Browse Articles

One‐Pot Tandem Conversion Strategy Enables Atom‐Economical Recovery of Spent Layered Oxide Cathodes

ABSTRACT The efficient and sustainable recycling of valuable metals from spent lithium‐ion batteries (LIBs) presents a significant challenge, as conventional hydrometallurgical methods often suffer from harsh operational conditions and inefficient reagent utilization. Herein, we report an atom‐economical one‐pot tandem conversion (OTC) strategy for the rapid recovery of critical metals from spent layered oxide cathodes (i.e., LiNi 0.5 Co 0.2 Mn 0.3 O 2 ). This approach completely converts cathode powder into transition metal oxalates and a lithium‐rich solution in a single reactor at 80 °C within 20 min, achieving > 98% recovery for Ni/Co/Mn and 99.4% for Li. Crucially, near‐quantitative (≈100%) utilization of stoichiometrically dosed K 2 SO 3 , ascorbic acid (AA), and oxalic acid (Ox) is confirmed by ion chromatography and pH tracking, leaving negligible reagent waste. Mechanistic investigations combining experimental evidence and theoretical calculations reveal that the OTC strategy proceeds through a uniquely orchestrated, pH‐modulated sequence, where the reductive activation, coordinative dissolution, and spontaneous precipitation are decoupled and activated in a sequential manner to avoid the competitive interactions that plague conventional one‐pot systems. The OTC strategy demonstrates broad applicability across various layered oxide cathodes, establishing a green and efficient paradigm for spent LIB recycling that significantly enhances both leaching kinetics and atom economy compared to existing hydrometallurgical routes.

A spatiotemporal U-Net++ deep learning framework for dengue risk mapping in Colombia

Abstract Understanding dengue dynamics from a spatiotemporal perspective has become increasingly relevant in recent years, as it allows the identification of factors influencing disease transmission, including climatic conditions, human behavior, and the distribution of Aedes aegypti , the primary vector responsible for dengue virus transmission in tropical and subtropical regions. This knowledge is essential for supporting public health decision-making and reducing the impact of dengue on vulnerable populations. This study proposes a spatio-temporal deep learning framework based on the U-Net++ architecture to generate high-resolution dengue risk maps in Colombia. The model integrates climatic, environmental, demographic, and socioeconomic information derived from satellite and census sources. Two spatial approaches were evaluated: a high-dimensional geography ( HDG ) approach at the national scale and a low-dimensional geography ( LDG ) approach applied to five departments with high dengue incidence. The model integrating climatic, social, and environmental information achieved the best performance in both approaches. In the HDG configuration, the best model (M9) reached a test mIoU of 0.6646 (validation mIoU = 0.7361). In the LDG configuration, the corresponding model achieved an average test mIoU of approximately 0.73 across departments. These results highlight the potential of integrating heterogeneous climatic, environmental, and socioeconomic data within a spatiotemporal deep learning framework to characterize dengue risk patterns and support high-resolution surveillance.

Dual-function floating-gate memory driver for energy-efficient integrated display–illumination system

Abstract The increasing demand for multifunctional micro-displays capable of both high-brightness illumination and low-power image display highlights a critical bottleneck in existing display technologies, which struggle to efficiently switching between large-area uniform light output and fine-grained pixel-level control. Here, we introduce the Integrated Nonvolatile Display–Illumination System (INDIS)—a monolithically integrated Micro-LED architecture based on transparent indium tin oxide (ITO) floating-gate memory (FGM) devices, achieving dual-mode operation. Pixel-level addressing is realized with only a single FGM per pixel and an independent gate controller, enabling the programmed image pattern to be retained without continuous refresh. To support lighting functionality, INDIS adopts a matrix circuit with shared drain electrodes acting as a common anode port, allowing the entire Micro-LED array to be simultaneously activated for large-area illumination. By unifying memory and emission functions within a compact architecture, INDIS achieves display–illumination fusion, offering a scalable and energy-efficient solution for next-generation multifunctional micro-display applications.

