Photovoltaic Rotation and Transportation of a Fragile Fluorescent Microrod Toward Assembling a Tunable Light-Source System.

Continuous rotation of a fragile, photosensitive microrod in a safe, flexible way remains challenging in spite of its importance to microelectro-mechanical systems. We propose a photovoltaic strategy to continuously rotate a fragile, fluorescent microrod on a LiNbO3/Fe (LN/Fe) substrate using a continuous wave visible (473 nm) laser beam with an ultralow power (few tens of µW) and a simple structure (Gaussian profile). This strategy does not require the laser spot to cover the entire microrod nor does it result in a sharp temperature rise on the microrod. Both experiments and simulation reveal that the strongest photovoltaic field generated beside the laser spot firmly traps one corner of the microrod and the axisymmetric photovoltaic field exerts an electrostatic torque on the microrod driving it to rotate continuously around the laser spot. The dependence of the rotation rate on the laser power indicates contributions from both deep and shallow photovoltaic centers. This rotation mode, combined with the transportation mode, enables the controllable movement of an individual microrod along any complex trajectory with any specific orientation. The tuning of the end-emitting spectrum and the photothermal cutting of the fluorescent microrod are also realized by properly configuring the laser illumination. By taking a microrod as the emitter and a polystyrene microsphere as the focusing lens, we demonstrate the photovoltaic assembly of a microscale light-source system with both spectrum and divergence-angle tunabilities, which are realized by adjusting the photoexcitation position along the microrod and the geometry relationship in the system, respectively.

Extreme condition-tolerant stretchable flexible supercapacitor and triboelectric nanogenerator based on carrageenan-enhanced gel for energy storage, energy collection and self-powered sensing.

As flexible electronics devices for energy storage, mechanical energy collection and self-powered sensing, stretchable flexible supercapacitor and triboelectric nanogenerator (TENG) have attracted extensive attention. However, it is difficult to satisfy the requirements of high safety and resistance to extreme conditions. Dual roles of mechanical and electrical enhancement of inorganic salt are put forward, and a carrageenan (CG) enhanced poly (N-hydroxyethyl acrylamide)/CG/lithium chloride/glycerol (PCLG) conductive gel is prepared by designing hydrogen bonding self-crosslinking and chain entanglement. A high concentration and rapid deposition strategy is proposed to prepare a PCLG gel-based stretchable flexible all-in-one supercapacitor for energy storage, and a single electrode PCLG gel-based TENG is designed for mechanical energy collection, self-powered strain and tactile sensing. The supercapacitor has high capacitance, excellent cycling stability. The TENG possesses efficient energy harvesting with high and stable output voltage and power density, and sensitive and stable self-powered strain and tactile sensing without external power supply. Even under extreme conditions such as low temperatures, self-healing after damage, prolonged placement, deformation, post-deformation, multiple continuous work, pinprick and burning, the supercapacitor and TENG still have excellent properties. Therefore, we provide novel ideas to design flexible supercapacitor and TENG used under extreme conditions for future wearable electronics.

Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.

BACKGROUND: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT). METHODS: Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated. RESULTS: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively. CONCLUSION: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.

Learning Disentangled Priors for Hyperspectral Anomaly Detection: A Coupling Model-Driven and Data-Driven Paradigm.

Accurately distinguishing between background and anomalous objects within hyperspectral images poses a significant challenge. The primary obstacle lies in the inadequate modeling of prior knowledge, leading to a performance bottleneck in hyperspectral anomaly detection (HAD). In response to this challenge, we put forth a groundbreaking coupling paradigm that combines model-driven low-rank representation (LRR) methods with data-driven deep learning techniques by learning disentangled priors (LDP). LDP seeks to capture complete priors for effectively modeling the background, thereby extracting anomalies from hyperspectral images more accurately. LDP follows a model-driven deep unfolding architecture, where the prior knowledge is separated into the explicit low-rank prior formulated by expert knowledge and implicit learnable priors by means of deep networks. The internal relationships between explicit and implicit priors within LDP are elegantly modeled through a skip residual connection. Furthermore, we provide a mathematical proof of the convergence of our proposed model. Our experiments, conducted on multiple widely recognized datasets, demonstrate that LDP surpasses most of the current advanced HAD techniques, exceling in both detection performance and generalization capability.

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence.

