Hydrogenated V2O5 with Improved Optical and Electrochemical Activities for Photo-Accelerated Lithium-Ion Batteries.

Solar power represents an abundant and readily available source of renewable energy. However, its intermittent nature necessitates external energy storage solutions, which can often be expensive, bulky, and associated with energy conversion losses. This study introduces the concept of a photo-accelerated battery that seamlessly integrates energy harvesting and storage functions within a single device. In this research, a novel approach for crafting photocathodes is presented using hydrogenated vanadium pentoxide (H:V2O5) nanofibers. This method enhances optical activity, electronic conductivity, and ion diffusion rates within photo-accelerated Li-ion batteries. This study findings reveal that H:V2O5 exhibits notable improvements in specific capacity under both dark and illuminated conditions. Furthermore, it demonstrates enhanced diffusion kinetics and charge storage performance when exposed to light, as compared to pristine counterparts. This strategy of defect engineering holds great promise for the development of high-performance photocathodes in future energy storage applications.

Bio-Inspired Polyanionic Electrolytes for Highly Stable Zinc-Ion Batteries.

For zinc-ion batteries (ZIBs), the non-uniform Zn plating/stripping results in a high polarization and low Coulombic efficiency (CE), hindering the large-scale application of ZIBs. Here, inspired by biomass seaweed plants, an anionic polyelectrolyte alginate acid (SA) was used to initiate the in situ formation of the high-performance solid electrolyte interphase (SEI) layer on the Zn anode. Attribute to the anionic groups of -COO- , the affinity of Zn2+ ions to alginate acid induces a well-aligned accelerating channel for uniform plating. This SEI regulates the desolvation structure of Zn2+ and facilitates the formation of compact Zn (002) crystal planes. Even under high depth of discharge conditions (DOD), the SA-coated Zn anode still maintains a stable Zn stripping/plating behavior with a low potential difference (0.114 V). According to the classical nucleation theory, the nucleation energy for SA-coated Zn is 97 % less than that of bare Zn, resulting in a faster nucleation rate. The Zn||Cu cell assembled with the SA-coated electrode exhibits an outstanding average CE of 99.8 % over 1,400 cycles. The design is successfully demonstrated in pouch cells, where the SA-coated Zn exhibits capacity retention of 96.9 % compared to 59.1 % for bare Zn anode, even under the high cathode mass loading (>10 mg/cm2 ).

Highly Reversible Zinc Metal Anode in a Dilute Aqueous Electrolyte Enabled by a pH Buffer Additive.

Aqueous zinc-ion batteries have drawn increasing attention due to the intrinsic safety, cost-effectiveness and high energy density. However, parasitic reactions and non-uniform dendrite growth on the Zn anode side impede their application. Herein, a multifunctional additive, ammonium dihydrogen phosphate (NHP), is introduced to regulate uniform zinc deposition and to suppress side reactions. The results show that the NH4 + tends to be preferably absorbed on the Zn surface to form a "shielding effect" and blocks the direct contact of water with Zn. Moreover, NH4 + and (H2 PO4 )- jointly maintain pH values of the electrode-electrolyte interface. Consequently, the NHP additive enables highly reversible Zn plating/stripping behaviors in Zn//Zn and Zn//Cu cells. Furthermore, the electrochemical performances of Zn//MnO2 full cells and Zn//active carbon (AC) capacitors are improved. This work provides an efficient and general strategy for modifying Zn plating/stripping behaviors and suppressing side reactions in mild aqueous electrolyte.

Rational phase transformation and morphology design to optimize oxygen evolution property of cobalt tungstate.

In this work, a facile and feasible soft template method with the aid of buffer solution is successfully applied to synthesize high-order mesoporous cobalt tungstate for the first time. Attributing to the regulation of reaction solution's pH value and the existence of template, the phenomenon of phase transformation occurs, and high-order mesoporous structure is formed. Because of the variation of phase and morphology, only 448 mV can deliver a current density of 10 mA cm-2 with a small Tafel slope (61 mV dec-1) for mesoporous cobalt tungsten oxide hydroxide, while the cobalt tungstate nanoparticles cannot satisfy the basic demand of electrocatalysts. Herein, rational phase transformation and morphology design can significantly affect the property of oxygen evolution, which can provide vast opportunities to turn into candidates for the novel oxygen evolution catalyst.