Chemistry Evolution of Low‐Temperature Divalent Vanadium V(II) Precipitation Enables Wide Temperature Range Vanadium Flow Batteries

ABSTRACT Vanadium flow batteries (VFBs) are promising technologies for large‐scale energy storage. However, the precipitation of divalent vanadium [V(II)] species in the negative electrolyte at low temperatures hinders their further application. At present, the microscopic structure and formation mechanism of this precipitate remain unclear. Herein, single‐crystal X‐ray diffraction (SCXRD) reveals the crystal structure of the low‐temperature precipitate as VSO 4 ·6H 2 O. The precipitation mechanism was clarified by combining in situ variable‐temperature Raman spectroscopy with density functional theory (DFT) calculations. The results indicate that the precipitation originates from the enhanced deprotonation of HSO 4 − (HSO 4 − + H 2 O ⇌ H 3 O + + SO 4 2− ) at low temperatures. The SO 4 2− acts as an anionic bridge, directly inducing the dimerization of two V(H 2 O) 6 2+ units via hydrogen bonding, which in turn triggers precipitation. Furthermore, we propose a dual‐site solvation engineering strategy, where the co‐introduction of acetonitrile (ACN) and HCl precisely modulates both the primary solvation shell of V(II) (forming [V(H 2 O) 5 ACN] 2+ ) and its secondary solvation environment (reducing SO 4 2− ). The designed electrolyte enables stable operation of a VFB for over 500 cycles (∼30 days) at −10 °C and 40 mA cm −2 , with energy efficiency (EE) > 80%, demonstrating its potential in freezing regions.

Impact of insulin resistance partially mediating symptomatic intracranial hemorrhage on futile recanalization in large vessel occlusion patients

Nonmetallic plasmonic Al-doped W18O49 drives pure CO production from formic acid dehydration

Synergistic Regulation of Interfacial Potential and Anionic Covalency for High‐Voltage Cobalt‐Free All‐Solid‐State Batteries

ABSTRACT High‐voltage cobalt‐free all‐solid‐state lithium batteries (ASSLBs) represent a promising pathway toward high‐energy‐density and sustainable energy storage. However, their practical viability is fundamentally hindered by a coupled interfacial failure mechanism involving kinetic bottlenecks at the space‐charge layer (SCL) and the electrochemical instability of interfacial lattice oxygen. Here, we propose a synergistic regulation to decouple these constraints in 5 V‐class LiNi 0.5 Mn 1.5 O 4 (LNMO) ASSLBs. We reveal that the large lithium (Li) chemical potential mismatch at the LNMO/electrolyte interface drives a Li‐deficient SCL, while the high voltage triggers interfacial oxygen release, causing severe interfacial structural degradation. To address this, a stable interface was constructed where interfacial potential and anion covalency are regulated synergistically. Specifically, a high‐dielectric BaTiO 3 (BTO) coating layer was introduced to regulate interfacial potential and suppress SCL formation, while sulfate‐derived S─O covalent bonds stabilized the interfacial lattice oxygen. Consequently, the BTO‐S‐LNMO ASSLB achieves a notable increase in reversible capacity from 52 to 116 mAh g −1 at 0.1 C and enables high‐rate capacity up to 3 C and exhibits long‐term durability at 1 C. This work establishes a paradigm of coupling dielectric regulation and anion‐chemistry stabilization to unlock the potential of high‐voltage LNMO ASSLBs.

Lentinula edodes (Shiitake) extract as a vitamin D receptor activator to enhance skin adaptogenic responses

Author Correction: GGCX promotes Eurasian avian-like H1N1 swine influenza virus adaption to interspecies receptor binding

In Situ Activating a Protein‐Complexed Quenched Probe via Alkaline Phosphatase Response for Pyroptosis‐Mediated Cancer Immunotherapy

ABSTRACT In situ synthesis of therapeutic agents and activation of immune effect within tumor cells are highly desired for precision cancer theranostics. Intracellular reaction and aggregation triggered by tumor‐specific biomarkers represent a potent strategy to activate therapeutics and modulate immune responses. Here, we designed a protein‐complexed quenched probe that specifically and sensitively responded to the abnormally overexpressed biomarker alkaline phosphatase (ALP) in tumors, enabling in situ activation to induce cell pyroptosis and immunoactivation. Compared to its monophosphate analog (TdVPy‐P), the diphosphate probe (TdVPy‐PP) demonstrated faster ALP responsiveness and stronger fluorescence output with a fourfold increase in signal‐to‐noise ratio, enabling specific targeting and aggregation on subcellular organelle membranes (mitochondria and endoplasmic reticulum). This process further boosted localized ROS generation and activated GSDME‐mediated pyroptosis, leading to the release of immunogenic antigens that promoted dendritic cell maturation and toxic T cell activation. After complexation with protein, TdVPy‐PP can be administered via tail vein injection to target and fluorescently visualize tumor tissue. The growth of primary, distant, and metastatic tumors was significantly suppressed through in situ activated chemodynamic/photodynamic effects combined with systemic immune activation, all while maintaining excellent biocompatibility. This protein‐assisted in situ activation strategy offers a promising tool for precise and efficient tumor therapy.