BACKGROUND: Research into the acute kidney disease (AKD) after acute ischemic stroke (AIS) is rare, and how clinical features influence its prognosis remain unknown. We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality. METHODS: We conducted a retrospective cohort study involving AIS patients from January 2020 to June 2021. Patient data were randomly divided into training and test sets. Eight ML algorithms were employed to construct predictive models for mortality. The performance of the best model was evaluated using various metrics. Furthermore, we created an artificial intelligence (AI)-driven web application that leveraged the top ten most crucial features for mortality prediction. RESULTS: The study cohort consisted of 1633 AIS patients, among whom 257 (15.74%) developed subacute AKD, 173 (10.59%) experienced AKI recovery, and 65 (3.98%) met criteria for both AKI and AKD. The mortality rate stood at 4.84%. The LightGBM model displayed superior performance, boasting an AUROC of 0.96 for mortality prediction. The top five features linked to mortality were ACEI/ARE, renal function trajectories, neutrophil count, diuretics, and serum creatinine. Moreover, we designed a web application using the LightGBM model to estimate mortality risk. CONCLUSIONS: Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.

Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy.

BACKGROUND AND OBJECTIVE: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiotherapy safety and management. METHODS: Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction. RESULTS: The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively. CONCLUSIONS: The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.

Facile synthesis of MoS2@red phosphorus heterojunction for synergistically photodynamic and photothermal therapy of renal cell carcinoma.

The therapy of the clear cell renal cell carcinoma (ccRCC) is crucial for the human healthcare due to its easy metastasis and recurrence, as well as resistance to radiotherapy and chemotherapy. In this work, we propose the synthesis of MoS2@red phosphorus (MoS2@RP) heterojunction to induce synergistic photodynamic and photothermal therapy (PDT/PTT) of ccRCC. The MoS2@RP heterojunction exhibits enhanced spectra absorption in the NIR range and produce local heat-increasing under the NIR laser irradiation compared with pure MoS2 and RP. The high photocatalytic activity of the MoS2@RP heterojunction contributes to effective transferring of the photo-excited electrons from the RP to MoS2, which promotes the production of various types of radical oxygen species (ROS) to kill the ccRCC cells. After the NIR irradiation, the MoS2@RP can effectively induce the apoptosis in the ccRCC cells through localized hyperthermia and the generation of ROS, while exhibiting low cytotoxicity towards normal kidney cells. In comparison to MoS2, the MoS2@RP heterojunction shows an approximate increase of 22 % in the lethality rate of the ccRCC cells and no significant change in toxicity towards normal cells. Furthermore, the PDT/PTT treatment using the MoS2@RP heterojunction effectively eradicates a substantial number of deep-tissue ccRCC cells in vivo without causing significant damage to major organs. This study presents promising effect of the MoS2@RP heterojunction-based photo-responsive therapy for effective ccRCC treatment.

Distribution and molecular characterization of carbapenemase-producing gram-negative bacteria in Henan, China.

This study aimed to investigate the epidemiological characteristics and trends over time of carbapenemase-producing (e.g., KPC, NDM, VIM, IMP, and OXA-48) Gram-negative bacteria (CPGNB). Non-duplicated multi-drug resistant Gram-negative bacteria (MDRGNB) were collected from the First Affiliated Hospital of Zhengzhou University from April 2019 to February 2023. Species identification of each isolate was performed using the Vitek2 system and confirmed by matrix-assisted laser desorption ionization-time of flight mass spectrometry according to the manufacturer's instructions. PCR detected carbapenem resistance genes in the strains, strains carrying carbapenem resistance genes were categorized as CPGNB strains after validation by carbapenem inactivation assay. A total of 5705 non-repetitive MDRGNB isolates belonging to 78 different species were collected during the study period, of which 1918 CPGNB were validated, with the respiratory tract being the primary source of specimens. Epidemiologic statistics showed a significant predominance of ICU-sourced strains compared to other departments. Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii, and Pseudomonas aeruginosa were the significant CPGNB in Henan, and KPC and NDM were the predominant carbapenemases. Carbapenem-resistant infections in Henan Province showed an overall increasing trend, and the carriage of carbapenemase genes by CPGNB has become increasingly prevalent and complicated. The growing prevalence of CPGNB in the post-pandemic era poses a significant challenge to public safety.

Intelligent double-layer film based on gellan gum/modified anthocyanin/curcumin/sodium alginate/zinc oxide for monitoring shrimp freshness.