Novel Luminescent Multilayer Films with Magnetic and Electronic Microenvironment.

Luminescent multilayer thin films (MTFs) based on exfoliated magnetic layered double hydroxides (LDHs) with/without oppositely-charged montmorillonite (MMT) nanosheets were fabricated via layer-by-layer self-assembly method. In this work, we chose transition metal-bearing LDHs nanosheets to offer magnetic field for the chromophores. At the same time, the oppositely-charged nanosheets can provide additional electronic microenvironment (EME). As a result, both EME and magnetic field have remarkable influences on enhancing the luminescent lifetimes of chromophores, which suggests a new pathway to develop the novel light-emitting materials and optical devices.

Water Stability of Fibers-Enhanced Asphalt Mixtures under Static and Dynamic Damage Conditions.

Water damage is one of the major distresses of asphalt pavements. Existing methods for investigating the water stability of asphalt mixtures rely primarily on static water test methods, the tensile strength ratio (TSR) test, and the retained Marshall stability (RMS) test, which evaluate the strength and stability loss after freeze-thaw damage or hot water immersion, respectively. However, these methods do not accurately replicate the actual dynamic water damage conditions to pavement. Therefore, in this study, a variety of damage conditions, including static water conditions and dynamic water pressure conditions, were used to investigate the effects of lignin fibers (LFs), polyester fibers (PFs), and polypropylene fibers (PPFs) on the water stability of asphalt mixtures. First, three fibers-enhanced SMA gap-gradation asphalt mixtures were designed. Then, TSR and RMS were measured under traditional static water damage conditions and new dynamic water pressure damage conditions to evaluate the effect of fiber types on the water stability of asphalt mixtures. Finally, the void rate of asphalt mixtures and its changes under dynamic water damage conditions were further revealed with the help of CT scanning technique. Results showed that, among these three types of fibers, PFs-enhanced asphalt mixture exhibited excellent stability under both static and dynamic water conditions, and the CT scanning test also indicated that the PFs can significantly reduce the increase rate of voids in asphalt mixtures after dynamic water pressure damage. This study identified the potential of incorporating suitable type of fiber to enhance the performance of asphalt mixture under dynamic water pressure damage.

M-DDC: MRI based demyelinative diseases classification with U-Net segmentation and convolutional network.

Childhood demyelinative diseases classification (DDC) with brain magnetic resonance imaging (MRI) is crucial to clinical diagnosis. But few attentions have been paid to DDC in the past. How to accurately differentiate pediatric-onset neuromyelitis optica spectrum disorder (NMOSD) from acute disseminated encephalomyelitis (ADEM) based on MRI is challenging in DDC. In this paper, a novel architecture M-DDC based on joint U-Net segmentation network and deep convolutional network is developed. The U-Net segmentation can provide pixel-level structure information, that helps the lesion areas location and size estimation. The classification branch in DDC can detect the regions of interest inside MRIs, including the white matter regions where lesions appear. The performance of the proposed method is evaluated on MRIs of 201 subjects recorded from the Children's Hospital of Zhejiang University School of Medicine. The comparisons show that the proposed DDC achieves the highest accuracy of 99.19% and dice of 71.1% for ADEM and NMOSD classification and segmentation, respectively.

Atomically dispersed asymmetric cobalt electrocatalyst for efficient hydrogen peroxide production in neutral media.

Electrochemical hydrogen peroxide (H2O2) production (EHPP) via a two-electron oxygen reduction reaction (2e- ORR) provides a promising alternative to replace the energy-intensive anthraquinone process. M-N-C electrocatalysts, which consist of atomically dispersed transition metals and nitrogen-doped carbon, have demonstrated considerable EHPP efficiency. However, their full potential, particularly regarding the correlation between structural configurations and performances in neutral media, remains underexplored. Herein, a series of ultralow metal-loading M-N-C electrocatalysts are synthesized and investigated for the EHPP process in the neutral electrolyte. CoNCB material with the asymmetric Co-C/N/O configuration exhibits the highest EHPP activity and selectivity among various as-prepared M-N-C electrocatalyst, with an outstanding mass activity (6.1 × 105 A gCo-1 at 0.5 V vs. RHE), and a high practical H2O2 production rate (4.72 mol gcatalyst-1 h-1 cm-2). Compared with the popularly recognized square-planar symmetric Co-N4 configuration, the superiority of asymmetric Co-C/N/O configurations is elucidated by X-ray absorption fine structure spectroscopy analysis and computational studies.

