Heterogeneous CNF/MoO3 nanofluidic membranes with tunable surface plasmon resonances for solar-osmotic energy conversion.

Two-dimensional (2D) nanofluidic membranes are competitive candidates for osmotic energy harvesting and have been greatly developed. However, the use of diverse inherent characteristics of 2D nanosheets, such as electronic or optoelectronic properties, to achieve intelligent ion transport, still lacks sufficient exploration. Here, a cellulose nanofiber/molybdenum oxide (CNF/MoO3) heterogeneous nanofluidic membrane with high performance solar-osmotic energy conversion is reported, and how surface plasmon resonances (SPR) regulate selective cation transport is revealed. The SPR of amorphous MoO3 endows the heterogeneous nanofluidic membranes with tunable surface charge and good photothermal conversion. Through DFT calculations and finite element modeling, the regulation of electronic and optoelectronic properties on the surface of materials by SPR and the influence of surface charge density and temperature gradient on ion transport in nanofluidic membranes are demonstrated. By mixing 0.01/0.5 M NaCl solutions using SPR and photothermal effects, the power density can achieve a remarkable value of ≈13.24 W m-2, outperforming state-of-the-art 2D-based nanofluidic membranes. This work first reveals the regulation and mechanism of SPR on ion transport in nanofluidic membranes and systematically studies photon-electron-ion interactions in nanofluidic membranes, which could also provide a new viewpoint for promoting osmotic energy conversion.

Recognition of Human Lower Limb Motion and Muscle Fatigue Status Using a Wearable FES-sEMG System.

Functional electrical stimulation (FES) devices are widely employed for clinical treatment, rehabilitation, and sports training. However, existing FES devices are inadequate in terms of wearability and cannot recognize a user's intention to move or muscle fatigue. These issues impede the user's ability to incorporate FES devices into their daily life. In response to these issues, this paper introduces a novel wearable FES system based on customized textile electrodes. The system is driven by surface electromyography (sEMG) movement intention. A parallel structured deep learning model based on a wearable FES device is used, which enables the identification of both the type of motion and muscle fatigue status without being affected by electrical stimulation. Five subjects took part in an experiment to test the proposed system, and the results showed that our method achieved a high level of accuracy for lower limb motion recognition and muscle fatigue status detection. The preliminary results presented here prove the effectiveness of the novel wearable FES system in terms of recognizing lower limb motions and muscle fatigue status.

An improved semantic segmentation algorithm for high-resolution remote sensing images based on DeepLabv3.

High-precision and high-efficiency Semantic segmentation of high-resolution remote sensing images is a challenge. Existing models typically require a significant amount of training data to achieve good classification results and have numerous training parameters. A novel model called MST-DeepLabv3+ was suggested in this paper for remote sensing image classification. It's based on the DeepLabv3+ and can produce better results with fewer train parameters. MST-DeepLabv3+ made three improvements: (1) Reducing the number of model parameters by substituting MobileNetV2 for the Xception in the DeepLabv3+'s backbone network. (2) Adding the attention mechanism module SENet to increase the precision of semantic segmentation. (3) Increasing Transfer Learning to enhance the model's capacity to recognize features, and raise the segmentation accuracy. MST-DeepLabv3+ was tested on international society for photogrammetry and remote sensing (ISPRS) dataset, Gaofen image dataset (GID), and practically applied to the Taikang cultivated land dataset. On the ISPRS dataset, the mean intersection over union (MIoU), overall accuracy (OA), Precision, Recall, and F1-score are 82.47%, 92.13%, 90.34%, 90.12%, and 90.23%, respectively. On the GID dataset, these values are 73.44%, 85.58%, 84.10%, 84.86%, and 84.48%, respectively. The results were as high as 90.77%, 95.47%, 95.28%, 95.02%, and 95.15% on the Taikang cultivated land dataset. The experimental results indicate that MST-DeepLabv3+ effectively improves the accuracy of semantic segmentation of remote sensing images, recognizes the edge information with more completeness, and significantly reduces the parameter size.

Using Network Analysis and Predictive Functional Analysis to Explore the Fluorotelomer Biotransformation Potential of Soil Microbial Communities.