In this study, intelligent double-layer films were prepared using modified black rice anthocyanin (MBRA)-curcumin (CUR)-gellan gum (GG) as the inner indicator layer and sodium alginate (ALG)­zinc oxide (ZnO) as the outer antimicrobial layer. The bilayer films were successfully prepared, as revealed by scanning electron microscopy, Fourier-transform infrared spectroscopy, and X-ray diffraction measurements. The mechanical characteristics, moisture content, and water vapor resistance of GG-MBRA/CUR1@ALG-ZnO, GG-MBRA/CUR2@ALG-ZnO, and GG-MBRA/CUR3@ALG-ZnO films showed significant enhancement compared to GG-MBRA/CUR3 and ALG-ZnO films. The bilayer films exhibited excellent pH responsiveness and reacted effectively to ammonia. The outer layer significantly improved the antioxidant and antibacterial properties of the inner layer. When the films were applied to shrimp, it was found that the double-layer films not only monitored the freshness of the shrimp in real-time but also were influential in extending the shelf life of the shrimp by about 1 d. Therefore, the double-layer film demonstrated potential as a smart packaging material for real-time monitoring of meat product freshness.

A taxonomy for advancing systematic error analysis in multi-site electronic health record-based clinical concept extraction.

BACKGROUND: Error analysis plays a crucial role in clinical concept extraction, a fundamental subtask within clinical natural language processing (NLP). The process typically involves a manual review of error types, such as contextual and linguistic factors contributing to their occurrence, and the identification of underlying causes to refine the NLP model and improve its performance. Conducting error analysis can be complex, requiring a combination of NLP expertise and domain-specific knowledge. Due to the high heterogeneity of electronic health record (EHR) settings across different institutions, challenges may arise when attempting to standardize and reproduce the error analysis process. OBJECTIVES: This study aims to facilitate a collaborative effort to establish common definitions and taxonomies for capturing diverse error types, fostering community consensus on error analysis for clinical concept extraction tasks. MATERIALS AND METHODS: We iteratively developed and evaluated an error taxonomy based on existing literature, standards, real-world data, multisite case evaluations, and community feedback. The finalized taxonomy was released in both .dtd and .owl formats at the Open Health Natural Language Processing Consortium. The taxonomy is compatible with several different open-source annotation tools, including MAE, Brat, and MedTator. RESULTS: The resulting error taxonomy comprises 43 distinct error classes, organized into 6 error dimensions and 4 properties, including model type (symbolic and statistical machine learning), evaluation subject (model and human), evaluation level (patient, document, sentence, and concept), and annotation examples. Internal and external evaluations revealed strong variations in error types across methodological approaches, tasks, and EHR settings. Key points emerged from community feedback, including the need to enhancing clarity, generalizability, and usability of the taxonomy, along with dissemination strategies. CONCLUSION: The proposed taxonomy can facilitate the acceleration and standardization of the error analysis process in multi-site settings, thus improving the provenance, interpretability, and portability of NLP models. Future researchers could explore the potential direction of developing automated or semi-automated methods to assist in the classification and standardization of error analysis.

Investigating the L-Glu-NMDA receptor-H2S-NMDA receptor pathway that regulates gastric function in rats' nucleus ambiguus.

Background: In previous investigations, we explored the regulation of gastric function by hydrogen sulfide (H2S) and L-glutamate (L-Glu) injections in the nucleus ambiguus (NA). We also determined that both H2S and L-Glu have roles to play in the physiological activities of the body, and that NA is an important nucleus for receiving visceral sensations. The purpose of this study was to explore the potential pathway link between L-Glu and H2S, resulting in the regulation of gastric function. Methods: Physiological saline (PS), L-glutamate (L-Glu, 2 nmol), NaHS (2 nmol), D-2-amino-5-phopho-novalerate (D-AP5, 2 nmol) + L-Glu (2 nmol), aminooxyacetic acid (AOAA, 2 nmol) + L-Glu (2 nmol), D-AP5 (2 nmol) + NaHS (2 nmol) were injected into the NA. A balloon was inserted into the stomach to observe gastric pressure and for recording the changes of gastric smooth muscle contraction curve. The gastric fluid was collected by esophageal perfusion and for recording the change of gastric pH value. Results: Injecting L-Glu in NA was found to significantly inhibit gastric motility and promote gastric acid secretion in rats (p < 0.01). On the other hand, injecting the PS, pre-injection N-methyl-D-aspartate (NMDA) receptor blocker D-AP5, cystathionine beta-synthase (CBS) inhibitor AOAA and re-injection L-Glu did not result in significant changes (p > 0.05). The same injection NaHS significantly inhibit gastric motility and promote gastric acid secretion in rats (p < 0.01), but is eliminated by injection D-AP5 (p > 0.05). Conclusion: The results indicate that both exogenous L-Glu and H2S injected in NA regulate gastric motility and gastric acid secretion through NMDA receptors. This suggests that NA has an L-Glu-NMDA receptor-CBS-H2S pathway that regulates gastric function.