Design and Gesture Optimization of a Soft-Rigid Robotic Hand for Adaptive Grasping.

Soft robotic hands are inherently safer and more compliant in robot-environment interaction than rigid manipulators, but their flexibility and versatility still need improving. In this article, a gesture adaptive soft-rigid robotic hand is proposed. The robotic hand has three pneumatic two-segment fingers. Each finger segment is driven independently for flexible gesture adjustment to match up with different object shapes. The palm is constructed by a rigid skeleton driven by a soft pneumatic spring. It provides a firm support, large workspace, and independent force control for the fingers. Geometry model of the robotic hand is established, based on which a grasping gesture optimization algorithm is adopted. The fingers achieve optimal contact with objects by performing maximal curving similarity with the object outlines. Experiment shows that the soft-rigid robotic hand provides adaptive and reliable grasping for objects of different sizes, shapes, and materials with optimized gestures.

The efficacy and toxicity of mirvetuximab soravtansine, a novel antibody-drug conjugate, in the treatment of advanced or recurrent ovarian cancer: a meta-analysis.

INTRODUCTION: This meta-analysis aims to systematically analyze the efficacy and toxicity of mirvetuximab soravtansine (MIRV) as second-line and above treatment for advanced or recurrent ovarian cancer. METHODS: Candidate studies were identified in PubMed, Embase, Cochrane Library, CNKI, and Wanfang databases up to 1 May 2023. Objective response rate (ORR), progression-free survival (PFS), the incidence of adverse events (AEs), and incidence of grade ≥ 3 AEs were extracted and calculated by meta-analysis of merging ratios or mean to describe the efficacy and toxicity of MIRV. RESULTS: Seven eligible prospective studies were included in this meta-analysis, including 605 patients with advanced ovarian cancer who received second-line or higher therapy. ORR of MIRV was 34.2% (95% confidence interval [CI] 25.0-43.5), and PFS was 5.82 months (95%CI 4.47-7.18). The overall incidence of AEs was 87.4% (95%CI 52.9-100.0) and the incidence of grade ≥ 3 AEs was 27.1% (95%CI 18.9-36.1). The most common AEs were vision blurring, nausea, and diarrhea, with incidence of 46.7% (39.6-53.8), 41.8% (34.0-49.9), and 41.3% (30.4-52.5), respectively. CONCLUSIONS: MIRV has definite efficacy and good safety as a novel choice for second-line and above treatment of advanced or recurrent FRα positive ovarian cancer. This may have promising application in patients with platinum-resistant diseases. PROSPERO REGISTRATION NUMBER: CRD42023428599.

Modification and Enhancing Contribution of Fiber to Asphalt Binders and Their Corresponding Mixtures: A Study of Viscoelastic Properties.

The performance of asphalt binders and asphalt mixtures can be enhanced by the inclusion of fiber. The viscoelastic characteristics of fiber-reinforced asphalt binders and their corresponding mixtures were characterized in this study. To generate fiber-reinforced asphalt samples for dynamic shear rheometer (DSR) tests, polypropylene fibers (PPFs), polyester fibers (PFs), and lignin fibers (LFs) were added into modified asphalt with a ratio of 5wt%. Indirect tensile resilience tests were conducted on the fiber-reinforced asphalt mixture with Marshall samples, which was prepared with a 6.4% of bitumen/aggregate ratio. The addition of fiber can increase the anti-rutting performance of asphalt binders, and also reduce the anti-fatigue performance of asphalt binders to varying degrees. Viscoelastic properties of the fiber-reinforced asphalt binders are highly dependent on the shape of the used fiber. The resistance of the fiber-reinforced asphalt binders to rutting at high temperatures increases with the roughness degree of the fiber's surface morphology. PPF-reinforced asphalt binders surpass the others in terms of anti-rutting capabilities. The high-temperature deformation resistance of the PPF-reinforced asphalt mixture is stronger, whereas the low-temperature crack resistance of the PF-reinforced asphalt mixture is stronger, which can be observed from the master curve of indirect tensile resilient modulus.

