Browse Articles

Synergizing Schottky/S‐Scheme Dual‐Junction With Cu/Pb Dual‐Site in a 0D/3D Cu@CsPbBr ₃ /g‐C ₃ N ₄ Heterojunction for Efficient CO ₂ ‐to‐C ₂ H ₄ Photoreduction

ABSTRACT The CO 2 photoreduction to multi‐carbon products, particularly C 2 H 4 , remains challenging due to inefficient charge separation, high C−C coupling barriers, and limited mass transport. Herein, we designed a mixed‐dimensional heterostructure integrating S‐scheme and Schottky junctions, with Cu‐decorated CsPbBr 3 nanocrystals anchored on three‐dimensionally ordered macroporous g‐C 3 N 4 (Cu@CPB/3DOM‐CN). In situ x‐ray photoelectron spectroscopy and femtosecond transient absorption spectroscopy demonstrate dual internal electric fields driving rectified spatial carrier separation with strong redox capability. Strong metal–support interaction induces charge redistribution, generating polarized Cu/Pb dual sites that lower the energy barrier for *CO hydrogenation to *CHO. In situ diffuse reflectance infrared Fourier transform spectroscopy combined with density functional theory calculations provides direct evidence that dual sites promote asymmetric *CO−*CHO coupling. The 3DOM framework functions as a nanoreactor, enhancing the local enrichment of CO 2 and key intermediates to promote coupling kinetics. The optimized catalyst exhibits distinct selectivity across varying CO 2 concentrations, achieving 52.4% C 2 H 4 selectivity (26.37 µmol g −1 h −1 ) in pure CO 2 , which increases to 77.6% and 65.1% under 5% and 0.04% CO 2 (balanced with Ar), respectively, highlighting its remarkable adaptability to diverse application scenarios. This work establishes a triple synergy of directional charge transport, active‐site polarization, and reactant enrichment for efficient and selective CO 2 ‐to‐C 2 H 4 conversion.

Beyond the Fixed Charge: A New On‐Tissue Derivatisation Strategy Employing Photoionisable Chromophores for Mass Spectrometry Imaging of Amino‐Containing Metabolites

ABSTRACT Matrix‐assisted laser desorption/ionisation mass spectrometry imaging (MALDI‐MSI) has been widely adopted for the spatially resolved mapping of chemical composition within biological tissues. However, inherent limitations in ionisation efficiencies limit the scope of analyte detection. Currently, methods to enhance molecular coverage have focused on chemical derivatisation of target analyte classes or post‐ionisation techniques, namely, laser post‐ionisation (MALDI‐2). Previously, we observed that under reduced laser fluence conditions, MALDI‐2 can be utilised to selectively enhance photoionisation of aromatic analytes. Leveraging these observations, we have conducted a proof‐of‐concept study to establish a conceptually novel class of derivatisation reagents, employing a tocopherol active chromophore O ‐linked succinimide (TAC/OS) for the selective derivatisation of primary amines. Using TAC/OS, amino metabolites were selectively detected as the derivatised radical cations in the presence of MALDI‐2. Across three tissue types, over 30 metabolites were detected and localised, including 20 amino acids alongside neurotransmitters such as γ‐aminobutyric acid (GABA) and dopamine. Imaging at 10 µm pixel size revealed minimal delocalisation, allowing for MSI at near single‐cell resolution. Furthermore, we demonstrated the ability of the developed protocol on late‐stage pancreatic cancer xenografts.

Towards clinically reliable AI: comparative evaluation of CNN, transformer, and hybrid architectures for meningioma detection and segmentation

Delicate inhibition of NFκB/RELA circuitry facilitates efficient transition towards ground-state pluripotency in human

Abstract Rebuilding human naïve pluripotency from primed stem cells is essential for generating pre-implantation epiblast-like cells, a key source for regenerative medicine. Here we present a defined, feeder-free protocol that efficiently converts primed human pluripotent stem cells (hPSCs) to a naïve-like state within 12 days. Integrated ATAC-seq, RNA-seq and miRNA-seq analyses reveal a rapid chromatin rewiring marked by increased accessibility at OCT/KLF motifs, closure at ZIC/RFX/NFκB sites, upregulation of miR-372/373 and miR-182/183, and downregulation of miR-302 and miR-363/106 clusters. Functional assays demonstrate that the inflammatory transcription factor NFκB/RELA blocks naïve induction by activating peri-implantation barrier genes, including IKBKE and VEGFR1. Conversely, enforced miR-372/373 expression accelerates conversion by directly targeting RELA. These data delineate an NFκB/RELA axis that orchestrates the epigenetic switch to human naïve pluripotency, offering a tractable framework for studying early human development and advancing cell-based therapies.