Microbial transformation of per- and polyfluoroalkyl substances (PFAS), including fluorotelomer-derived PFAS, by native microbial communities in the environment has been widely documented. However, few studies have identified the key microorganisms and their roles during the PFAS biotransformation processes. This study was undertaken to gain more insight into the structure and function of soil microbial communities that are relevant to PFAS biotransformation. We collected 16S rRNA gene sequencing data from 8:2 fluorotelomer alcohol and 6:2 fluorotelomer sulfonate biotransformation studies conducted in soil microcosms under various redox conditions. Through co-occurrence network analysis, several genera, including Variovorax, Rhodococcus, and Cupriavidus, were found to likely play important roles in the biotransformation of fluorotelomers. Additionally, a metagenomic prediction approach (PICRUSt2) identified functional genes, including 6-oxocyclohex-1-ene-carbonyl-CoA hydrolase, cyclohexa-1,5-dienecarbonyl-CoA hydratase, and a fluoride-proton antiporter gene, that may be involved in defluorination. This study pioneers the application of these bioinformatics tools in the analysis of PFAS biotransformation-related sequencing data. Our findings serve as a foundational reference for investigating enzymatic mechanisms of microbial defluorination that may facilitate the development of efficient microbial consortia and/or pure microbial strains for PFAS biotransformation.

Automated measurement and grading of knee cartilage thickness: a deep learning-based approach.

Background: Knee cartilage is the most crucial structure in the knee, and the reduction of cartilage thickness is a significant factor in the occurrence and development of osteoarthritis. Measuring cartilage thickness allows for a more accurate assessment of cartilage wear, but this process is relatively time-consuming. Our objectives encompass using various DL methods to segment knee cartilage from MRIs taken with different equipment and parameters, building a DL-based model for measuring and grading knee cartilage, and establishing a standardized database of knee cartilage thickness. Methods: In this retrospective study, we selected a mixed knee MRI dataset consisting of 700 cases from four datasets with varying cartilage thickness. We employed four convolutional neural networks-UNet, UNet++, ResUNet, and TransUNet-to train and segment the mixed dataset, leveraging an extensive array of labeled data for effective supervised learning. Subsequently, we measured and graded the thickness of knee cartilage in 12 regions. Finally, a standard knee cartilage thickness dataset was established using 291 cases with ages ranging from 20 to 45 years and a Kellgren-Lawrence grading of 0. Results: The validation results of network segmentation showed that TransUNet performed the best in the mixed dataset, with an overall dice similarity coefficient of 0.813 and an Intersection over Union of 0.692. The model's mean absolute percentage error for automatic measurement and grading after segmentation was 0.831. The experiment also yielded standard knee cartilage thickness, with an average thickness of 1.98 mm for the femoral cartilage and 2.14 mm for the tibial cartilage. Conclusion: By selecting the best knee cartilage segmentation network, we built a model with a stronger generalization ability to automatically segment, measure, and grade cartilage thickness. This model can assist surgeons in more accurately and efficiently diagnosing changes in patients' cartilage thickness.

Application of fixed-frequency ultrasound in the cultivation of Saccharomyces cerevisiae for rice wine fermentation.

BACKGROUND: Rice wine (RW) fermentation is limited by its long fermentation time, weak taste and unpleasant flavors such as oil and odor. In this study, a novel ultrasound technology of Saccharomyces cerevisiae was used with the aim of improving fermentation efficiency and volatile flavor quality of RW. RESULTS: The results showed that fixed-frequency ultrasonic treatment (28 kHz, 45 W L-1, 20 min) of S. cerevisiae seed culture at its logarithmic metaphase significantly increased the biomass and alcohol yield by 31.58% and 26.45%, respectively, and reduced fermentation time by nearly 2 days. Flavor analysis indicated that the flavor compounds in RW, specifically the esters and alcohols, were also increased in quantity after the ultrasonic treatment of S. cerevisiae seed liquid. Isobutyl acetate, ethyl butyrate, ethyl hexanoate and phenethyl acetate contents were increased by 78.92%, 129.19%, 7.79% and 97.84%, respectively, as compared to the control. CONCLUSION: Ultrasonic treatment of S. cerevisiae reduced fermentation time and enhanced the flavor profile of RW. This study could provide a theoretical and/or technological basis for the research and development of RW. © 2024 Society of Chemical Industry.