Research on Recycling of Phosphorus Tailings Powder in Open-Graded Friction Course Asphalt Concrete.

The reuse in high-value materials is one of the important resource utilization approaches of phosphorus tailings. At present, a mature technical system has been formed on the reuse of phosphorus slag in building materials, and silicon fertilizers in the extraction of yellow phosphorus. But there is a lack of research on the high-value reuse of phosphorus tailings. In order to make safe and effective utilization of phosphorus tailing resources, this research concentrated on how to solve easy agglomeration and difficult dispersion of phosphorus tailing micro-powder, when it was recycled in road asphalt. In the experimental procedure, phosphorus tailing micro-powder is treated in two methods. One method is to directly add it with different contents in asphalt to form a mortar. Dynamic shear tests were used to explore the effect of phosphorus tailing micro-powder on the high-temperature rheological properties of asphalt influence mechanism of material service behavior. The other method is to replace the mineral powder in asphalt mixture. The effect of phosphate tailing micro-powder on the water damage resistance in open-graded friction course (OGFC) asphalt mixtures was illustrated, based on the Marshall stability test and the freeze-thaw split test. The research results show that the performance indicators of the modified phosphorus tailing micro-powder meet the requirements for mineral powder in road engineering. Compared with standard OGFC asphalt mixtures, the residual stability of immersion and freeze-thaw splitting strength were improved when replace the mineral powder. The residual stability of immersion increased from 84.70% to 88.31%, and freeze-thaw splitting strength increased from 79.07% to 82.61%. The results indicate that phosphate tailing micro-powder has a certain positive effect on the water damage resistance. These performance improvements can be attributed to the larger specific surface area for phosphate tailing micro-powder than ordinary mineral powder, which can effectively adsorb asphalt and form structural asphalt. The research results are expected to support the large-scale reuse of phosphorus tailing powder in road engineering.

Fiber-Reinforced Asphalt Mixture Design on Anti-Skid Surfacing for Field Testing High-Speed Vehicles on Pavements.

Fiber can absorb asphalt binder and therefore reinforce and stabilize the asphalt mixture structure and also prevent the asphalt from the leaking, which occurs in the process of mixing and transport. In this study, three kinds of fiber (polyester fiber, polypropylene fiber, and lignin fiber) are used to evaluate the relationship between the fiber types and mechanic performance of SMA-13 fiber asphalt mixture, which is specially designed for field tests of high-speed vehicles on pavements. The micro-surface characteristics of fiber and aggregates were studied by SEM and image analysis. Marshall stability and splitting strength were used to measure the properties of the asphalt mixture. In addition, a field test, including measures for curve-section edge, curve-section center, straight-section edge, and straight-section center, was conducted to evaluate the skid resistance of the high-speed vehicles that test field pavement. The results show that the Marshall stabilities of asphalt mixture with three kinds of fibers have been improved, whereas the stability of asphalt mixture prepared by polypropylene fiber and polyester fiber particularly increased before immersion. Among the three kinds of fiber asphalt mixtures, the polyester fiber asphalt mixture has enhanced water susceptibility. Skid resistance in the field test indicated that high skid resistance and good surface-texture depth were achieved.

Differentiation of MOGAD in ADEM-like presentation children based on FLAIR MRI features.

OBJECTIVE: The differences in magnetic resonance imaging (MRI) between children with classic acute disseminated encephalomyelitis (ADEM) and myelinal oligodendrocyte glycoprotein antibody associated disease (MOGAD) with ADEM-like presentation are controversial. The purpose of this study was to investigate whether the radiological characteristics of the MRI-FLAIR sequence can predict MOGAD in children with ADEM-like presentation and to further explore its imaging differences. METHODS: We extracted 1041 radiomics features from MRI-FLAIR lesions. Then we used the redundancy analysis (Spearman correlation coefficient), significance test (student test or Mann-Whitney U test), least absolute contraction and selection operator (LASSO) to select potential predictors from the feature groups. The selected potential predictors and MOG antibody test results were used to fit the machine learning model for classification. Combined with feature selection and machine learning classifiers, the optimal model for each subgroup was derived. The resulting models have been evaluated using the receiver operator characteristic curve (ROC) at the lesion level and the model performance was evaluated at the case level using decision curve analysis. RESULTS: We retrospectively reviewed and re-diagnosed 70 ADEM-like presentation cases in our center from April 2015 to January 2020. Including 49 cases with classic ADEM and 21 cases with MOGAD. 30(43%) were female, with a median age of 5.3 years. On the four subgroups by age and gender, the area under the curve (AUC) of the optimal models were 89%, 90%, 98%, and 99%, and the MOGAD detection rates (Specificity) were 83%, 83%, 92%, and 75%, respectively. CONCLUSIONS: The machine learning model trained on radiomics features of MR-FLAIR images can effectively predict patients' MOGAD. This study provides a fast, objective, and quantifiable method for MOGAD diagnosis.