Pollution removal efficiency enhancement by agricultural biomass additions in constructed wetlands: A framework integrating meta-analysis with explainable machine learning

The agricultural biomass addition on constructed wetlands (CWs) is a sustainable strategy that integrates pollution control with waste valorization. However, their widespread applications remain constrained by variations in biomass types, pretreatments, and system designs. This study combined meta-analysis and explainable machine learning to assess how agricultural biomass addition influences Chemical Oxygen Demand (COD) and Total Nitrogen (TN) removal in CWs, particularly under low C/N conditions, using 272 and 1,283 independent observations for meta-analysis and machine learning, respectively. Results showed that bamboo biochar significantly enhances COD removal efficiency in CWs by an average of 54.8% (SMD = 2.50), while lotus leaf biochar improves TN removal efficiency by 32.5% (SMD = 1.20) compared with controls. Additionally, multiple machine learning models were tested and the results showed that the XGBoost model demonstrated the robustperformance in simulating TN removal (R 2  = 0.83), whereas the Random Forest model is effective for COD removal (R 2  = 0.76). SHAP analysis further indicatedthat increasing wetland volume can simultaneously enhance both COD and TN removal efficiencies. This study underscores the power of combining meta-analysis and explainable machine learning to optimize CW design and management, offering a robust framework for improving pollution removal in CWs.

Closed‐loop Recyclable and Robust Supramolecular Hydrogel Electrolytes With High Zinc Ion Mobility for Stable Zinc Anode

ABSTRACT Hydrogel electrolytes are promising candidates for zinc‐based energy storage systems (ZESs) owing to their superior safety features, favorable ionic conductivity and interfacial compatibility. However, current hydrogel electrolytes suffer from weak mechanical strength, low Zn 2+ transference numbers, causing Zn dendrite growth and parasitic reactions, while the common permanent covalent crosslinked polymer matrices are non‐recyclable. Herein, a supramolecular hydrogel electrolyte that achieves robust mechanical rigidity with high Zn 2+ mobility is developed by introducing guanylate quadruplex (G‐quadruplex), which effectively regulates concentration gradients and confines water activity. Experimental characterizations and theoretical simulations display that the G‐quadruplex polyanionic network restrains water activity, homogenizes Zn 2+ flux and uniforms electric field, effectively stabilize the Zn anode. The hydrogel electrolyte enables durable cycling life of 3,800 hours at 1 mA cm −2 and 1,800 hours at 10 mA cm −2 in Zn//Zn symmetric cells. Additionally, the zinc‐ion hybrid supercapacitors display outstanding electrochemical performance and high safety, achieving 97.4% capacity retention over 10 000 cycles. Moreover, the wasted supramolecular hydrogel can be facilely depolymerized back into monomers and biomacromolecules with negligible performances loss, underscoring its recyclability advantage over conventional covalently crosslinked hydrogels. This work provides a supramolecular engineering strategy that overcomes the trade‐offs in hydrogel electrolytes, advancing sustainable and durable ZESs.

Wildfire fuel heat transfer sensor

Abstract Energy exchange in the fire environment is highly influenced by the heterogeneous and dynamic coupling between the atmosphere, fire, and fuels. While it has long been known that radiation and convection are the dominant heat transfer mechanisms in wildfire spread, much is unknown about their relative contribution. This is largely due to significant challenges observing these processes in the extreme wildfire environment. We describe a low cost, easy to deploy, simple heat transfer sensor and associated model, which was developed to measure heat transfer in wildfire. The sensor consists of stainless steel thermocouple probes differing in emissivity and similar in geometry and size to fine fuel elements responsible for carrying fire. Field and laboratory tests indicate that stainless steel probes with 3.2 mm and 1.6 mm diameters alone are not able to resolve the highly-dynamic heat transfer ahead of the fire. However, coupling the probes with a fine-wire thermocouple significantly improves its sensitivity. Results presented here indicate that the sensor and model are capable of measuring physically realistic convective and radiative heat transfer in high-intensity crown fire and simple laboratory scenarios when compared to observations in literature. Some limitations are identified for future investigation and additional testing and validation are required.

