Solution-Processed One-Dimensional Photonic Crystals Based on Hollow Silica Exhibiting High Refractive Index Contrast.

Poor interfacial quality and low refractive index contrast (Δn) are critical challenges for the development of high-performance one-dimensional photonic crystals (1DPhCs) via solution methods that impede their optical efficiency. Herein, we introduce an innovative approach by hybridizing hollow SiO2 with poly(vinyl alcohol), referred to as PHS, followed by alternate assembly with TiO2 via spin-coating, achieving a 1DPhC with Δn = 0.76 at the wavelength of 550 nm. This method circumvents the need for high-temperature treatment and complex curing conditions, resulting in a 1DPhC with superior interfacial and optical characteristics. By adjusting the thickness of the PHS layers, we can finely tune the reflectance spectrum, attaining over 99% reflectance at the photonic band gap. Furthermore, 1DPhC demonstrates excellent adhesion to polycarbonate substrates and retains its optimal optical performance even after rigorous environmental testing, including hygrothermal cycles, exposure to hot water, friction, and solvent sonication. This research paves the way for the facile fabrication of high-performance 1DPhCs under mild conditions, offering new perspectives for photonic material processing.

Antibacterial polyurethane foams with quaternized-chitosan as a chain extender for nasal packing and hemostasis.

Endoscopic surgery is an effective and common clinical practice for chronic sinusitis. Nasal packing materials are applied in nasal surgery to prevent hemorrhage and promote wound healing. In this study, a degradable polyurethane foam dressing is successfully developed as a promising nasal packing material with good biocompatibility and antibacterial capability. Specifically, quaternized chitosan (QCS) serves as the crosslinker instead of polyols to offer polyurethane foam (PUF-QCS) antibacterial capability. The PUF-QCS2.0 % (with 2.0 wt% QCS) exhibits satisfactory liquid absorption capacity (19.4 g/g), high compressive strengths at both wet (14.5 kPa) and dry states (7.7 kPa), and a good degradation rate (8.3 %) within 7 days. Meanwhile, PUF-QCS2.0 % retains long-term antibacterial activity for 7 days and kills 97.3 % of S. aureus and 91.8 % of E. coli within 6 hours in antibacterial testing. Furthermore, PUF-QCS2.0 % demonstrates a positive hemostatic response in the rabbit nasal septum mucosa trauma model by reducing hemostatic time over 50.0 % and decreasing blood loss up to 76.1 % compared to the commercial PVA nasal packing sponge. Importantly, PUF-QCS also exhibits a significant antibacterial activity in nasal cavity. This nasal packing material has advantages in post-surgery bleeding control and infection prevention. STATEMENT OF SIGNIFICANCE: The performance of a nasal packing sponge requires good mechanical properties, fast and high liquid absorption rate, effective degradability and strong antibacterial activity. These features are helpful for improving the postoperative recovery and patient healing. However, integrating these into a single polyurethane foam is a challenge. In this study, quaternized chitosan (QCS) is synthesized and used as a chain extender and antibacterial agent in preparing a degradable polyurethane foam (PUF-QCS) dressing. PUF-QCS undergoes partial degradation and exhibits effective broad-spectrum antibacterial activity in 7 days. The reduction of postoperative bleeding and infection observed in the animal experiment further demonstrates that the PUF-QCS developed here outperforms the existing commercial nasal packing materials.

Decoding the complete organelle genomic architecture of Stewartia gemmata: an early-diverging species in Theaceae.