Ion-Conducting Ceramic Membrane Reactors for the Conversion of Chemicals.

Ion-conducting ceramic membranes, such as mixed oxygen ionic and electronic conducting (MIEC) membranes and mixed proton-electron conducting (MPEC) membranes, have the potential for absolute selectivity for specific gases at high temperatures. By utilizing these membranes in membrane reactors, it is possible to combine reaction and separation processes into one unit, leading to a reduction in by-product formation and enabling the use of thermal effects to achieve efficient and sustainable chemical production. As a result, membrane reactors show great promise in the production of various chemicals and fuels. This paper provides an overview of recent developments in dense ceramic catalytic membrane reactors and their potential for chemical production. This review covers different types of membrane reactors and their principles, advantages, disadvantages, and key issues. The paper also discusses the configuration and design of catalytic membrane reactors. Finally, the paper offers insights into the challenges of scaling up membrane reactors from experimental stages to practical applications.

Scalp EEG functional connection and brain network in infants with West syndrome.

The common age-dependent West syndrome can be diagnosed accurately by electroencephalogram (EEG), but its pathogenesis and evolution remain unclear. Existing research mainly aims at the study of West seizure markers in time/frequency domain, while less literature uses a graph-theoretic approach to analyze changes among different brain regions. In this paper, the scalp EEG based functional connectivity (including Correlation, Coherence, Time Frequency Cross Mutual Information, Phase-Locking Value, Phase Lag Index, Weighted Phase Lag Index) and network topology parameters (including Clustering coefficient, Feature path length, Global efficiency, and Local efficiency) are comprehensively studied for the prognostic analysis of the West episode cycle. The scalp EEGs of 15 children with clinically diagnosed string spasticity seizures are used for prospective study, where the signal is divided into pre-seizure, seizure, and post-seizure states in 5 typical brain wave rhythm frequency bands (δ (1-4 Hz), θ (4-8 Hz), α (8-13 Hz), ß (13-30 Hz), and γ (30-80 Hz)) for functional connectivity analysis. The study shows that recurrent West seizures weaken connections between brain regions responsible for cognition and intelligence, while brain regions responsible for information synergy and visual reception have greater variability in connectivity during seizures. It is observed that the changes inßandγfrequency bands of the multiband brain network connectivity patterns calculated by Corr and WPLI can be preliminarily used as judgment of seizure cycle changes in West syndrome.

Multidimensional Feature Optimization Based Eye Blink Detection Under Epileptiform Discharges.

OBJECTIVES: Eye blink artifact detection in scalp electroencephalogram (EEG) of epilepsy patients is challenging due to its similar waveforms to epileptiform discharges. Developing an accurate detection method is urgent and critical. METHODS: In this paper, we proposed a novel multi-dimensional feature optimization based eye blink artifact detection algorithm for EEGs containing rich epileptiform discharges. An unsupervised clustering algorithm based on smoothed nonlinear energy operator (SNEO) and variational mode extraction (VME) is proposed to detect epileptiform discharges in the frontal leads. Then, multi-dimensional time/frequency EEG features extracted from forehead electrodes (FP1 and FP2 channels) combining with the improved VME (IVME) threshold are derived for EEG representation. A variance filtering method is further applied for discriminative feature selection and a machine learning model is finally learned to perform detection. RESULTS: Experiments on EEGs of 16 subjects from the Children's Hospital of Zhejiang University School of Medicine (CHZU) show that our method achieves the highest average sensitivity, specificity and accuracy of 95.04, 89.52, and 93.01, respectively. That outperforms 5 recent and state-of-the-art (SOTA) eye blink detection algorithms. SIGNIFICANCE: The proposed method is robust in eye blink artifact detection for EEGs containing high-frequency epileptiform discharges. It is also effective in dealing with individual differences in EEGs, which is usually ignored in conventional methods.