Author Correction: Slab tearing and its surface signals controlled by passive margin strength

Correction: Flight delay prediction: Evaluating machine learning algorithms for enhanced accuracy

Strain‐Release Pentafluorosulfanylation of Carbonyl‐Containing Disubstituted Bicyclobutanes: A Fortuitous Path to SF ₅ ‐Containing Oxa[2.1.1]bicyclohexanes

ABSTRACT The recent merger of pentafluorosulfanylation with strain‐release functionalization logic—largely enabled by an increase in SF 5 Cl accessibility—has begun to reshape the ways we can imagine incorporating the SF 5 group into organic molecules. In this study, we first explore how both radical polarity matching and substituent effects, when employed synergistically, can be used to effect regio‐ and diastereoselective SF 5 Cl addition across 1,3‐disubstituted ( viz . 3‐aryl‐1‐carbonyl‐substituted) bicyclobutanes (BCBs) to access a class of congested α‐SF 5 ‐carbonyl‐containing tetrasubstituted cyclobutanes (CBs). Synthetic, computational, and structural studies provide insight on the observed selectivity and reveal unusually close intramolecular SF 5 ⋯C═X (X = C, N, or O) contacts in several SF 5 ‐CBs by SC‐XRD. Beyond accessing compounds of fundamental interest, we describe a complementary telescoped protocol to facilitate isolation of SF 5 ‐CBs as alcohols, as well as a fortuitous observation that led to the synthesis of the first suite of SF 5 ‐substituted oxa[2.1.1]bicyclohexanes (OBHs). Finally, we examine the compatibility of SF 5 ‐CBs and SF 5 ‐OBHs with various reaction conditions to expand the scope of accessible building blocks and report the synthesis of an SF 5 ‐OBH‐containing drug derivative. Results from comparative in vitro ADME profiling indicate the SF 5 ‐OBH motif may serve as an attractive “hybrid bioisostere” for the meta ‐CF 3 ‐Ph motif.

Development and in vitro evaluation of glycyrrhetinic acid and alpha-lipoic acid co-loaded nanophytosomes: a strategy to mitigate cisplatin-induced hepatocellular damage

Learning regularities in noise engages both neural predictive activity and representational changes

Handwritten tax receipt recognition method based on improved DBNet and CRNN

To recognize handwritten tax invoice text and manage tax invoices, a handwritten tax invoice recognition method based on improved differentiable binary network and convolutional recurrent neural network (CRNN) is proposed. This method first achieves bill text detection and localization through a differentiable binary network that integrates feature pyramid enhancement modules (FPEM). An improved CRNN is used to achieve bill text content recognition. In terms of text detection, the proposed module has an average detection precision of 89.1%, a recall rate of 86.4%, and an average frame rate of 63.5 frames per second. Compared with the Text Detection model based on Segformer and Enhanced Feature Pyramid and the Improved Fourier Contour Embedding Network, the precision has increased by 4.9% and 3.5%, respectively, and the frame rate has increased by 20.5 and 11.7 frames per second. In terms of text recognition, the improved CRNN has an average recognition precision of 95.6%, a recall rate of 94.2%, an average frame rate of 56.5 frames per second, and an F1-Score of 0.94. Compared with the Dual-Stream Network Fusing Spatial and Frequency Domain Feature and the Multi-modal Network based on Visual Attention and Semantic Perception, the precision has been improved by 4.9% and 3.0%, respectively, and the F1-Score has been improved by 0.07 and 0.04. This method performs excellently in processing complex handwritten tax receipts, with outstanding recognition precision and robustness, significantly reducing tax workload, and improving tax declaration and management efficiency.

<i>N</i> ‐Alkoxysulfurdiimide Reagents for the One‐Step Modular Synthesis of Primary Sulfonimidamides

ABSTRACT Sulfonimidamides are an emerging motif in medicinal chemistry; however, current synthetic routes often require multistep reaction sequences, deprotection steps, and lack the modularity needed for rapid and diverse library synthesis. Here, we describe the synthesis and application of previously unexplored N ‐alkoxysulfurdiimide reagents for the direct, one‐step preparation of primary NH 2 ‐sulfonimidamides. By modulating the choice of N ‐substituent, N ‐alkoxysulfurdiimide reagents can be designed to deliver different product distributions. This reactivity correlates with the electron density at sulfur, as inferred from 13 C NMR chemical shifts from the reagents. This tunability can be exploited to design reagents with improved compatibility across organometallic reagents, enabling the preparation of a diverse set of primary NH 2 ‐sulfonimidamides.