BACKGROUND: Theaceae, comprising 300 + species, holds significance in biodiversity, economics, and culture, notably including the globally consumed tea plant. Stewartia gemmata, a species of the earliest diverging tribe Stewartieae, is critical to offer insights into Theaceae's origin and evolutionary history. RESULT: We sequenced the complete organelle genomes of Stewartia gemmata using short/long reads sequencing technologies. The chloroplast genome (158,406 bp) exhibited a quadripartite structure including the large single-copy region (LSC), a small single-copy region (SSC), and a pair of inverted repeat regions (IRs); 114 genes encoded 80 proteins, 30 tRNAs, and four rRNAs. The mitochondrial genome (681,203 bp) exhibited alternative conformations alongside a monocyclic structure: 61 genes encoding 38 proteins, 20 tRNAs, three rRNAs, and RNA editing-impacting genes, including ATP6, RPL16, COX2, NAD4L, NAD5, NAD7, and RPS1. Comparative analyses revealed frequent recombination events and apparent rRNA gene gains and losses in the mitochondrial genome of Theaceae. In organelle genomes, the protein-coding genes exhibited a strong A/U bias at codon endings; ENC-GC3 analysis implies selection-driven codon bias. Transposable elements might facilitate interorganelle sequence transfer. Phylogenetic analysis confirmed Stewartieae's early divergence within Theaceae, shedding light on organelle genome characteristics and evolution in Theaceae. CONCLUSIONS: We studied the detailed characterization of organelle genomes, including genome structure, composition, and repeated sequences, along with the identification of lateral gene transfer (LGT) events and complexities. The discovery of a large number of repetitive sequences and simple sequence repeats (SSRs) has led to new insights into molecular phylogenetic markers. Decoding the Stewartia gemmata organellar genome provides valuable genomic resources for further studies in tea plant phylogenomics and evolutionary biology.

Regulating microstructures of aerogels by controlling phase separation mechanism for improving specific surface area and energy harvesting.

Aerogels with 3D porous structures have been attracting increasing attention among functional materials due to their advantages of being lightweight and high specific surface area. Precise control of the porous structure of aerogel is essential to improve its performance. In this work, polylactic acid (PLA) aerogels with distinctly different microstructures were fabricated by precisely controlling the phase separation behavior of the ternary solution system. Rheological and theoretical analyses have revealed that the interactions between polymer molecules, solvents and non-solvents play a crucial role in determining the nucleation and growth of poor olymer and rich polymer phases. By adjusting the non-solvent type and the solution composition, aerogels with spider network structure, bead-like connected microsphere structure, and cluster petal structure were obtained. Ideal spinodal phase separation conditions were obtained to produce aerogels with a homogeneous fiber network structure. The optimum PLA aerogel achieved an extremely porosity of 96 % and a high specific surface area of 114 m2/g, which rendered it with excellent triboelectric generation performance. Thus, this work provides fundamental insights into the precise regulation of the phase separation behavior and the structure of the aerogel, which can help boost the performance and expand the applications of PLA aerogels.

Informing virtual clinical trials of hepatocellular carcinoma with spatial multi-omics analysis of a human neoadjuvant immunotherapy clinical trial.

Human clinical trials are important tools to advance novel systemic therapies improve treatment outcomes for cancer patients. The few durable treatment options have led to a critical need to advance new therapeutics in hepatocellular carcinoma (HCC). Recent human clinical trials have shown that new combination immunotherapeutic regimens provide unprecedented clinical response in a subset of patients. Computational methods that can simulate tumors from mathematical equations describing cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico. To facilitate designing dosing regimen and identifying potential biomarkers, we developed a new computational model to track tumor progression at organ scale while reflecting the spatial heterogeneity in the tumor at tissue scale in HCC. This computational model is called a spatial quantitative systems pharmacology (spQSP) platform and it is also designed to simulate the effects of combination immunotherapy. We then validate the results from the spQSP system by leveraging real-world spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. The model output is compared with spatial data from Imaging Mass Cytometry (IMC). Both IMC data and simulation results suggest closer proximity between CD8 T cell and macrophages among non-responders while the reverse trend was observed for responders. The analyses also imply wider dispersion of immune cells and less scattered cancer cells in responders' samples. We also compared the model output with Visium spatial transcriptomics analyses of samples from post-treatment tumor resections in the original clinical trial. Both spatial transcriptomic data and simulation results identify the role of spatial patterns of tumor vasculature and TGFß in tumor and immune cell interactions. To our knowledge, this is the first spatial tumor model for virtual clinical trials at a molecular scale that is grounded in high-throughput spatial multi-omics data from a human clinical trial.

A Multifunctional Asymmetric Fabric for Sustained Electricity Generation from Multiple Sources and Simultaneous Solar Steam Generation.