A review of the occurrence and microbial transformation of per- and polyfluoroalkyl substances (PFAS) in aqueous film-forming foam (AFFF)-impacted environments.

Aqueous film-forming foams (AFFFs) have been extensively used for extinguishing hydrocarbon-fuel fires at military sites, airports, and fire-training areas. Despite being a significant source of per- and polyfluoroalkyl substances (PFAS), our understanding of PFAS occurrence in AFFF formulations and AFFF-impacted environments is limited, as is the impact of microbial transformation on the environment fate of AFFF-derived PFAS. This literature review compiles PFAS concentrations in electrochemical fluorination (ECF)- and fluorotelomer (FT)-based AFFFs and provides an overview of PFAS occurrence in AFFF-impacted environments. Our analysis reveals that AFFF use is a predominant point source of PFAS contamination, including primary precursors (polyfluoroalkyl substances as AFFF components), secondary precursors (polyfluoroalkyl transformation products of primary precursors), and perfluoroalkyl acids (PFAAs). Moreover, there are discrepancies between PFAS concentration profiles in AFFFs and those measured in AFFF-impacted media. For example, primary precursors constitute 52.6 % and 99.5 % of PFAS mass in ECF- and FT-based AFFFs, respectively, whereas they represent only 0.7 % total mass in AFFF-impacted groundwater. Conversely, secondary precursors, which constitute <1 % of PFAS in AFFFs, represent 4.0-27.8 % of PFAS in AFFF-impacted environments. The observed differences in PFAS levels between AFFFs and environmental samples are likely due to in-situ biotransformation processes. Biotransformation rates and pathways reported for AFFF-derived primary and secondary precursors varied among different classes of precursors, consistent with the PFAS occurrence in AFFF-impacted environments. For example, readily biodegradable primary precursors, N-dimethyl ammonio propyl perfluoroalkane sulfonamide (AmPr-FASA) and n:2 fluorotelomer thioether amido sulfonate (n:2 FtTAoS), were rarely detected in AFFF-impacted environments. In contrast, key secondary precursors, perfluoroalkane sulfonamides (FASAs) and n:2 fluorotelomer sulfonate (n:2 FTS), were widely detected, which was attributed to their resistance to biotransformation. Key knowledge gaps and future research priorities are presented to better understand the occurrence, fate, and transport of AFFF-derived PFAS in the environment and to design more effective remediation strategies.

Interfacial Bonding Induced Charge Transfer in Two-Dimensional Amorphous MoO3-x/Graphdiyne Oxide Non-Van der Waals Heterostructures for Dominant SERS Enhancement.

Two-dimensional semiconductor-based nanomaterials have shown to be an effective substrate for Surface-enhanced Raman Scattering (SERS) spectroscopy. However, the enhancement factor (EF) tends to be relatively weak compared to that of noble metals and does not allow for trace detection of molecules. In this work, we report the successful preparation of two-dimensional (2D) amorphous non-van der Waals heterostructures MoO3-x/GDYO nanomaterials using supercritical CO2. Due to the synergistic effect of the localized surface plasmon resonance (LSPR) effect and the charge transfer effect, it exhibits excellent SERS performance in the detection of methylene blue (MB) molecules, with a detection limit as low as 10-14 M while the enhancement factor (EF) can reach an impressive 2.55×1011. More importantly, the chemical bond bridging at the MoO3-x/GDYO heterostructures interface can accelerate the electron transfer between the interfaces, and the large number of defective surface structures on the heterostructures surface facilitates the chemisorption of MB molecules. And the charge recombination lifetime can be proved by a ~1.7-fold increase during their interfacial electron-transfer process for MoO3-x/GDYO@MB mixture, achieving highly sensitive SERS detection.

Biotransformation of 6:2 fluorotelomer sulfonate and microbial community dynamics in water-saturated one-dimensional flow-through columns.