Large-Substrate-Terrace Confined Growth of Arrayed Ultrathin PtSe2 Ribbons on Step-Bunched Vicinal Au(001) Facets Toward Electrocatalytic Applications.

Ultrathin PtSe2 ribbons can host spin-polarized edge states and distinct edge electrocatalytic activity, emerging as a promising candidate for versatile applications in various fields. However, the direct synthesis is still challenging and the growth mechanism is still unclear. Herein, the arrayed growth of ultrathin PtSe2 ribbons on bunched vicinal Au(001) facets, via a facile chemical vapor deposition (CVD) route is reported. The ultrathin PtSe2 flakes can transform from traditional irregular shapes to desired ribbon shapes by increasing the height of bunched and unidirectionally oriented Au steps (with step height hstep) is found. This crossover, occurring at hstep ≈ 3.0 nm, defines the tailored growth from step-flow to single-terrace-confined modes, as validated by density functional theory calculations of the different system energies. On the millimeter-scale single-crystal Au(001) films with aligned steps, the arrayed ultrathin PtSe2 ribbons with tunable width of ≈20-1000 nm, which are then served as prototype electrocatalysts for hydrogen evolution reaction (HER) is achieved. This work should represent a huge leap in the direct synthesis and the mechanism exploration of arrayed ultrathin transition-metal dichalcogenides (TMDCs) ribbons, which should stimulate further explorations of the edge-related physical properties and practical applications.

Disruption of TIGAR-TAK1 alleviates immunopathology in a murine model of sepsis.

Macrophage-orchestrated inflammation contributes to multiple diseases including sepsis. However, the underlying mechanisms remain to be defined clearly. Here, we show that macrophage TP53-induced glycolysis and apoptosis regulator (TIGAR) is up-regulated in murine sepsis models. When myeloid Tigar is ablated, sepsis induced by either lipopolysaccharide treatment or cecal ligation puncture in male mice is attenuated via inflammation inhibition. Mechanistic characterizations indicate that TIGAR directly binds to transforming growth factor ß-activated kinase (TAK1) and promotes tumor necrosis factor receptor-associated factor 6-mediated ubiquitination and auto-phosphorylation of TAK1, in which residues 152-161 of TIGAR constitute crucial motif independent of its phosphatase activity. Interference with the binding of TIGAR to TAK1 by 5Z-7-oxozeaenol exhibits therapeutic effects in male murine model of sepsis. These findings demonstrate a non-canonical function of macrophage TIGAR in promoting inflammation, and confer a potential therapeutic target for sepsis by disruption of TIGAR-TAK1 interaction.

Independent organelle and organelle-organelle interactions: essential mechanisms for malignant gynecological cancer cell survival.

Different eukaryotic cell organelles (e.g., mitochondria, endoplasmic reticulum, lysosome) are involved in various cancer processes, by dominating specific cellular activities. Organelles cooperate, such as through contact points, in complex biological activities that help the cell regulate energy metabolism, signal transduction, and membrane dynamics, which influence survival process. Herein, we review the current studies of mechanisms by which mitochondria, endoplasmic reticulum, and lysosome are related to the three major malignant gynecological cancers, and their possible therapeutic interventions and drug targets. We also discuss the similarities and differences of independent organelle and organelle-organelle interactions, and their applications to the respective gynecological cancers; mitochondrial dynamics and energy metabolism, endoplasmic reticulum dysfunction, lysosomal regulation and autophagy, organelle interactions, and organelle regulatory mechanisms of cell death play crucial roles in cancer tumorigenesis, progression, and response to therapy. Finally, we discuss the value of organelle research, its current problems, and its future directions.