Deep feature fusion based childhood epilepsy syndrome classification from electroencephalogram.

Accurate classification of the children's epilepsy syndrome is vital to the diagnosis and treatment of epilepsy. But existing literature mainly focuses on seizure detection and few attention has been paid to the children's epilepsy syndrome classification. In this paper, we present a study on the classification of two most common epilepsy syndromes: the benign childhood epilepsy with centro-temporal spikes (BECT) and the infantile spasms (also known as the WEST syndrome), recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). A novel feature fusion model based on the deep transfer learning and the conventional time-frequency representation of the scalp electroencephalogram (EEG) is developed for the epilepsy syndrome characterization. A fully connected network is constructed for the feature learning and syndrome classification. Experiments on the CHZU database show that the proposed algorithm can offer an average of 92.35% classification accuracy on the BECT and WEST syndromes and their corresponding normal cases.

Multimodal Moore-Penrose Inverse-Based Recomputation Framework for Big Data Analysis.

Most multilayer Moore-Penrose inverse (MPI)-based neural networks, such as deep random vector functional link (RVFL), are structured with two separate stages: unsupervised feature encoding and supervised pattern classification. Once the unsupervised learning is finished, the latent encoding is fixed without supervised fine-tuning. However, in complex tasks such as handling the ImageNet dataset, there are often many more clues that can be directly encoded, while unsupervised learning, by definition, cannot know exactly what is useful for a certain task. There is a need to retrain the latent space representations in the supervised pattern classification stage to learn some clues that unsupervised learning has not yet been learned. In particular, the residual error in the output layer is pulled back to each hidden layer, and the parameters of the hidden layers are recalculated with MPI for more robust representations. In this article, a recomputation-based multilayer network using Moore-Penrose inverse (RML-MP) is developed. A sparse RML-MP (SRML-MP) model to boost the performance of RML-MP is then proposed. The experimental results with varying training samples (from 3k to 1.8 million) show that the proposed models provide higher Top-1 testing accuracy than most representation learning algorithms. For reproducibility, the source codes are available at https://github.com/W1AE/Retraining.

Differential Effect of Vaginal Microbiota on Spontaneous Preterm Birth among Chinese Pregnant Women.

Objective: The effect of vaginal microbiota on spontaneous preterm birth (sPTB) has not been fully addressed, and few studies have explored the associations between vaginal taxa and sPTB in the gestational diabetes mellitus (GDM) and non-GDM groups, respectively. Study Design. To minimize external interference, a total of 41 pregnant women with sPTB and 308 controls (pregnant women without sPTB) from same regain were enrolled in this case-cohort study. Controls were randomly selected at baseline. With the exception of GDM, other characteristics were not significantly different between the two groups. Vaginal swabs were collected at early second trimester. Using 16S amplicon sequencing, the main bioinformatics analysis was performed on the platform of QIIME 2. Vaginal microbiota traits of the sPTB group were compared with controls. Finally, the effects of binary taxa on sPTB in the GDM group and the non-GDM group were analyzed, respectively. Results: The proportion of GDM in the sPTB (19.51%) was higher than the controls (7.47%, P = 0.018). The vaginal microbiota of pregnant women with sPTB exhibited higher alpha diversity metrics (observed features, P = 0.001; Faith's phylogenetic diversity, P = 0.013) and different beta diversity metrics (unweighted UniFrac, P = 0.006; Jaccard's distance, P = 0.004), compared with controls. The presence of Lactobacillus paragasseri/gasseri (aOR: 3.12, 95% CI: 1.24-7.84), Streptococcus (aOR: 3.58, 95% CI: 1.68-7.65), or Proteobacteria (aOR: 3.39, 95% CI: 1.55-7.39) was associated with an increased risk of sPTB in the non-GDM group (P < 0.05). However, the relative abundance of novel L. mulieris (a new species of the L. delbrueckii group) was associated with a decreased risk of sPTB (false discovery rate, 0.10) in all pregnant women. Conclusion: GDM may modify the association of vaginal taxa with sPTB, suggesting that maternal GDM should be considered when using vaginal taxa to identify pregnant women at high risk of sPTB.