Nearly all per- and polyfluoroalkyl substances (PFAS) biotransformation studies reported to date have been limited to laboratory-scale batch reactors. The fate and transport of PFAS in systems that more closely represent field conditions, i.e., in saturated porous media under flowing conditions, remain largely unexplored. This study investigated the biotransformation of 6:2 fluorotelomer sulfonate (6:2 FTS), a representative PFAS of widespread environmental occurrence, in one-dimensional water-saturated flow-through columns packed with soil obtained from a PFAS-contaminated site. The 305-day column experiments demonstrated that 6:2 FTS biotransformation was rate-limited, where a decrease in pore-water velocity from 3.7 to 2.4 cm/day, resulted in a 21.7-26.1 % decrease in effluent concentrations of 6:2 FTS and higher yields (1.0-1.4 mol% vs. 0.3 mol%) of late-stage biotransformation products (C4C7 perfluoroalkyl carboxylates). Flow interruptions (2 and 7 days) were found to enhance 6:2 FTS biotransformation during the 6-7 pore volumes following flow resumption. Model-fitted 6:2 FTS column biotransformation rates (0.039-0.041 cmw3/gs/d) were ∼3.5 times smaller than those observed in microcosms (0.137 cmw3/gs/d). Additionally, during column experiments, planktonic microbial communities remained relatively stable, whereas the composition of the attached microbial communities shifted along the flow path, which may have been attributed to oxygen availability and the toxicity of 6:2 FTS and associated biotransformation products. Genus Pseudomonas dominated in planktonic microbial communities, while in the attached microbial communities, Rhodococcus decreased and Pelotomaculum increased along the flow path, suggesting their potential involvement in early- and late-stage 6:2 FTS biotransformation, respectively. Overall, this study highlights the importance of incorporating realistic environmental conditions into experimental systems to obtain a more representative assessment of in-situ PFAS biotransformation.

RegGAN: A Virtual Sample Generative Network for Developing Soft Sensors with Small Data.

Quality variables play a pivotal role in monitoring the performance of chemical production systems. However, certain critical quality variables cannot be measured online through instruments. In such scenarios, using soft sensors becomes imperative to enable real-time measurements, accurately reflecting the system's operational status. The development of high-performance soft sensors requires abundantly labeled samples. Nevertheless, the prolonged periods and substantial costs associated with acquiring quality variable data pose challenges in obtaining sufficient labeled samples. Therefore, this paper proposes a regression generative adversarial network to generate virtual samples. The proposed method considers the mapping relationship between auxiliary and target variables while learning the data distribution. Moreover, the importance-weighted autoencoder is introduced to enhance the training stability of the generative model. The virtual samples, selected by using the similarity measurement algorithm, are incorporated into the training set. This inclusion addresses the diminished predictive performance of soft sensors when labeled samples are insufficient. The soft sensor employed in the anaerobic digestion process serves as a case study to illustrate the efficacy of the proposed generative method. Experimental results validate that the virtual samples generated by the proposed method exhibit greater proximity to the actual samples compared to those of other methods. Furthermore, integrating virtual samples into the training process of the long short-term memory-based soft sensor yields a 21.03% reduction in root-mean-square error compared with that of using the original training set alone.

Mir-338-3p targeting THBS1 attenuates glioma progression by inhibiting the PI3K/Akt pathway.

BACKGROUND: Glioma is a brain tumor with high morbidity and mortality rates. Understanding its molecular pathogenesis can provide targets and therapeutic strategies for glioma treatment. miR-338-3p represses tumor growth in several cancers, including glioma. Thus, this study aimed to identify the regulatory effects of miR-338-3p/phosphoinositide 3-kinase (PI3K)/Akt/thrombospondins 1 (THBS1) on glioma progression. MATERIALS AND METHODS: Quantitative reverse transcription polymerase chain reaction and western blotting were performed to evaluate the levels of miR-338-3p, THBS1, and PI3K/Akt phosphorylation-related proteins. TargetScan software predicted that miR-338-3p targeted THBS1. This was confirmed by performing the dual-luciferase assay. Wound-healing and cell-counting-kit-8 experiments were performed to analyze how THBS1 and miR-338-3p affect the ability of glioma cells to migrate and proliferate. The effect of miR-338-3p on tumorigenicity in mice was also analyzed. RESULTS: miR-338-3p downregulation was observed in gliomas, whereas THBS1 showed the opposite trend. By suppressing the PI3K/Akt signaling pathway activation, miR-338-3p overregulated the ability of glioma cells to migrate and proliferate in vitro. Additionally, miR-338-3p inhibited the development of glioma tumors in vivo. Moreover, miR-338-3p directly targeted THBS1. THBS1 overexpression promoted glioma cell migration and proliferation by increasing PI3K/Akt phosphorylation. Nonetheless, miR-338-3p overregulation alleviated the effects of THBS1 overexpression. CONCLUSION: The miR-338-3p/PI3K/Akt/THBS1 regulatory axis can modulate the progression of glioma cell proliferation and migration; thus, it can be considered a therapeutic biomarker.