Harvesting electrical energy from water and moisture has emerged as a novel ecofriendly energy conversion technology. Herein, a multifunctional asymmetric polyaniline/carbon nanotubes/poly(vinyl alcohol) (APCP) that can produce electric energy from both saline water and moisture and generate fresh water simultaneously is developed. The constructed APCP possesses a negatively charged porous structure that allows continuous generation of protons and ion diffusion through the material, and a hydrophilicity-hydrophobic interface which results in a constant potential difference and sustainable output. A single APCP can maintain stable output for over 130 h and preserve a high voltage of 0.61 V, current of 81 µA, and power density of 82.4 µW cm-3 with 0.15 cm3 unit size in the water-induced electricity generation process. When harvesting moisture energy, the APCP creates dry-wet asymmetries and triggers the spontaneous development of electrical double layer with a current density of 1.25 mA cm-3 , sufficient to power small electronics. A device consisting of four APCP can generate stable electricity of 3.35 V and produce clean water with an evaporation rate of 2.06 kg m-2  h-1 simultaneously. This work provides insights into the fabrication of multifunctional fabrics for multisource energy harvesting and simultaneous solar steam generation.

A science mapping-based review of work-related musculoskeletal disorders among construction workers.

INTRODUCTION: Work-related musculoskeletal disorders (WMSDs) are recognized as a leading cause of nonfatal injuries in construction, but no review of existing studies has systematically analyzed and visualized the trends of WMSDs among construction workers. The current science mapping-based review summarized research published between 2000 and 2021 related to WMSDs among construction workers through co-word, co-author, and citation analysis. METHOD: A total of 63 bibliographic records retrieved from the Scopus database were analyzed. RESULTS: The results identified influential authors with high impacts in this research domain. Moreover, the results indicated that MSDs, ergonomics, and construction not only had the highest occurrence of been studied, but also the highest impact in terms of total link strength. In addition, the most significant contributions to research relating to WMSDs among construction workers have originated primarily from the United States, Hong Kong, and Canada. Furthermore, a follow-up in-depth qualitative discussion was conducted to focus on summarizing mainstream research topics, identifying existing research gaps, and proposing directions for future studies. CONCLUSIONS: This review provides an in-depth understanding of related research on WMSDs among construction workers and proposes the emerging trends in this research field.

Surface Modification of Super Arborized Silica for Flexible and Wearable Ultrafast-Response Strain Sensors with Low Hysteresis.

Conductive hydrogels exhibit high potential in the fields of wearable sensors, healthcare monitoring, and e-skins. However, it remains a huge challenge to integrate high elasticity, low hysteresis, and excellent stretch-ability in physical crosslinking hydrogels. This study reports the synthesis of polyacrylamide (PAM)-3-(trimethoxysilyl) propyl methacrylate-grafted super arborized silica nanoparticle (TSASN)-lithium chloride (LiCl) hydrogel sensors with high elasticity, low hysteresis, and excellent electrical conductivity. The introduction of TSASN enhances the mechanical strength and reversible resilience of the PAM-TSASN-LiCl hydrogels by chain entanglement and interfacial chemical bonding, and provides stress-transfer centers for external-force diffusion. These hydrogels show outstanding mechanical strength (a tensile stress of 80-120 kPa, elongation at break of 900-1400%, and dissipated energy of 0.8-9.6 kJ m-3 ), and can withstand multiple mechanical cycles. LiCl addition enables the PAM-TSASN-LiCl hydrogels to exhibit excellent electrical properties with an outstanding sensing performance (gauge factor = 4.5), with rapid response (210 ms) within a wide strain-sensing range (1-800%). These PAM-TSASN-LiCl hydrogel sensors can detect various human-body movements for prolonged durations of time, and generate stable and reliable output signals. The hydrogels fabricated with high stretch-ability, low hysteresis, and reversible resilience, can be used as flexible wearable sensors.

Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model.

Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as "cold", "compartmentalized" and "mixed", which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.

Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 58 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. We detected distinct cell distribution patterns among clinical subgroups, potentially stemming from different infiltration related to tumor vasculature and fibroblast heterogeneity. Spatial analysis also identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. Coupling of the prevalence of pan-immune and perivasculature immune hotspot CNs, enrichment of inter-CN interactions was associated with improved survival. Using a deep learning model trained on engineered spatial data, we can with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.

