Tong Chen

为应对氢气在储运过程中泄漏需精确检测的迫切需求，并克服传统气敏材料灵敏度低、选择性差等局限，本文基于第一性原理，采用密度泛函理论与非平衡格林函数方法，从理论层面系统研究了联苯纳米带(biphenylene nanoribbon, BPNN)的结构设计、电子性质及其对H₂的气敏检测性能。通过对不同边界钝化方式进行筛选，发现氧原子钝化可使扶手椅型BPNN兼具良好的结构稳定性和金属导电性。然而，本征O钝化BPNN对H₂的吸附能绝对值不足0.01 eV，属弱范德华物理吸附。为提升H₂检测能力，采用过渡金属Co修饰BPNN表面以增加活性吸附位点，结果表明：Co掺杂后H₂吸附表现出明显的化学吸附特征，吸附能绝对值均大于1 eV，且每个BPNN-Co构型最多可同时吸附2个H₂分子。器件输运特性呈现显著各向异性：扶手椅型BPNN-Co器件对H₂浓度的响应灵敏度均比锯齿型器件相对较高，且在O₂、N₂和H₂O环境中仍保持良好选择性，尤其O₂可进一步提升其灵敏度，最高可达36.12%，而N₂和H₂O影响甚微；锯齿型器件虽导电性优异，但对H₂浓度及环境变化均不敏感，传感能力较弱。综上，Co修饰联苯纳米带在气敏领域具有较大应用潜力，尤其适用于低浓度H₂泄漏检测，为高性能氢气传感器的设计提供了理论依据。

Aluminum alloys are of strategic importance in aerospace applications. Higher demands for light weight and high strength are being imposed by current equipment development. During this strengthening process, the structural evolution at grain boundaries plays a critical role. In this study, the element segregation mechanism at grain boundaries was evaluated using hybrid molecular dynamics/Monte Carlo simulations. Under identical matrix concentration, the segregation degree was found to be closely related to the grain boundary (GB) structure, specifically the distribution of lattice voids within structural units. Compact boundaries exhibit negligible substitutional segregation, whereas loose boundaries enable interstitial segregation, the level of which correlates positively with the amount of lattice voids. Owing to the periodic structure of grain boundaries, intragranular segregation exhibits concentration fluctuations consistent with the periodicity of the structural units. Subsequent tensile simulations revealed that Cu solid solution suppresses slip band formation and reduces plasticity, though this reduction can be mitigated by segregation at low‐density grain boundaries. Excessive segregation, however, induces lattice mismatch between the boundary and the matrix, leading to premature fracture. The GB segregation and strengthening mechanisms revealed in this study provide an important theoretical basis for the design and optimization of lightweight aluminum alloys.

Two-dimensional layered nitrides combine atomic-scale thickness with strain-sensitive electronic structures and tunable optoelectronic properties, providing an attractive material platform for strain-tunable nanoelectronic and optoelectronic devices. Here, first-principles calculations combined with the nonequilibrium Green’s function method are employed to systematically investigate the effects of biaxial strain on the electronic structure, optical response, and device-level quantum transport of monolayer InSiN₂. Stability analyses based on elastic criteria, phonon dispersion, and ab initio molecular dynamics simulations support its mechanical, dynamical, and short-time thermal stability. Biaxial strain markedly modifies the electronic and optical properties of InSiN₂: both tensile and compressive strains reduce the band gap, which closes under -8% compression. Tensile strain predominantly enhances the high-energy optical response, whereas compressive strain shifts the dominant absorption features toward lower photon energies and extends the pronounced optical response into the visible region. At the device level, +8% tensile strain increases the photocurrent of the armchair-oriented p-i-n photodiode to approximately 2.46 times its unstrained value. The zigzag-oriented strain sensor exhibits a maximum resolved strain-induced current amplification ratio of approximately 1.84 × 1010 under -8% compression. For the double-gate field-effect transistors, +4% tensile strain produces the strongest electrostatic gate modulation, with the gate-induced current-modulation ratio of the zigzag-oriented device exceeding 9.79 × 107, whereas -8% compression drives the devices into persistent high-current transport states with markedly weakened gate control. These results establish that biaxial strain provides an effective means of jointly regulating the intrinsic electronic structure, optical response, and device-level transport of monolayer InSiN₂, supporting its potential for strain-tunable optoelectronic, strain-responsive, and mechanically reconfigurable nanoelectronic devices.

Abnormal operation of power transformers can generate thermal runaway gases (TRGs), including C2H2, C2H4, CH4, CO, CO2, and H2, which threaten the safety and reliability of electrical equipment. Therefore, the development of gas-sensing materials with high sensitivity, excellent selectivity, and rapid response/recovery characteristics is essential for effective transformer fault diagnosis and online monitoring. In this work, density functional theory calculations were employed to systematically investigate the adsorption behavior and gas-sensing performance of Janus PdSO monolayers and their alkali-metal-modified systems (Li, Na, and K) toward typical TRGs. The pristine PdSO monolayer exhibits weak physisorption toward the six gases, with adsorption energies ranging from -0.044 to -0.179 eV and limited charge transfer, indicating poor intrinsic sensing capability. After alkali metal modification, the adsorption activity and electronic response of PdSO are significantly enhanced. Among the studied systems, Li-PdSO exhibits the strongest adsorption toward CO and CO2, with adsorption energies of -0.62 and -0.67 eV, respectively, while the adsorption toward H2 and CH4 remains relatively weak at -0.15 and -0.27 eV, demonstrating excellent gas selectivity. Moreover, CO and CO2 on Li-PdSO exhibit extremely short recovery times of 0.031 s and 0.214 s, respectively, indicating excellent desorption behavior and reversibility, which is highly desirable for reliable gas sensing applications. Overall, Li modification effectively optimizes the electronic structure of PdSO and selectively enhances its sensing performance toward CO and CO2, highlighting Li-PdSO as a promising candidate for transformer fault gas detection.

The environmental and health risks associated with toxic gas emissions highlight the urgent need for high-performance and cost-effective gas-sensing materials. Here, we establish an integrated density functional theory-machine learning (DFT-ML) framework for the accelerated screening of Janus transition metal dichalcogenide materials toward NO, NO₂, and NH₃ sensing. A total of 162 Janus material-gas adsorption systems were established, and adsorption energies were obtained from DFT calculations on representative structures. An initial feature set was derived from intrinsic material properties and gas descriptors. Redundancy was reduced through Pearson correlation analysis, resulting in 25 relevant features for subsequent modeling. Among 12 ML models evaluated, Gradient Boosting Regression (GB) achieved the best predictive performance (R² = 0.960, RMSE = 0.057, MAE = 0.042 on the validation set). Subsequent SHAP analysis revealed that the material band gap (sb) strongly interacted with gas type (gt), average atomic radius (gr), and electronegativity (gn), which were critical for accurate model predictions. With the optimized GB model, promising candidates were rapidly screened and further assessed with respect to adsorption distance, charge transfer, work function, and recovery time. In particular, NbSSe and VSSe were identified as promising materials with excellent responsiveness and reusability for NO and NO₂ detection. These findings underscore the effectiveness of the proposed DFT-ML framework for high-throughput gas-sensing material discovery. In conclusion, this study establishes an effective DFT-ML paradigm for high-throughput discovery of gas-sensing materials, achieving significant reductions in computational cost without sacrificing predictive reliability, and offering a viable pathway toward the intelligent design of complex material systems.