Serum uric acid levels and mortality in diabetic patients with chronic kidney disease

Thermodynamically programmed one-pot CRISPR platform for point-of-care SNP genotyping

Abstract One-pot CRISPR diagnostics face a fundamental incompatibility: isothermal nucleic acid amplification enables rapid target accumulation, whereas CRISPR activation irreversibly consumes those substrates, destabilizing reaction kinetics. Here we show that reaction order can be programmed into DNA primers through thermodynamic design. Differences in primer-binding strength create two sequential amplification stages, delaying CRISPR activation until enough amplicons have accumulated without physical separation or external control. The design also introduces the protospacer adjacent motif (PAM), a short sequence required for CRISPR recognition, through the primer rather than relying on its presence in the native target, expanding target accessibility while retaining single-nucleotide discrimination. An ordinary differential equation model captures the threshold behavior and establishes a predictable framework for primer design. Building on this principle, we develop Thermodynamically Encoded Molecular Programming for One-pot diagnostics (TEMPO), which achieves attomolar sensitivity within 30 min and enables sequencing-concordant SNP genotyping and pathogen detection in a single-step microfluidic format.

Diurnal temperature range: A climatological primer for health researchers

Days with high diurnal temperature range (DTR) are commonly linked to morbidity and mortality, yet these health impacts are rarely connected with the underlying meteorological factors. To provide health researchers with much-needed context, we examine the climatology of DTR in the continental United States from 1950 to 2022 using the gridded ERA5 Land reanalysis archive. DTR has declined significantly over the period of record over much of the United States, with stronger signals in the east and north in summer and autumn. Extreme positive DTR values (95th percentile), which are commonly linked to morbidity, are also declining significantly. DTR declines can be ascribed to maximum temperatures increasing at a slower rate than minima, with minimum temperatures rising in response to increasing atmospheric humidity, cloud cover, and precipitation. Given that DTR trends are clearly linked to anthropogenic warming, this would imply reduced health risks, if DTR is a direct causal exposure. Because the most extreme DTR days occur under calm, low-humidity and cloud-free conditions, clinical evidence is needed to demonstrate how and why these weather conditions produce intra-day changes in exposure that are physiologically harmful.

Photocatalytic Anaerobic Conversion of <i>m</i> ‐Xylene to Xylenols With Co‐Production of Hydrogen

ABSTRACT Mild photocatalytic valorization of aromatic hydrocarbons is promising for phenolic synthesis, but reliance on oxidants (O 2 or H 2 O 2 ) often leads to the oxidation of alkyl groups rather than stable phenyl rings, thus producing aromatic aldehydes or ketones instead of targeted phenols. In this work, we employed a photocatalytic anaerobic reaction pathway for directly synthesizing the targeted high‐value xylenol and H 2 from m ‐xylene and water over a palladium single atom‐loaded TiO 2 with zinc modification (Pd 1 ‐TiO 2 (Zn)). In situ infrared spectroscopy and electron paramagnetic resonance, in combination with first‐principles simulations, revealed that the photocatalytic anaerobic conversion of m ‐xylene proceeds via surface lattice oxygen‐mediated hydroxylation. Lattice oxygen coordinated with Pd acts as a recyclable oxygen source for phenolic hydroxyl groups, regenerated by the rate‐determining water oxidation. Isotope labeling confirms hydroxyl hydrogen originates from the benzene ring, not water; water only provides oxygen. The zinc modification could significantly reduce the reaction barrier of the water‐oxidation step from 0.85 to 0.22 eV at the Pd‐O site. Therefore, the phenolic production rate over Pd 1 ‐TiO 2 (Zn) reached 376.9 µmol g −1 h −1 , accompanied by an exceptionally high phenolic selectivity of 98.5% and a hydrogen production rate of 380.6 µmol g −1 h −1 , 2‐fold higher than that of unmodified Pd 1 ‐TiO 2 .

Forecasting ambient PM2.5 and PM10 concentrations in Hisar City through machine learning

Environment geometry alters sequential route learning and its integration into cognitive maps

Abstract Researchers hypothesize that geometric irregularities can impede spatial memory by distorting neural metrics of space provided by entorhinal grid cells. However, irregularly-shaped geometries could also contribute orientation information that benefits spatial memory. Moreover, serial order effects could counteract or amplify geometry effects during route navigation. Our study directly tests how effects of environment geometry (trapezoid and square) and series in route navigation interact to influence spatial memory in a virtual navigation task. We uncover effects of environment geometry predicted by grid cell models and serial order effects predicted by path integration models. Critically, our results demonstrate that these effects combine in an additive manner, such that location memory toward the end of a route in regions with the largest geometric irregularities (trapezoid) yield the worst spatial memory. Additionally, self-report measures of navigational ability are associated with increased sensitivity to geometric irregularities, emphasizing the essential role of individual differences in navigation.