Quantitative Spatial Profiling of Immune Populations in Pancreatic Ductal Adenocarcinoma Reveals Tumor Microenvironment Heterogeneity and Prognostic Biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with poor 5-year survival rates, necessitating identification of novel therapeutic targets. Elucidating the biology of the tumor immune microenvironment (TiME) can provide vital insights into mechanisms of tumor progression. In this study, we developed a quantitative image processing platform to analyze sequential multiplexed IHC data from archival PDAC tissue resection specimens. A 27-plex marker panel was employed to simultaneously phenotype cell populations and their functional states, followed by a computational workflow to interrogate the immune contextures of the TiME in search of potential biomarkers. The PDAC TiME reflected a low-immunogenic ecosystem with both high intratumoral and intertumoral heterogeneity. Spatial analysis revealed that the relative distance between IL10+ myelomonocytes, PD-1+ CD4+ T cells, and granzyme B+ CD8+ T cells correlated significantly with survival, from which a spatial proximity signature termed imRS was derived that correlated with PDAC patient survival. Furthermore, spatial enrichment of CD8+ T cells in lymphoid aggregates was also linked to improved survival. Altogether, these findings indicate that the PDAC TiME, generally considered immuno-dormant or immunosuppressive, is a spatially nuanced ecosystem orchestrated by ordered immune hierarchies. This new understanding of spatial complexity may guide novel treatment strategies for PDAC. SIGNIFICANCE: Quantitative image analysis of PDAC specimens reveals intertumoral and intratumoral heterogeneity of immune populations and identifies spatial immune architectures that are significantly associated with disease prognosis.

Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model.

Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.

Multi-Scale Spatial Analysis of the Tumor Microenvironment Reveals Features of Cabozantinib and Nivolumab Efficacy in Hepatocellular Carcinoma.

Background: Concomitant inhibition of vascular endothelial growth factor (VEGF) and programmed cell death protein 1 (PD-1) or its ligand PD-L1 is a standard of care for patients with advanced hepatocellular carcinoma (HCC), but only a minority of patients respond, and responses are usually transient. Understanding the effects of therapies on the tumor microenvironment (TME) can provide insights into mechanisms of therapeutic resistance. Methods: 14 patients with HCC were treated with the combination of cabozantinib and nivolumab through the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center. Among them, 12 patients (5 responders + 7 non-responders) underwent successful margin negative resection and are subjects to tissue microarray (TMA) construction containing 37 representative tumor region cores. Using the TMAs, we performed imaging mass cytometry (IMC) with a panel of 27-cell lineage and functional markers. All multiplexed images were then segmented to generate a single-cell dataset that enables (1) tumor-immune compartment analysis and (2) cell community analysis based on graph-embedding methodology. Results from these hierarchies are merged into response-associated biological process patterns. Results: Image processing on 37 multiplexed-images discriminated 59,453 cells and was then clustered into 17 cell types. Compartment analysis showed that at immune-tumor boundaries from NR, PD-L1 level on tumor cells is significantly higher than remote regions; however, Granzyme B expression shows the opposite pattern. We also identify that the close proximity of CD8+ T cells to arginase 1hi (Arg1hi) macrophages, rather than CD4+ T cells, is a salient feature of the TME in non-responders. Furthermore, cell community analysis extracted 8 types of cell-cell interaction networks termed cellular communities (CCs). We observed that in non-responders, macrophage-enriched CC (MCC) and lymphocyte-enriched CC (LCC) strongly communicate with tumor CC, whereas in responders, such communications were undermined by the engagement between MCC and LCC. Conclusion: These results demonstrate the feasibility of a novel application of multiplexed image analysis that is broadly applicable to quantitative analysis of pathology specimens in immuno-oncology and provides further evidence that CD163-Arg1hi macrophages may be a therapeutic target in HCC. The results also provide critical information for the development of mechanistic quantitative systems pharmacology models aimed at predicting outcomes of clinical trials.

Development and Applications of Hydrogel-Based Triboelectric Nanogenerators: A Mini-Review.