MRI radiomics nomogram integrating postoperative adjuvant treatments in recurrence risk prediction for patients with early-stage cervical cancer.

BACKGROUND AND PURPOSE: Adjuvant treatments are valuable to decrease the recurrence rate and improve survival for early-stage cervical cancer patients (ESCC), Therefore, recurrence risk evaluation is critical for the choice of postoperative treatment. A magnetic resonance imaging (MRI) based radiomics nomogram integrating postoperative adjuvant treatments was constructed and validated externally to improve the recurrence risk prediction for ESCC. MATERIAL AND METHODS: 212 ESCC patients underwent surgery and adjuvant treatments from three centers were enrolled and divided into the training, internal validation, and external validation cohorts. Their clinical data, pretreatment T2-weighted images (T2WI) were retrieved and analyzed. Radiomics models were constructed using machine learning methods with features extracted and screen from sagittal and axial T2WI. A nomogram for recurrence prediction was build and evaluated using multivariable logistic regression analysis integrating radiomic signature and adjuvant treatments. RESULTS: A total of 8 radiomic features were screened out of 1020 extracted features. The extreme gradient boosting (XGboost) model based on MRI radiomic features performed best in recurrence prediction with an area under curve (AUC) of 0.833, 0.822 in the internal and external validation cohorts, respectively. The nomogram integrating radiomic signature and clinical factors achieved an AUC of 0.806, 0.718 in the internal and external validation cohorts, respectively, for recurrence risk prediction for ESCC. CONCLUSION: In this study, the nomogram integrating T2WI radiomic signature and clinical factors is valuable to predict the recurrence risk, thereby allowing timely planning for effective treatments for ESCC with high risk of recurrence.

Identifying the Pattern Characteristics of Anoikis-related Genes in Keloid.

OBJECTIVE: Anoikis, a kind of programmed cell death that is triggered when cells lose contact with each other or with the matrix. However, the potential value of anoikis-related genes (ARGs) in keloid (KD) has not been investigated. APPROACH: We downloaded three keloid fibroblast (KF) RNA-seq datasets from the GEO and obtained 338 ARGs from a search of the GeneCards database and PubMed articles. WGCNA was used to construct the coexpression network, and obtain the KF-related ARGs. The LASSO-Cox method was used to screen the hub ARGs and construct the best prediction model. Then, GEO single cell sequencing datasets were used to verify the expression of hub genes. We used whole RNA sequencing for gene-level validation, and the correlation between KD immune infiltration and anoikis. RESULTS: Our study comprehensively analyzed the role of ARGs in KD for the first time. LASSO regression analysis identified six hub ARGs (HIF1A, SEMA7A, SESN1, CASP3, LAMA3 and SIK2). A large number of miRNAs participate in the regulation of hub ARGs. In addition, correlation analysis revealed that ARGs were significantly correlated with the infiltration levels of multiple immune cells in KD patients. Innovation We explored the expression characteristics of ARGs in KD, which is extremely important for determining the molecular pathways and mechanisms underlying KD. CONCLUSIONS: This study provides a useful reference for revealing the characteristics of ARGs in the pathogenesis of KD. The identified hub genes may provide potential therapeutic targets for patients. This study provides new ideas for individualized therapy and immunotherapy.

Solar-Driven Drum-Type Atmospheric Water Harvester Based on Bio-Based Gels with Fast Adsorption/Desorption Kinetics.

Sorption-based atmospheric water harvesting is an attractive technology for exploiting unconventional water sources. A critical challenge is how to facilitate fast and continuous collection of potable water from air. Here, a bio-based gel (cellulose/alginate/lignin gel, CAL gel), resulting from the integration of a whole biomass-derived polymer network with lithium chloride is reported. A fast adsorption/desorption kinetics, with a water capture rate of 1.74 kg kg-1 h-1 at 30% relative humidity and a desorption rate of 1.98 kg kg-1 h-1, is simultaneously realized in one piece of CAL gel, because of its strong hygroscopicity, hydrophilic network, abundant water transport channels, photothermal conversion ability, and ≈200-µm-thick self-supporting bulky structure caused by multicomponent synergy. A solar-driven, drum-type, tunable, and portable harvester is designed that can harvest atmospheric water within a brief time. Under outdoor conditions, the harvester with CAL gels operates 36 switches (180°) per day realizes a water yield of 8.96 kg kggel -1 (18.87 g kgdevice -1). This portable harvester highlights the potential for fast and scalable atmospheric water harvesting in extreme environments.

