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Fe-embedded N-doped graphene (Fe–N–C) stands out as a promising electrocatalyst for the oxygen reduction reaction (ORR), a critical process in fuel cell technologies. The role of spin multiplicity at the Fe center and its influence on ORR intermediates, particularly the spin crossover effect, is a pivotal yet underexplored aspect of spin-related electrocatalysis. Unraveling the nuances of spin multiplicity and its manipulation holds the key to the next generation of Fe-based spin electrocatalysts for renewable energy. Our study employs a first-principles density functional theory (DFT) approach to dissect the electronic configurations across various spin states of ORR intermediates. Our wavefunction and bond analysis unveil a critical mechanism: the spin electron transfer from the nonbonding dx2–y2 orbital to the antibonding orbital, which dominantly dictates the spin state transition in ORR intermediates on the Fe–N–C catalyst. This insight is further complemented by the discovery that the electronic occupation of d–p π bonds, formed by X–Fe–Y (X/Y: *OH, *O, and *OOH), is insensitive to the IS–HS transition of adsorbates. Another novel finding of this research is the strain-induced modulation of spin states within Fe–N–C. We demonstrate that increasing strain drives a transition from an intermediate spin (IS) to a high spin (HS) state in FeN4 and its ORR intermediates, effectively suppressing ligand-induced spin crossover. Notably, a unique behavior is observed in five-coordinate FeN4OH and FeN4OOH, where strain prompts a return to IS due to electron injection into the dxy orbital. Our simulation of polarization current density reveals a direct correlation between applied strain and ORR onset potential. Specifically, at +8% strain, the onset potential is remarkably enhanced to 0.98 V vs. SHE due to the dominant active moiety of FeN HS4 *OH, underscoring the role of spin state modulation in weakening the Fe–O bond and reducing the desorption energy of *OH. This work not only elucidates the origin of spin multiplicity in ORR intermediates on Fe–N–C but also establishes strain-controlled spin regulation as a viable strategy for tuning ORR activity. Our findings offer a significant insight into the design of spin-related electrocatalysts, paving the way for more efficient and sustainable energy technologies. [ABSTRACT FROM AUTHOR]

Recently, Ni‐based chalcogenides havedemonstrated remarkable activity and selectivity for alcohol electrooxidation, but the mechanisms remain debated. This study synthesizes Ni‐based electrodeswith different chalcogen anion coordination on nickel nanorod arrays (NiOx/Ni,NiSx/Ni, and NiSex/Ni NRAs). NiSex/Ni NRAsshowcases superior performance (Faradaic efficiency 92.9%) in glycerolelectrooxidation reaction (GOR). In situ spectroscopy reveals that NiSecoordination inhibits deep oxidative reconstruction of the Ni‐based interface, preventingNiOOH phase formation during GOR, enhancing activity and stability of NiSex/NiNRAs. Conversely, NiS and NiO coordination lead to deep reconstruction with NiOOHphase formation, limiting GOR performance. Differently, during competingreaction of GOR, the oxygen evolution reaction (OER) leads to deepreconstruction of NiSex interface due to the instability of Ni‐Sebonds, inducing performance degradation and dissolution of Se components. Furthermechanism investigation elucidates that the rate‐determining step (RDS) ofGOR at the NiSex interface involves oxidation of *C2H3O3 intermediatesthrough H2O adsorption, favoring stable formate production.Contrarily, the RDS at the NiSx, NiOx, and NiOOHinterfaces predominantly focus on the decarboxylation of multi‐carbon intermediates, raisingenergy barriers and over‐oxidizing formate to CO2. These results providenew insights for designing Ni‐based non‐oxide catalysts forefficient and stable electrocatalytic oxidation. [ABSTRACT FROM AUTHOR]

Feature pyramids are widely adopted in visual detection models for capturing multiscale features of objects. However, the utilization of feature pyramids in practical object detection tasks is prone to complex background interference, resulting in suboptimal capture of discriminative multiscale foreground semantic features. In this article, a foreground capture feature pyramid network (FCFPN) for multiscale object detection is proposed, to address the problem of inadequate feature learning in complex backgrounds. FCFPN consists of a foreground dual attention (FDA) module and a pathway aggregation (PA) structure. Specifically, the FDA mechanism activates top-down foreground channel responses and lateral spatial foreground location features, so that channel and spatial foreground features are adequately captured. Then, the PA module adaptively learns the fusion weights of multiscale features at different levels of the feature pyramid, which enhances the complementarity of semantic information between different levels of the foreground feature maps. Since the fusion weights are learned adaptively based on different pyramid levels, the detection model accordingly retains the gained information of feature sizes and suppresses the conflicting information. The evaluations on public datasets and the self-built complex background dataset demonstrate that the detection average precision (AP) and the feature learning performance of the proposed method are superior compared with other FPNs, which proves the effectiveness of the proposed FCFPN.

With the rapid development of information era, the traditional von Neumann architecture faces the computing bottleneck, and integration of memory and perception is regarded as a potential solution. Herein, a Ga 2 O 3 /Si heterojunction based multi-modulated optoelectronic synaptic device is fabricated and demonstrated. As stimulated by ultraviolet (UV) optical spikes, the heterojunction device reveals typical synaptic functions of excitatory-postsynaptic current (EPSC), paired-pulse facilitation (PPF), spike-timing-dependent plasticity (STDP), and switch between short-term memory (STM) and long-term memory (LTM). In addition, stronger stimulations like higher reading voltage, stronger optical stimulated intensity, and longer pulse duration time can significantly prolong the attenuation of EPSC, which contributes to the improvement of the forgetting process. Our work provides a potential strategy for future neuromorphic computation through a UV light driven stimulation.