Aerobic biotransformation of 6:2 fluorotelomer sulfonate in soils from two aqueous film-forming foam (AFFF)-impacted sites.

Although 6:2 fluorotelomer sulfonate (6:2 FTS) is a common ingredient in aqueous film-forming foam (AFFF) formulations, its environmental fate at AFFF-impacted sites remains poorly understood. This study investigated the biotransformation of 6:2 FTS in microcosms prepared with soils collected from two AFFF-impacted sites; the former Loring Air Force Base (AFB) and Robins AFB. The half-life of 6:2 FTS in Loring soil was 43.3 days; while >60 mol% of initially spiked 6:2 FTS remained in Robins soil microcosms after a 224-day incubation. Differences in initial sulfate concentrations and the depletion of sulfate over the incubation likely contributed to the different 6:2 FTS biotransformation rates between the two soils. At day 224, stable transformation products, i.e., C4C7 perfluoroalkyl carboxylates, were formed with combined molar yields of 13.8 mol% and 1.2 mol% in Loring and Robins soils, respectively. Based on all detected transformation products, the biotransformation pathways of 6:2 FTS in the two soils were proposed. Microbial community analysis suggests that Desulfobacterota microorganisms may promote 6:2 FTS biotransformation via more efficient desulfonation. In addition, species from the genus Sphingomonas, which exhibited higher tolerance to elevated concentrations of 6:2 FTS and its biotransformation products, are likely to have contributed to 6:2 FTS biotransformation. This study demonstrates the potential role of biotransformation processes on the fate of 6:2 FTS at AFFF-impacted sites and highlights the need to characterize site biogeochemical properties for improved assessment of 6:2 FTS biotransformation behavior.

Multi-parametric MRI-based machine learning model for prediction of WHO grading in patients with meningiomas.

OBJECTIVE: The purpose of this study was to develop and validate a nomogram combined multiparametric MRI and clinical indicators for identifying the WHO grade of meningioma. MATERIALS AND METHODS: Five hundred and sixty-eight patients were included in this study, who were diagnosed pathologically as having meningiomas. Firstly, radiomics features were extracted from CE-T1, T2, and 1-cm-thick tumor-to-brain interface (BTI) images. Then, difference analysis and the least absolute shrinkage and selection operator were orderly used to select the most representative features. Next, the support vector machine algorithm was conducted to predict the WHO grade of meningioma. Furthermore, a nomogram incorporated radiomics features and valuable clinical indicators was constructed by logistic regression. The performance of the nomogram was assessed by calibration and clinical effectiveness, as well as internal validation. RESULTS: Peritumoral edema volume and gender are independent risk factors for predicting meningioma grade. The multiparametric MRI features incorporating CE-T1, T2, and BTI features showed the higher performance for prediction of meningioma grade with a pooled AUC = 0.885 (95% CI, 0.821-0.946) and 0.860 (95% CI, 0.788-0.923) in the training and test groups, respectively. Then, a nomogram with a pooled AUC = 0.912 (95% CI, 0.876-0.961), combined radiomics score, peritumoral edema volume, and gender improved diagnostic performance compared to radiomics model or clinical model and showed good calibration as the true results. Moreover, decision curve analysis demonstrated satisfactory clinical effectiveness of the proposed nomogram. CONCLUSIONS: A novel nomogram is simple yet effective in differentiating WHO grades of meningioma and thus can be used in patients with meningiomas. CLINICAL RELEVANCE STATEMENT: We proposed a nomogram that included clinical indicators and multi-parameter radiomics features, which can accurately, objectively, and non-invasively differentiate WHO grading of meningioma and thus can be used in clinical work. KEY POINTS: • The study combined radiomics features and clinical indicators for objectively predicting the meningioma grade. • The model with CE-T1 + T2 + brain-to-tumor interface features demonstrated the best predictive performance by investigating seven different radiomics models. • The nomogram potentially has clinical applications in distinguishing high-grade and low-grade meningiomas.