A Lignin Silver Nanoparticles/Polyvinyl Alcohol/Sodium Alginate Hybrid Hydrogel with Potent Mechanical Properties and Antibacterial Activity.

Antibacterial hydrogels have attracted significant attention due to their diverse applications, efficient antimicrobial properties, and adaptability to various environments and requirements. However, their relatively fragile structure, coupled with the potential for environmental toxicity when exposed to their surroundings for extended periods, may significantly limit their practical application potential. In this work, a composite hydrogel was synthesized with outstanding mechanical features and antibacterial capability. The hydrogel was developed through the combination of the eco-friendly and enduring antibacterial agent, lignin silver nanoparticles (Lig-Ag NPs), with polyvinyl alcohol (PVA) and sodium alginate (SA), in varying proportions. The successful synthesis of the hydrogel and the dispersed distribution of Lig-Ag NPs within the hydrogel were confirmed by various analytical techniques, including field emission scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), mercury intrusion porosimetry (MIP), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The formation of multiple hydrogen bonds between Lig-Ag NPs and the composites contributed to a more stable and dense network structure of the hydrogel, consequently enhancing its mechanical properties. Rheological tests revealed that the hydrogel exhibited an elastic response and demonstrated outstanding self-recovery properties. Significantly, the antibacterial hydrogel demonstrated effectiveness against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), achieving a <5% survival of bacteria within 12 h. This study presented a green and straightforward synthetic strategy for the application of antibacterial composite hydrogels in various fields.

Single-crystal oxygen-rich bismuth oxybromide nanosheets with highly exposed defective {10-1} facets for the selective oxidation of toluene under blue LED irradiation.

Reactive radicals are crucial for activating inert and low-polarity C(sp3)-H bonds for the fabrication of high value-added products. Herein, novel single-crystal oxygen-rich bismuth oxybromide nanosheets (Bi4O5Br2 SCNs) with more than 85 % {10-1} facets exposure and oxygen defects were synthesized via a facile solvothermal route. The Bi4O5Br2 SCNs demonstrated excellent photocatalytic performance in the selective oxidation of toluene under blue light. The yield of benzaldehyde was 1876.66 µmol g-1 h-1, with a selectivity of approximately 90 %. Compared to that of polycrystalline Bi4O5Br2 nanosheets (Bi4O5Br2 PCNs), the activity of Bi4O5Br2 SCNs exhibit a 21-fold increase. Experimental studies and density functional theory (DFT) calculations have demonstrated that the defect Bi4O5Br2 (10-1) facets exhibits exceptional adsorption properties for O2 molecules. In addition, the single-crystal structure in the presence of surface defects significantly increases the separation and transport of photogenerated carriers, resulting in the effective activation of adsorbed O2 into superoxide radicals (•O2-). Subsequently, the positively charged phenylmethyl H is readily linked to the negatively charged superoxide radical anion, thereby activating the CH bond. This study offers a fresh perspective and valuable insights into the development of efficient molecular oxygen-activated photocatalysts and their application in the selective catalytic conversion of aromatic C(sp3)-H bonds.

Ultrafast light-driven metasurfaces with an ultra-broadband frequency agile channel for sensing.

Active terahertz metasurface devices have been widely used in communication technology, optical computing and biosensing. However, numerous dynamically tunable metasurfaces are only operating at a single frequency point or in a narrow range, limiting the further possibility of the devices to meet contemporary broad-spectrum biosensing requirements. In this paper, a novel compact biosensor is proposed with an ultrawide resonance frequency agile channel shifted from 0.82 to 1.85 THz, with a tuning functionality up to 55.7%. In addition, under optical pumping irradiation, the modulator with ultra-fast response is able to complete the ultra-wideband resonant mode conversion from the Fano mode to the electromagnetically induced transparency (EIT) mode within 4 ps, and achieves a frequency shift sensitivity of 118 GHz RIU-1 and 247 GHz RIU-1 at 0.82 and 1.85 THz, respectively. This mechanism implements both refractive index and conductivity sensing functions, which provide a wealth of sensing information. Thus, this work presents the possibility of realising the detection of ultra-wide fingerprint spectra and can be extended to a wider range of optical fields.