Record‐Breaking Asymmetric Hysteresis Width Greater Than 100 K Spanning Room Temperature in Mn(III)‐Based Spin‐Crossover Materials Enabled by 2D Hydrogen‐Bonding Cooperativity

ABSTRACT Spin crossover (SCO) materials capable of exhibiting wide thermal hysteresis loops near room temperature are highly desirable for molecular switches, memory devices, and sensors. While such behavior has been extensively studied in Fe(II), Fe(III), and Co(II) complexes, examples in Mn(III)‐based systems remain limited due to the typically favored high‐spin state and subtle structural changes accompanying spin‐state switching. Herein, we report a Mn(III)‐based spin‐crossover complex [Mn(3‐MeO‐5‐Me‐sal 2 ‐323)]·BF 4 ( 1 ), which exhibits a record‐breaking asymmetric thermal hysteresis width exceeding 100 K, spanning room temperature. Detailed magnetic, calorimetric, and crystallographic studies reveal that this exceptional cooperativity is driven by an unprecedented two‐dimensional (2D) hydrogen‐bonding network formed between the complex cations and ordered BF 4 – counterions. High‐resolution transmission electron microscopy (HRTEM) reveals locally ordered nanoscale domains, confirming retention of the supramolecular stacking motif at the nanoscale, consistent with the interplanar spacings obtained from single‐crystal x‐ray analysis. Variable‐temperature Raman spectroscopy further corroborates Mn(III) spin crossover via spin‐state‐dependent metal–ligand vibrational signatures. Overall, the present report demonstrates the critical role of dimensionality in supramolecular hydrogen‐bonding cooperativity in governing bistability in Mn(III)‐SCO systems, offering a powerful design strategy for developing next‐generation switchable materials.

Integrating causal inference and explainable AI to reveal the driving factors of provincial carbon emissions in China

Systematic evaluation of PASEF acquisition strategies in complex metaproteomes

Abstract Metaproteomics measures functional expression in complex microbial communities, but extreme sample complexity and dynamic range challenge acquisition strategies. Trapped ion mobility spectrometry with parallel accumulation–serial fragmentation (PASEF) has expanded into multiple acquisition modes, yet systematic evaluations in high-complexity metaproteomes remain limited. Here, we benchmark five PASEF modes—DDA-, DIA-, Slice-, Synchro-, and midia-PASEF—using a complex fecal peptide background spiked with defined bacterial references. Across three gradients and input levels, 540 LC–MS acquisitions are analyzed under matched conditions. Based on data-derived performance scores, DIA-based strategies outperform DDA-PASEF in peptide and protein coverage, particularly for low-abundance microbial features. DIA- and Slice-PASEF show strong quantitative reproducibility, reduced ratio compression, and consistent species-abundance scaling, while functional profiling reveals expanded annotation depth. When tested in a murine colonic injury model, the two highest-scoring methods, DIA- and Slice-PASEF, capture concordant host and microbial responses.

Chemically Masked aPD‐L1‐Sialidase Conjugate for Tumor‐Specific Desialylation and Immunotherapy

