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Ibrutinib improves overall survival in patients with mantle cell lymphoma
Hexosamine biosynthesis drives hemocyanin O-GlcNAcylation to potentiate antibacterial immunity in shrimp
Experimentally calibrated numerical prediction of underwater vibroacoustic response of bolted and embedded composite steel joints
Can multispecific biologics break the efficacy ceiling?
First-line zanidatamab–chemotherapy with or without tislelizumab improves survival in advanced-stage HER2+ GEA
Connecting single-cell transcriptomes to projectomes in the mouse visual cortex
Proximal labeling of the Golgi secretome reveals fat body–derived humoral factors in Drosophila disc regeneration
Flexible Organic Single Crystals with Unconventional Negative Photoconductivity for Intelligent Security Systems
Unlocking Photocatalytic CO <sub>2</sub> Conversion to Ethylene Glycol by Microdroplet‐Enabled Interfacial Electric Field
ABSTRACT The electrochemical carbon dioxide (CO 2 ) reduction features attractive potentials of carbon neutrality and chemical upgrading, while the types of products from photocatalysis or electrocatalysis still remain limited. The conversion of CO 2 into ethylene glycol, an important chemical that is typically produced by thermo‐catalytic process, has not been achieved by photocatalysis or electrocatalysis. Herein, we demonstrate a microdroplet‐enhanced photocatalytic strategy with a Pd‐TiO 2 catalyst, which allows for efficient conversion of CO 2 into ethylene glycol with high selectivity. The electro‐sprayed microdroplets provide a high interfacial electric field that facilitates deep reduction of CO 2 into *CH 3 , as well as generation of reactive *OH intermediates to form CH 3 OH. The in situ formed CH 3 OH is further photo‐catalytically activated to cleave a C−H bond to *CH 2 OH, followed by the C−C coupling to produce ethylene glycol. Under 1‐sun illumination, the electro‐sprayed microdroplets and Pd‐TiO 2 catalyst exhibited a high CO 2 ‐to‐ethylene glycol conversion rate of 2985 µmol L −1 h −1 , which significantly outperformed those by only photocatalysis or only electro‐sprayed microdroplets, revealing the capability of combining multiple energy fields for tuning reaction pathways and advancing green synthesis.
Global metabolomic profiling of serum biomarkers in women with polycystic ovary syndrome
Novel drug targets in 2025
Clinical implementation of multi-cancer early detection tests: can we find a path forward?
Targeted enzyme discovery using metal-coordination mining
Correction: Colchicine inhibits vascular calcification by suppressing inflammasome activation through the enhancement of the Sirt2-PP2Ac signaling pathway
Linking device dynamics to neural network performance in ionically gated synaptic transistors
Abstract Neuromorphic computing based on artificial synapses requires devices capable of gradual, repeatable, and energy-efficient conductance modulation. Ionically gated transistors are promising candidates because their ion dynamics naturally produce synaptic behavior under low-voltage operation. However, how key device characteristics—conductance range, number of accessible conductance states N , and weight-update nonlinearity β —jointly influence neural network performance remains insufficiently understood. Here, we investigate MoS 2 -based ionically gated synaptic transistors using a combined experimental and modeling framework that links device physics to hardware-aware artificial neural network (ANN) simulations across image-classification tasks of varying complexity. We show that under fixed-amplitude pulsing, increasing N introduces a fundamental trade-off: finer weight resolution is accompanied by stronger update nonlinearity. ANN simulations further reveal that, within the nonlinearity range studied here, classification accuracy is governed by a task-dependent optimal weight resolution rather than a simply maximized number of states. To overcome the nonlinear weight updates, we employ a physics-informed transient model to develop a predictive pulse-engineering algorithm and experimentally demonstrate that it can linearize synaptic weight evolution in the same device. These linearized updates improve ANN accuracy by 1.5%–5.2% for MNIST, 4.0%–5.2% for FMNIST, and 1.2%–12% for KMNIST across the tested state numbers, establishing a quantitative link between device-level dynamics and neural network performance in ionically gated synaptic transistors.