In recent years, with the appearance of the triboelectric nanogenerator (TENG), there has been a wave of research on small energy harvesting devices and self-powered wearable electronics. Hydrogels-as conductive materials with excellent tensile properties-have been widely focused on by researchers, which encouraged the development of the hydrogel-based TENGs (H-TENGs) that use the hydrogel as an electrode. Due to the great feasibility of adjusting the conductivity and mechanical property as well as the microstructure of the hydrogels, many H-TENGs with excellent performance have emerged, some of which are capable of excellent outputting ability with an output voltage of 992 V, and self-healing performance which can spontaneously heal within 1 min without any external stimuli. Although there are numerous studies on H-TENGs with excellent performance, a comprehensive review paper that systematically correlates hydrogels' properties to TENGs is still absent. Therefore, in this review, we aim to provide a panoramic overview of the working principle as well as the preparation strategies that significantly affect the properties of H-TENGs. We review hydrogel classification categories such as their network composition and their potential applications on sensing and energy harvesting, and in biomedical fields. Moreover, the challenges faced by the H-TENGs are also discussed, and relative future development of the H-TENGs are also provided to address them. The booming growth of H-TENGs not only broadens the applications of hydrogels into new areas, but also provides a novel alternative for the sustainable power sources.

MXene/Polylactic Acid Fabric-Based Resonant Cavity for Realizing Simultaneous High-Performance Electromagnetic Interference (EMI) Shielding and Efficient Energy Harvesting.

Proliferation in telecommunications and integrated/intelligent devices entails an intense concern for electromagnetic interference (EMI) shielding and versatility. It remains an activated passion to launch infusive EMI shielding materials integrated with self-powered peculiarities. Herein, a double-layered MXene/polylactic acid (PLA) fabric resonance cavity (D-MPF-RC) comprised of two MXene/PLA fabrics (MPFs) with alternating MXene and PLA structures that are separated by a poly(tetrafluoroethylene) (PTFE) frame is developed. The D-MPF-RC achieved 48.5 and 74.8% improvement in SET and SEA, and 24.6% reduction in SER by introducing the double-layered structure and increasing the resonance cavity (RC) distance without varying the material composition and cost. A high shielding efficiency (SE) of 92.3 dB was obtained at an RC distance of 6 mm owing to the synergetic effects of multiple reflections and destructive EM wave interference. The tribopolarity difference between PLA and MXene and the RC structure made the D-MPF-RC a readily available triboelectric nanogenerator (TENG) that could convert mechanical energy into electricity. The D-MPF-RC TENG demonstrated an open-circuit voltage of 88 V and achieved a peak power density of 35.4 mW m-2 on a 6.6 MΩ external resistor, which made it possible to charge capacitors and serve as a self-powered tactile sensor. This report offers new insights into the design of high-performance EMI shielding shields with a resonance cavity and proposes a feasible pathway to integrate them with energy harvesting capabilities.

Predictive models of response to neoadjuvant chemotherapy in muscle-invasive bladder cancer using nuclear morphology and tissue architecture.

Characterizing likelihood of response to neoadjuvant chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is an important yet unmet challenge. In this study, a machine-learning framework is developed using imaging of biopsy pathology specimens to generate models of likelihood of NAC response. Developed using cross-validation (evaluable N = 66) and an independent validation cohort (evaluable N = 56), our models achieve promising results (65%-73% accuracy). Interestingly, one model-using features derived from hematoxylin and eosin (H&E)-stained tissues in conjunction with clinico-demographic features-is able to stratify the cohort into likely responders in cross-validation and the validation cohort (response rate of 65% for predicted responder compared with the 41% baseline response rate in the validation cohort). The results suggest that computational approaches applied to routine pathology specimens of MIBC can capture differences between responders and non-responders to NAC and should therefore be considered in the future design of precision oncology for MIBC.

Design and Optimization Principles of Cylindrical Sliding Triboelectric Nanogenerators.

Reciprocating motion is a widely existing form of mechanical motion in the natural environment. Triboelectric nanogenerators (TENGs) that work in sliding mode are ideal for harnessing large-distance reciprocating motion, and their energy conversion efficiency could be greatly enhanced by adding springs to them. Herein, we focused on investigating the design and optimization principles of sliding mode TENGs by analyzing the effects of spring parameters and vibration frequency on the triboelectric output performance of typical cylindrical sliding TENGs (CS-TENGs). Experimental study and finite elemental analysis were carried out based on a CS-TENG model assembled using a polytetrafluoroethylene (PTFE) film as the negative layer and an aluminum film as the positive layer. The energy output was found to be mainly affected by the change of relative displacement between the two friction layers, rather than the reactive force applied by the springs or the velocity of the sliding motion. However, the frequency of the output signals could be improved when the stiffness coefficient of the springs and the CS-TENG vibration frequency were increased. This study provides valuable directions for the design and optimization of sliding mode TENGs containing springs, and will motivate in-depth research on the fundamental principles of TENG operation.

