volume-based modified LM score (VMLMs), which should correlate more strongly with clinical symptoms than the TLMs. METHODS: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our convolutional neural networks, with the algorithm including a combination of MobileNet, SENet, and ResNet. A total of 175 CT sets, with 50 participants that would undergo sinus surgery, were recruited. The Sinonasal Outcomes Test-22 (SNOT-22) was used to assess disease-specific symptoms before and after surgery. A 3D-projected view was created and VMLMs were calculated for further comparison. RESULTS: Our methods showed a significant improvement both in sinus classification and segmentation as compared to state-of-the-art networks, with an average Dice coefficient of 91.57%, an MioU of 89.43%, and a pixel accuracy of 99.75%. The sinus volume exhibited sex dimorphism. There was a significant positive correlation between volume and height, but a trend toward a negative correlation between maxillary sinus and age. Subjects who underwent surgery had significantly greater TLMs (14.9 vs. 7.38) and VMLMs (11.65 vs. 4.34) than those who did not. ROC-AUC analyses showed that the VMLMs had excellent discrimination at classifying a high probability of postoperative improvement with SNOT-22 reduction. CONCLUSIONS: Our method is suitable for obtaining detailed information, excellent sinus boundary prediction, and differentiating the target from its surrounding structure. These findings demonstrate the promise of CT-based volumetric analysis of sinus mucosal inflammation.

Synthesis of novel CuNb2O6/g-C3N4 binary photocatalyst towards efficient visible light reduction of Cr (VI) and dyes degradation for environmental remediation.

The further development of an efficient and sustainable water treatment requires the development of a very active and controllable photocatalyst. The heterojunction is a promising site where the activity of such a photocatalyst can be enhanced. Organic dyes have become a severe concern in recent years owing to their significant presence in wastewater. Hexavalent Chromium (Cr (VI)) is a potential carcinogen also exhibiting great persistence in wastewater. So, a low-waste, high-performance materials is required to eliminate organic dyes and Cr (VI) from wastewater. In this study, CNO/g-CN (CuNb2O6/g-C3N4) photocatalyst synthesized via co-precipitation, followed by calcination which were characterized using physiochemical and photo-electrochemical approaches to identify their structural, photochemical and optical traits. The uniqueness of the synthesized photocatalyst is due to both its efficient photo-reduction of Cr (VI) and photo-degradation of Rhodamine B (RhB), Methylene Blue (MB) and Methyl Orange (MO) under visible light. The CNO/g-CN composite with 30% CNO heterojunctions exhibited the highest photocatalytic activity with Cr (VI) 92.80% photoreduction and efficiency degradation for RhB, MB, MO of 99.6%, 98.50%, 99.0%, respectively, with constant rate (k). This efficient photocatalytic activity is attributed to the lower recombination rate of electron-hole pairs. Free radical trapping experiments showed that •O2- and h+ play an important role in the photodegradation. The study, therefore, opens an alternative route in the synthesis of very efficient binary photocatalysts for application in environmental remediation.

Enhanced Efficiency of Dye-Sensitized Solar Cells Based on Polymer-Assisted Dispersion of Platinum Nanoparticles/Carbon Nanotubes Nanohybrid Films as FTO-Free Counter Electrodes.

In this study, polymer-assisted dispersants are used to stabilize the nanohybrids of platinum nanoparticles (PtNPs)/carbon nanotubes (CNTs) through non-covalent bond forces. These dispersants aim to replace the florine-doped tin oxide (FTO) glass in traditional dye-sensitized solar cells (DSSCs) as counter electrodes. The large specific surface area, high conductivity, and redox potential of PtNPs/CNT nanohybrids are used as the basis to utilize them as the counter electrode material to fabricate a dye-sensitized solar cell. The conductivity results indicate that the resistance of the PtNP/CNT nanohybrid film can be reduced to 7.25 Ω/sq. When carbon nanotubes are mixed with platinum nanoparticles at a weight ratio of 5/1, the photoelectric conversion efficiency of DSSCs can reach 6.28%. When using the FTO-containing substrate as the counter electrode, its conversion efficiency indicates that the micro-/nano-hybrid material formed by PtNPs/CNTs also exhibits an excellent photoelectric conversion efficiency (8.45%) on the traditional FTO substrate. Further, a large-area dye-sensitive cell is fabricated, showing that an 8 cm × 8 cm cell has a conversion efficiency of 7.95%. Therefore, the traditional Pt counter electrode can be replaced with a PtNP/CNT nanohybrid film, which both provides dye-sensitive cells with a high photoelectric conversion efficiency and reduces costs.