SDA-Net: Self-distillation driven deformable attentive aggregation network for thyroid nodule identification in ultrasound images.

Early detection and accurate identification of thyroid nodules are the major challenges in controlling and treating thyroid cancer that can be difficult even for expert physicians. Currently, many computer-aided diagnosis (CAD) systems have been developed to assist this clinical process. However, most of these systems are unable to well capture geometrically diverse thyroid nodule representations from ultrasound images with subtle and various characteristic differences, resulting in suboptimal diagnosis and lack of clinical interpretability, which may affect their credibility in the clinic. In this context, a novel end-to-end network equipped with a deformable attention network and a distillation-driven interaction aggregation module (DIAM) is developed for thyroid nodule identification. The deformable attention network learns to identify discriminative features of nodules under the guidance of the deformable attention module (DAM) and an online class activation mapping (CAM) mechanism and suggests the location of diagnostic features to provide interpretable predictions. DIAM is designed to take advantage of the complementarities of adjacent layers, thus enhancing the representation capabilities of aggregated features; driven by an efficient self-distillation mechanism, the identification process is complemented with more multi-scale semantic information to calibrate the diagnosis results. Experimental results on a large dataset with varying nodule appearances show that the proposed network can achieve competitive performance in nodule diagnosis and provide interpretability suitable for clinical needs.

PKDN: Prior Knowledge Distillation Network for bronchoscopy diagnosis.

Bronchoscopy plays a crucial role in diagnosing and treating lung diseases. The deep learning-based diagnostic system for bronchoscopic images can assist physicians in accurately and efficiently diagnosing lung diseases, enabling patients to undergo timely pathological examinations and receive appropriate treatment. However, the existing diagnostic methods overlook the utilization of prior knowledge of medical images, and the limited feature extraction capability hinders precise focus on lesion regions, consequently affecting the overall diagnostic effectiveness. To address these challenges, this paper proposes a prior knowledge distillation network (PKDN) for identifying lung diseases through bronchoscopic images. The proposed method extracts color and edge features from lesion images using the prior knowledge guidance module, and subsequently enhances spatial and channel features by employing the dynamic spatial attention module and gated channel attention module, respectively. Finally, the extracted features undergo refinement and self-regulation through feature distillation. Furthermore, decoupled distillation is implemented to balance the importance of target and non-target class distillation, thereby enhancing the diagnostic performance of the network. The effectiveness of the proposed method is validated on the bronchoscopic dataset provided by Harbin Medical University Cancer Hospital, which consists of 2,029 bronchoscopic images from 200 patients. Experimental results demonstrate that the proposed method achieves an accuracy of 94.78% and an AUC of 98.17%, outperforming other methods significantly in diagnostic performance. These results indicate that the computer-aided diagnostic system based on PKDN provides satisfactory accuracy in diagnosing lung diseases during bronchoscopy.

Enhancing the growth of thermophilic Bacillus licheniformis YYC4 by low-intensity fixed-frequency continuous ultrasound.

The effect of low-intensity fixed-frequency continuous ultrasound (LIFFCU) on the growth of Bacillus licheniformis YYC4 was investigated. The changes in morphology and activity of the organism, contributing to the growth were also explored. Compared with the control, a significant increase (48.95%) in the biomass of B. licheniformis YYC4 (at the logarithmic metaphase) was observed following the LIFFCU (28 kHz, 1.5 h and 120 W (equivalent to power density of 40 W/L)) treatment. SEM images showed that ultrasonication caused sonoporation, resulting in increased membrane permeability, evidenced by increase in cellular membrane potential, electrical conductivity of the culture, extracellular protein and nucleic acid, and intracellular Ca2+ content. Furthermore, LIFFCU action remarkably increased the extracellular protease activity, volatile components of the culture medium, microbial metabolic activity, and spore germination of the strain. Therefore, LIFFCU could be used to efficiently promote the growth of targeted microorganisms.