ABSTRACT Immune checkpoint inhibitors (ICIs) have revolutionized cancer immunotherapy, yet their clinical efficacy remains limited by low response rates and frequent immune‐related adverse events caused by on‐target off‐tumor toxicity. In this study, we developed a chemically masked anti–PD‐L1 nanobody‐sialidase conjugate (CAPS) to achieve precise control of protein function. The chemical mask is installed via site‐specific PEGylation following the incorporation of a genetically encoded non‐canonical amino acid, and is selectively removed by MMP2 within the tumor microenvironment to restore function locally. This design achieves “one construct, three functions” by enabling simultaneous control over nanobody affinity, desialylation activity, and tumor tissue penetration, thereby enhancing tumor specificity and concurrently disrupting the PD‐1/PD‐L1 and Sialoglycan/Siglec axes. Compared to the unmasked control (anti–PD‐L1 nanobody–sialidase, APS), CAPS exhibited a 144‐fold extension in systemic half‐life and a 9.3‐fold increase in tumor‐site accumulation. Collectively, CAPS demonstrated superior antitumor efficacy, enhanced immune activation, and an improved safety profile in mouse models of ICI‐resistant colorectal cancer. These findings support CAPS as a new class of glyco‐ICIs and offer a promising therapeutic strategy to overcome the limitations of current ICIs by enhancing therapeutic efficacy while minimizing on‐target off‐tumor toxicity.

DEM calibration of high-oleic peanut seeds for precision seed metering

Global vulnerability assessment of mobile telecommunications infrastructure to climate hazards using crowdsourced open data

Heavier Alkyne‐Ni ⁰ Complexes, [R ₂ E ₂ ·Ni] (E = Sn, Pb), Exhibiting σ‐Complex Character

ABSTRACT Despite the ubiquitous nature of alkyne‐transition metal complexes in synthesis and catalysis, analogous examples for the heavier group 14 elements are extremely rare. Here, we describe access to the first such species for Sn and Pb in nickel(0) complexes, [( Cy LE) 2 ·Ni] ( 5 and 6 ; Cy L = [{Cy 2 PCH 2 Si( i Pr) 2 }(Dipp)N] − ; Dipp = 2,6‐ i Pr 2 C 6 H 3 ). Although these systems cannot be accessed by direct addition of the E I dimers to a Ni 0 synthon (i.e. Ni(cod) 2 ; cod = 1,5‐cyclooctadiene), we report a novel reductive group elimination pathway, forming the E I dimers from E II synthons in the coordination sphere of Ni, for example, through H 2 elimination from Sn II hydride compounds. Structural analyses in combination with computational studies indicate that these species are best described as σ‐complexes of E I dimers with a transition metal centre, representing a new bonding mode in organometallic coordination chemistry.

Optimization-driven feature learning for long-tailed object detection in optical remote sensing imagery

Non-competitive additives regulate molecular assembly for compact monolayers in perovskite/silicon tandems

Study on the predictive model of subgrade deformation of the Qinghai-Tibet Railway in permafrost regions based on multi-source data

Abstract Climate warming and engineering activities have exacerbated permafrost degradation in the lower part of the project, leading to significant subgrade deformation in cold regions. To ensure the safe operation of railways, it is necessary to predict subgrade deformation. This study innovatively investigates the deformation prediction of permafrost subgrades from the perspective of road-environment system coupling, transcending the limitations of traditional engineering scales. Using environmental, permafrost, and engineering data along the Qinghai-Tibet Railway collected through aerial, satellite, and ground-based techniques, a spatially distributed dataset covering 25 key conditioning factors (2,660 sample sets) is constructed. This study uses Random Forest (RF) as the base model and integrates two swarm metaheuristic optimization algorithms, that is, Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO), while incorporating a K-fold cross-validation mechanism to construct a stepwise optimization paradigm for predicting subgrade deformation. This paradigm progresses step-by-step through four levels: baseline model establishment, intelligent hyperparameter optimization, introduction of cross-validation strategies, and comparison and selection of optimization algorithms. Model evaluation is conducted across prediction accuracy and error, prediction uncertainty, and dimensionless reliability dimensions. The results show that the K-GWO-RF model demonstrated the best predictive performance, followed by the K-PSO-RF and PSO-RF models, with the standard RF model ranking last. This ranking confirms that the optimization strategies at each level yielded quantifiable performance gains. The K-GWO-RF model also significantly outperforms other commonly used models (Layered Summation Method, Backpropagation Neural Networks and Support Vector Machines). Futhermore, using the Mean Decreasing Accuracy calculation for feature importance analysis, it is determined that the main disaster-causing factors of subgrade deformation include solar radiation, thermokarst lakes, rain, and snow depth. This study realizes the accurate prediction of deformation for permafrost subgrades, which provides important technical support and data reference for the rational design, safe operation, and control of deformation diseases of railway projects in cold regions.