Versatile Janus Composite Nonwoven Solar Absorbers with Salt Resistance for Efficient Wastewater Purification and Desalination.

Solar steam generation is an efficient way of harvesting solar energy for water purification. Developing a versatile solar absorber with salt resistance and the capability to purify an oil-in-water emulsion is a grand challenge. Herein, a polypropylene (PP) nonwoven fabric-based photothermal absorber is fabricated by the combination of carbon nanotubes (CNTs), polypyrrole (PPy), and a fluorinated hydrophobic coating in a layer-by-layer approach. The specially designed architecture displays a hierarchical microstructure and Janus wetting properties, facilitating solar absorption and heat generation on the evaporation surface, and can effectively prevent salt crystallization. The water layer formed on the superhydrophilic/underwater superoleophobic bottom surface could repel oil droplets and form a channel to advect concentrated salt back into bulk water, which enabled high purity separation of an oil-in-water emulsion and continuous desalinization of seawater without the reduction of the evaporation rate. As a result, the solar absorber can achieve a remarkable evaporation rate of 1.61 kg m-2 h-1 and an energy efficiency of 91.2% under 1 sun irradiation and shows extraordinary performance in the purification of contaminated wastewater (over 99.8% purity). The strategy proposed provides a pathway for developing versatile high-performance solar absorbers for the sustainable treatment of saline water, wastewater, and oil-containing water.

Enhancing the Performance of Fabric-Based Triboelectric Nanogenerators by Structural and Chemical Modification.

Fabric-based triboelectric nanogenerators (TENGs) are promising candidates as wearable energy-harvesting devices and self-powered sensors. Booting the power generation performance is an eternal pursuit for TENGs. Herein, an efficient approach was proposed to enhance the triboelectric performance of commercial velvet fabric by enriching the fiber surface with hierarchical structures and amide bonds through chemical grafting of carbon nanotube (CNT) and poly(ethylenimine) (PEI) via a polyamidation reaction. With an optimized modifier concentration, the fabric-based TENG easily achieved over 10 times improvement in output voltage and current at a low modifier content of less than 1 wt %. The modified-fabric-based TENG was fully washable and exhibited excellent robustness and long-term stability. With a maximum power density of 3.2 W/m2 achieved on a 5 × 106 Ω external resistor, the TENG was able to serve as a power source for various small electronics such as pedometer, digital watch, calculator, and digital timer. In addition, the TENG demonstrated capability in self-powered tactile and pressure sensing and promising potential in human-computer interface applications. The approach proposed provides a feasible path for boosting the triboelectric performance of fabric-based TENGs and gives insights into the design of fabric-based nanogenerators and smart textiles.

Shish-Kebab-Structured UHMWPE Coating for Efficient and Cost-Effective Oil-Water Separation.

High-performance low-cost superhydrophobic sponges are desired for selective recycling of leaking oils from open water. Herein, an ingenious method is proposed to fabricate an ultrathin superhydrophobic coating layer on a commercial sponge. The coating layer is composed of a shish-kebab-structured porous ultrahigh molecular weight polyethylene (UHMWPE) film that is fabricated from a UHMWPE/xylene solution by shear flow-induced crystallization. A strong relationship between the shish-kebab crystallite morphology and the superwetting performance is confirmed. The UHMWPE coating layer fabricated at a 900 rpm rotation rate possesses a lamellae size of 95.1 nm and a lamellae distance of 27.4 nm, which lead to a high water contact angle of 157° and a low contact angle hysteresis of 4.5°. The UHMWPE layer prepared in 4 min of treatment is thick enough to prevent the intrusion of water even under vacuum and remain superoleophilic. The developed UHMWPE-coated sponge (UCS) exhibited a high absorption capability of 70-191 g/g toward various oils and solvents, which is comparable with the neat melamine sponge. Its excellent compressibility and durability enabled fast recovery of absorbed oil with a high recovery rate (over 85%) by mechanical squeezing. The UCS could be assembled into small devices to selectively collect oil from open water and a water/oil mixture using a pump, which manifests its promising practical applicability. Apart from these extraordinary properties, the approach developed has the lowest material cost and the shortest processing time hitherto.