Quantitative laryngoscopy with computer-aided diagnostic system for laryngeal lesions.

Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria. This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. First to reduce the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range for better performance. Second, we also proposed a method to automatically screen clear images from laryngoscopic film. Third, we used ACM to segment automatically them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree. This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively. The cross-validation accuracy for the five classes were 93.1%, 94.95%, 99.4%, 96.01% and 100%, respectively, and the average test accuracy for the laryngeal lesions was 93.33%. Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions.

Enhanced Piezoelectric Properties of Poly(Vinylidenefluoride-Co-Trifluoroethylene)/Carbon-Based Nanomaterial Composite Films for Pressure Sensing Applications.

In this study, heat and polarization treatments were applied to poly(vinylidenefluoride-co-trifluoroethylene (PVDF-TrFE) films to improve their crystallinity and piezoelectric effect. Carbon-based nanomaterials (CBNs) of multiple dimensions (i.e., modified zero-dimensional (0D) carbon black (OCB), one-dimensional (1D) modified carbon nanotubes (CNT-COOH) and two-dimensional (2D) graphene oxide (GO)) were added to the copolymer to study the effects of different CBN dimensions on the crystallinity and piezoelectric effect of PVDF-TrFE films. Additionally, amphiphilic polymeric dispersants were added to improve the dispersibility of CBNs; the dispersant was synthesized by the amidation, and imidization reactions of styrene-maleic anhydride copolymer (SMAz) and polyoxyalkylene amine (M1000). Polymer solutions with different ratios of CBN to dispersant (z = 10:1, 5:1, 1:1, 1:5, 1:10) were prepared. The enhanced dispersibility enabled the fluorine atoms in the PVDF-TrFE molecular chain to more efficiently form hydrogen bonds with the -COOH group in the CBN, thereby increasing the content of the ß crystal phase (the origin of the piezoelectric effect) of the film. Therefore, the resulting film exhibited a higher output voltage on the application side and better sensitivity on the sensing element. The addition of CNT-COOH and polymeric dispersants increased the ß-phase content in PVDF-TrFE from 73.6% to 86.4%, which in turn raised the piezoelectric coefficient from 19.8 ± 1.0 to 26.4 ± 1.3 pC/N. The composite film-based pressure sensor also exhibited a high degree of sensitivity, which is expected to have commercial potential in the future.

Quantitative Morphometric Measurements of the Oropharynx in Obstructive Sleep Apnea Syndrome Using a Laser Depth Measurement Module.

INTRODUCTION: Current diagnostic routines in obstructive sleep apnea syndrome (OSAS), including drug-induced sleep endoscopy (DISE), provide qualitative data. Quantitative morphometric measurements of oropharyngeal structures remain challenging. This study aims to introduce a special linear laser projection device that can facilitate computer-assisted digitalized analysis and provide important quantitative information for OSAS prediction. MATERIALS AND METHODS: We used a single-wavelength green three-linear laser to provide the scaling reference, with one at an angle of 8.5 degrees with the other two which were parallel. The oropharyngeal images were divided into two groups: the non-OSAS and OSAS group, after polysomnography. A minimum of three evaluations were carried out to determine the maximum cross-sectional area of the oropharyngeal inlet (CSAOI) and the retropalatal depth. RESULTS: A total of 132 subjects were enrolled in this study, with 76 subjects in the non-OSAS group and 56 cases in the OSAS group. In the non-OSAS group, the CSAOI was significantly larger in males than in females. There was a trend toward deeper retropalatal region in men than in women (14.25 vs 11.76 mm). Correlation analysis revealed that retropalatal depth is significantly related to body height and the CSAOI. The body weight and BMI of patients with OSAS were significantly higher than those of participants without OSAS. The retropalatal depth and CSAOI were significantly decreased in OSAS patients as compared to those without OSAS. Our new parameter, the oropharyngeal index, showed the most outstanding discrimination by ROC analysis to predict OSAS. CONCLUSION: Our innovative module can provide reference parameters, which make it possible to directly estimate the objective absolute values of relevant oropharyngeal structures. Our non-invasive approach can be used for outpatient screening, since it allows the identification of potential OSAS patients who should be referred for polysomnography, as many patients do not require DISE early in their evaluation.