Porphyrin-based Bi-MOFs with Enriched Surface Bi Active Sites for Boosting Photocatalytic CO2 Reduction.

The inherent challenges in using metal-organic frameworks (MOFs) for photocatalytic CO2 reduction are the combination of wide-range light harvesting, efficient charge separation and transfer as well as highly exposed catalytic active sites for CO2 activation and reduction. We present here a promising solution to satisfy these requirements together by modulating the crystal facet and surface atomic structure of a porphyrin-based bismuth-MOF (Bi-PMOF). The series of structural and photo-electronic characterizations together with photocatalytic CO2 reduction experiment collectively establish that the enriched Bi active sites on the (010) surface prefer to promote efficient charge separation and transfer as well as the activation and reduction of CO2 . Specifically, the Bi-PMOFs-120-F with enriched surface Bi active sites exhibits optimal photocatalytic CO2 reduction performance to CO (28.61 µmol h-1 g-1 ) and CH4 (8.81 µmol h-1 g-1 ). This work provides new insights to synthesize highly efficient main group p-block metal Bi-MOF photocatalysts for CO2 reduction through a facet-regulation strategy and sheds light on the surface structure-activity relationships of the MOFs.

Sulfur dioxide-releasing polymeric micelles based on modified hyaluronic acid for combined cancer therapy.

In this study, an amphiphilic polymer mPEG-HA(SA)-DNs was designed and synthesized to fabricate a multifunctional micellar system to enhance the therapeutic efficacy and reduce the toxic effect of paclitaxel (PTX). The polymer was prepared by introducing mPEG, stearic acid (SA) and 2,4-dinitrobenzenesulfonic acid (DNs) to the backbone of hyaluronic acid (HA). With above modifications, the fabricated micelles could encapsulate PTX in the core with high drug loading. The optimized PTX-loaded micelles had a mean size of 158.3 nm. Upon the effect of mPEG, the mPEG-HA(SA)-DNs micelles reduced the non-specific protein adsorption. In vitro drug release study revealed the excellent glutathione (GSH)-triggered PTX release behavior of the micelles. Moreover, GSH could trigger the detachment of DNs segment from mPEG-HA(SA)-DNs, and result in the release of SO2. In vitro and in vivo antitumor efficacy studies demonstrated that the PTX-loaded mPEG-HA(SA)-DNs micelles exhibited outstanding tumor suppression effect. The micelles would be potential carriers for combination cancer therapy by SO2 and PTX.

Phosphine-Oxide-Balanced Intra- and Interchain Through-Space Charge Transfer in Thermally Activated Delayed Fluorescence Polymers: Beyond 30% External Quantum Efficiency.

Through-space charge transfer (TSCT) is crucial for developing highly efficient thermally activated delayed fluorescence polymers. The balance of intra- and interchain TSCT can markedly improve performance, but it is still a big challenge. In this work, an effective strategy for "intra- and interchain TSCT balance" is demonstrated by way of a series of non-conjugated copolymers containing a 9,9-dimethylacridine donor and triazine-phosphine oxide (PO)-based acceptors. Steady-state and transient emission spectra indicate that compared to the corresponding blends, the copolymers can indeed achieve balanced intra- and interchain TSCT by accurately optimizing the inductive and steric effects of the acceptors. The DPOT acceptor with the strongest electron-withdrawing ability and the second bigger steric hindrance endows its copolymers with state-of-the-art photoluminescence and electroluminescence quantum efficiencies beyond 95% and 32%, respectively. This demonstrates that, compared to other congeners, the synergistic inductive and steric effects effectively enhance TSCT in DPOT-based copolymers for radiation, and suppress singlet and triplet quenching. The record-high efficiencies of its devices make this kind of copolymers hold the potential for low-cost, large-scale, and high-efficiency applications.