This paper presents a system-wide approach for contingency severity screening intended as a practical and scalable precursor to full dynamic security assessment (DSA). DSA is the standard practice used by Transmission System Operators (TSOs) to ensure that power systems remain stable following disturbances, with key variables—such as voltage, frequency, and thermal loading—kept within defined dynamic performance limits. However, applying DSA comprehensively across all credible contingencies and operating points is prohibitive, making a pre-screening stage indispensable. Commonly used static-security filters offer limited insight to transient dynamics, motivating the development of alternative screening strategies. To support early-stage analysis and prioritization, three static indicators are devised as proxies for dynamic behaviour, grounded in the power–angle characteristics determining transient stability. For each n−1 contingency, these indicators provide a severity estimate and serve as inputs to a supervised learning model trained on labels derived from time-domain simulation. This approach aims to reflect how each disturbance might impact system stability, consistent with the objectives of DSA. Contingency rankings derived from these estimates serve to guide further analysis, enabling TSOs to focus dynamic simulations on the most critical scenarios.
Nitrogen-doped graphene-supported single-atom catalysts (SACs) with maximum atom utilization have high catalytic activity for CO2 reduction reaction (CO2RR). Nevertheless, theoretical exploration of these systems remains immensely insufficient due to the complexity of a realistic microenvironment in CO2RR. This work, taking the NiN4 SAC as a probe, systematically investigates the synergistic effect of applied potential and water molecules on CO2RR performance. Results show that only under relatively higher applied potentials and the H2O molecule could jointly contribute to chemisorption and activation of CO2. Two possible proton sources during the CO2RR process are considered. Under relatively high potentials, H2O-containing H is the proton source for the initial CO2 activation. The reaction energies and energy barriers for both *CO2 -> *COOH and *COOH -> *CO steps are linearly correlated to applied potentials. This can be attributed to the enhanced spin state enabling stronger interaction of the Ni metal center and *COOH intermediate, which is conducive to facilitating the CO2RR process. This work not only explains a long-standing puzzle for an important catalyst but also highlights the joint contribution of the applied potential and water molecules, which can guide more rational elucidation of other electrocatalytic mechanisms and more effective catalyst design.
Due to the high energy and processing efficiencies, brain-inspired optoelectronic synaptic systems provide a promising solution for next-generation artificial vision computing. However, synapses based on single oxides face the challenge of high-power consumption, which seriously limits their practical application. This study presents multifunctional heterojunction optoelectronic synapses with low power consumption. Layered MoO3 and photochromic WO3 films are deposited in turn onto the ITO-covered quartz substrates by using the electron beam evaporation technique, and metal-oxide heterojunction synapses of MoO3/WO3 are fabricated. The synaptic devices exhibit versatile neuromorphic functionalities under both electrical and optical modulation. The heterojunction enables long- and short-term plasticity and achieves an accuracy of up to 92.4% in handwritten digit recognition. Under light stimulation, the device successfully demonstrated basic and advanced synaptic functions. More importantly, the power consumption of the synaptic event is only 67.6 fJ, which is far below those of other similar devices and close to biological synapses. The optoelectronic synapse arrays of 4 × 4 are developed to realize real-time visual perception and memory behaviors. This work provides effective strategies and a scientific foundation for developing next-generation ultralow-power artificial intelligence vision chips.
The excessive weakness and strength of the interactions between graphene and g-C3N4 with CO2 pose challenges for CO2 separation. Here, we proposed a gas separation nanochannel composed of the interlayer spacing in a two-dimensional graphene/g-C3N4 (Gra/CN) membrane to solve the issue by molecular dynamics simulation. Graphene is a finely tuned electrostatic interaction membrane in direct contact with CO2 within the nanochannel. Due to the proper interaction between Gra/CN and CO2, Gra/CN maintains high CO2 permeance and selectivity under mixed gas conditions at different interlayer spacings, which confirms the good applicability for CO2 separation. The nanochannel becomes a highway for CO2 separation under an external electric field (E-field) of 1.0 x 10(-4) V& Aring;(-1) along the z-axis; the CO2 permeance reaches 1.17 x 10(-3) mols(-1)m(-2)Pa-1 through computational simulation, marking a substantial enhancement of approximately 60.3% relative to conditions without E-field. Simultaneously, the solubility coefficient rises to 4.48 x 10(7) molm(-4)Pa as E-field in the z-axis. Moreover, the calculated energy consumption of the CO2 separation is 0.017 GJton(-1), which is below the theoretical minimum value of 0.050 GJton(-1), demonstrating practical feasibility and efficiency in real-world applications. The results of this work highlight the significant role of the synergistic effect of the hybrid membrane gas separation nanochannel and E-field in enhancing CO2 solubility and permeance, providing valuable theoretical guidance for CO2 separation.
