Cytokine secretion researches have been a main focus of studies among the scientists in the recent decades for its outstanding contribution to clinical diagnostics. Localized surface plasmon resonance (LSPR) technology is one of the conventional methods utilized to analyze these issues, as it could provide fast, label-free and real-time monitoring of biomolecule binding events. However, numerous LSPR-based biosensors in the past are usually utilized to monitor the average performance of cell groups rather than single cells. Meanwhile, the complicated sensor structures will lead to the fabrication and economic budget problems. Thus, in this paper, we report a simple synergistic integration of the cell trapping of microwell chip and gold-capped nanopillar-structured cyclo-olefin-polymer (COP) film for single cell level Interleukin 6 (IL-6) detection. Here, in-situ cytokine secreted from the trapped cell can be directly observed and analyzed through the peak red-shift in the transmittance spectrum. The fabricated device also shows the potential to conduct the real-time monitoring which would greatly help us identify the viability and biological variation of the tested single cell.

Lithium-sulfur (Li-S) batteries have received extensive attention due to their high theoretical specific capacity and theoretical energy density. However, their commercialization is hindered by the shuttle effect caused by the dissolution of lithium polysulfide. To solve this problem, a method is proposed to improve the performance of Li-S batteries using Ti₂N(Ti₂NS₂) with S-functional groups as the sulfur cathode host material. The calculation results show that due to the mutual attraction between Li and S atoms, Ti₂NS₂ has the moderate adsorption energies for Li₂S_x species, which is more advantageous than Ti₂NO₂ and can effectively inhibit the shuttle effect. Therefore, Ti₂NS₂ is a potential cathode host material, which is helpful to improve the performance of Li-S batteries. This work provides a reference for the design of high-performance sulfur cathode materials.

As the key connecting points in the neuromorphic computing systems, synaptic devices have been investigated substantially in recent years. Developing optoelectronic synaptic devices with optical outputs is becoming attractive due to many benefits of optical signals in systems. Colloidal quantum dots (CQDs) are potential luminescent materials for information displays. Light-emitting diodes based on CQDs have become appealing candidates for optoelectronic synaptic devices. Moreover, light-emitting transistors exhibit great application potential in these synaptic devices. From this perspective, light-emitting artificial synapses were discussed on the basis of these structures in the devices. Their mechanisms, performance, and future development were analysed and prospected in detail.

The growing demand from the extended reality and wearable electronics market has led to an increased focus on the development of flexible human-machine interfaces (HMI). These interfaces require efficient user input acquisition modules that can realize touch operation, handwriting input, and motion sensing functions. In this paper, we present a systematic review of triboelectric-based contact localization electronics (TCLE) which play a crucial role in enabling the lightweight and long-endurance designs of flexible HMI. We begin by summarizing the mainstream working principles utilized in the design of TCLE, highlighting their respective strengths and weaknesses. Additionally, we discuss the implementation methods of TCLE in realizing advanced functions such as sliding motion detection, handwriting trajectory detection, and artificial intelligence-based user recognition. Furthermore, we review recent works on the applications of TCLE in HMI devices, which provide valuable insights for guiding the design of application scene-specified TCLE devices. Overall, this review aims to contribute to the advancement and understanding of TCLE, facilitating the development of next-generation HMI for various applications.

The escalation of thermal runaway in lithium-ion batteries presents severe safety hazards that necessitate advanced monitoring protocols to ensure early warning of potential failures. Carbon dioxide (CO₂) is released during preliminary decomposition well before catastrophic failure occurs, thereby providing a strategic advantage for early-stage warning. Consequently, identifying materials with high-selective CO₂ recognition is an essential prerequisite for developing reliable sensing platforms. This study integrates Grand Canonical Monte Carlo simulations with Random Forest (RF) models to systematically screen 1470 MOFs from the CoRE-MOF 2019 database. The screening process evaluates selective CO₂ recognition under multicomponent competitive adsorption conditions involving CO₂, C₂H₄, and O₂. The performance evaluation is based on working capacity, selectivity, and the trade-off between working capacity and selectivity (TSN). The RF model achieves high predictive accuracy, with tested R² exceeding 0.92 on the test samples. Shapley Additive Explanations (SHAP) interpretability analysis identifies Q⁰_st(CO₂), Q⁰_st(C₂H₄), WEPA, K_H(C₂H₄), and ETR as key performance drivers. The results indicate that CO₂ selectivity is constrained by the binding strength of competing C₂H₄. Optimal materials tend to have hard Lewis acid centers and polar inorganic clusters to minimize non-specific π-interactions with interfering species. Top-performing MOFs require balanced structural features, concentrating in moderate surface areas (965–1975 m²/g), narrow pore windows (PLD ≈ 4–7 Å, LCD ≈ 5.5–9.6 Å), high void fractions above 0.6, and low densities below 1.3 g/cm³. AJOTEY emerges as the optimal candidate with a TSN of 6.43 mol/kg, combining substantial working capacity (4.57 mol/kg) with strong selectivity (25.52). These results will accelerate the discovery of sensing materials and provide a practical pathway for MOF-based CO₂ sensor development to enhance lithium-ion battery safety.

3D point clouds primarily consist of irregular, sparse data, dominated by background elements. The inherent irregularity of 3D point clouds induces elevated data movement, while the predominance of background points significantly amplifies computational requirements. Inspired by the substantial overlap of background points in adjacent frames, we introduce a pruning technique that exploits temporal correlations across successive frames to reduce redundant computations and expedite inference. This methodology optimizes computational resources to process valuable and highly correlated data, rather than indiscriminately processing entire point clouds. To further accelerate performance, we enhance data movement using Single Instruction Multiple Data (SIMD) techniques, optimizing the time-intensive Gather and Scatter operations within the dataflow. We compare it with the state-of-the-art sparse inference engine TorchSparse 2.0 to show our proposed method can achieve $1.2\times$ speedup for MinkUnet and SPVCNN, without a significant accuracy loss. In particular, our SIMD-based data movement can achieve more than $5\times$ speedup.