Diamond demonstrates significant application potential in electronic devices owing to its excellent properties, yet its practical application is often limited by interface challenges in precisely controlling its interfacial electronic properties. This study demonstrates that surface termination engineering serves as a potential strategy for effectively modulating the electron affinity (EA) of diamond, thereby tuning the band offsets and Schottky barrier heights (SBHs) at its interfaces. By constructing 24 distinct diamond termination models, the EA of diamond can be tuned from -3.95 to 2.71 eV, governed by the combined effects of surface dipole moment and gap states. Furthermore, the terminated diamond forms a charge-blocking layer at the heterojunction interface, thereby largely preserving the original band positions. Consequently, a linear modulation of the band offset from 0.58 to 4.34 eV is accomplished by integrating beta-Ga2 O3 with 12 different diamond terminations. For metal/diamond contacts, an SBH adjustment from -0.78 to 2.14 eV is achieved at Pt/diamond interfaces by employing different terminations. Notably, the introduction of surface-state-free terminations effectively suppresses metal-induced gap states, thereby achieving significant Fermi-level depinning. This work elucidates the underlying mechanism of termination-induced EA tuning and establishes a paradigm for quantitative band offset and SBH control, providing critical guidance for the rational design of high-performance diamond-based devices. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Herein, a hierarchically oriented porous anode supported micro-tubular solid oxide fuel cell (MT-SOFC) employing a bi-layered electrolyte has been prepared by phase inversion method and evaluated for power generation. The achieved asymmetric anode not only can facilitate the gas transportation through the anode, but also can provide sufficient active sites for hydrogen oxidation. Meanwhile, the bi-layered electrolyte can effectively prevent the formation of a highly resistive zirconate phase between the La0.6Sr0.4Co0.2Fe0.8O3 cathode and the yttria-stabilized zirconia (YSZ) electrolyte, while the YSZ electrolyte layer can effectively block the electronic conduction in the Ce0.8Sm0.2O1.9 (SDC) interlayer as well. The morphology of the prepared anode support is examined by the two-dimensional (2D) scanning electron microscopy (SEM) and backscattered electrons analysis, as well as the three-dimensional (3D) X-ray microscopy analysis. Moreover, the 3D microstructure of the anode support is reconstructed, and then a quantitative analysis is performed. Additionally, the microstructure of the bi-layered electrolyte including the interface between YSZ and SDC layer is investigated by SEM and energydispersive X-ray spectroscopy. The prepared MT-SOFC exhibits an excellent peak output power density of 0.43, 0.77, 1.19 and 1.79 Wcm_2 at 650, 700, 750, and 800 degrees C, respectively. The excellent cell performance is attributed to the asymmetrical structured anode support with a low tortuosity factor in combination with the sufficient active sites for hydrogen oxidation. Our findings can guide the development of high-performance MTSOFCs.
Cross-domain recommendation (CDR) offers an effective way to alleviate the cold-start problem, but most existing methods still model cross-domain preference transfer at the level of individual users. This point-wise paradigm relies heavily on overlapping users and struggles to capture the higher-order relational patterns underlying user interests across domains, resulting in limited performance when overlap is scarce. Meanwhile, representing items solely with ID embeddings ignores rich multimodal content, further constraining transferable preference modeling. In this work, we propose Multimodal Group-Level Relational Preference Modeling for Cross-Domain Recommendation, a framework that reformulates the transferable unit from direct user-to-user embedding mappings to user-to-key-user relational signatures, where overlapping users provide supervision and non-overlapping users serve as relational anchors. This design captures more stable and transferable cross-domain interest structures, yielding consistently improved performance. Concretely, we employ a multimodal large language model to extract item representations from textual and visual content, and further adopt a temporal attention pooling encoder to integrate users’ historical interactions with multimodal item semantics for constructing multimodal user representations. Based on these enriched representations, we develop a group-level relation learning module that models relational dependencies between each overlapping user and a domain-specific key user set selected from the non-overlapping ones, thereby enabling group-level preference transfer across domains. We further introduce an adversarial alignment strategy to reduce cross-domain representation discrepancies and enhance generalization. Extensive experiments on real-world datasets demonstrate that our method consistently outperforms state-of-the-art baselines.
The high pulse repetition frequency (PRF) can cause range ambiguity in radar systems, which negatively impacts the performance of detection, localization, and estimation. In this paper, we develop a radar scheme consisting of phased array (PA) and space-time coding array (STCA), namely the hybrid STCA radar, to resolve the problem of parameter estimation and target detection. By combining the increased range-transmit degrees of freedom provided by multiple-input multiple-output (MIMO)-STCA radar and the high transmit gain provided by PA radar, an unambiguous method is devised for jointly estimating range and angle, while improving the accuracy of parameter estimation. The Cramér-Rao bounds (CRB) are derived to verify the performance of the parameter estimation. Moreover, an integrated application of range-angle-velocity estimation and adaptive moving target detection is proposed for the case of complex jamming environment. Based on the MIMO-STCA mode of hybrid radar, the generalized likelihood ratio test (GLRT) detectors in the condition of available and unavailable training data are constructed. Furthermore, a low-complexity approach of the GLRT detector including rough search and fine search is created for practical application. At the analysis stage, numerical simulations and performance analysis validate the effectiveness of the proposed methods during the analysis stage.
In this paper, we propose an efficient and robust numerical solver, based on an incomplete Givens reduction, for solving periodic pentadiagonal linear systems with linear time complexity. The method employs a carefully designed sequence of Givens rotations to transform the original linear system into a block lower triangular linear system. This transformed system admits a trivial block LU factorization, leading to an efficient solution process. Compared with other existing methods, our algorithm exhibits superior numerical stability as well as robustness. Additionally, the solution to the periodic anti-pentadiagonal linear system is also discussed. A series of numerical experiments conducted in MATLAB validate the effectiveness, robustness, and competitiveness of the proposed algorithm.
