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Nanozymes have demonstrated significant potential as promising alternatives to natural enzymes in biomedical applications. However, their lower catalytic activity compared to that of natural enzymes has limited their practical utility. Addressing this challenge necessitates the development of innovative enzymatic systems capable of achieving specific activity levels of natural enzymes. In this study, we focus on enhancing the catalytic performance of nanozymes by introducing Ru atoms into the single-atom-thick A layer of the V2SnC MAX phase, resulting in the formation of V2(Sn0.8Ru0.2)C with Ru single-atom sites. The V2(Sn0.8Ru0.2)C MAX phase demonstrated an exceptional peroxidase-like specific activity of up to 1792.6 U mg–1, surpassing the specific activity of a previously reported horseradish peroxidase (HRP). Through X-ray photoelectron spectroscopy (XPS) and density functional theory (DFT) investigations, it has been revealed that both the V2C atom layers and single-atom-thick Sn readily accept a negative charge from Ru, leading to a reduction of the energy barrier for H2O2adsorption. This discovery has enabled the successful application of V2(Sn0.8Ru0.2)C in the development of a lateral flow immunoassay for heart failure biomarkers, achieving a detection sensitivity of 4 pg mL–1. Additionally, V2(Sn0.8Ru0.2)C demonstrated exceptional broad-spectrum antibacterial efficacy. This study lays the groundwork for the precise design of MAX phase-based nanozymes with high specific activity, offering a viable alternative to natural enzymes for various applications.

Accurate diagnosis of highly aggressive and deadly tumors is essential for effective treatment and improved patient outcomes, and microRNAs (miRNAs) have emerged as crucial biomarkers for their roles in tumor initiation, progression, and metastasis. Herein, we present an on-site visualization colorimetric assay for tumor-associated miRNAs using ruthenium nanoparticle decorated titanium dioxide nanoribbon (Ru@TiO2) as a peroxidase-like (POD) nanozyme. Remarkably, the Ru@TiO2nanozyme can catalyze the oxidation of chromogenic substrates through its POD-like activity, which is effectively inhibited by pyrophosphate generated during the rolling circle amplification process, thereby enabling miRNA detection through a visible colorimetric readout. This approach provides a highly sensitive and specificity assay for miRNAs in diluted human serum with a detection limit of 100 pM. It shows great potential for clinical diagnostics and biological research, offering a promising tool for early cancer diagnosis and molecular diagnostics.

In this article, we propose a novel batch Bayesian and Gaussian process enhanced subspace derivative free optimization (DFO) method to solve high-dimensional and simulation-expensive analog circuit optimization problems. The existing optimization methods, such as Bayesian optimization and trust region-based DFO, suffer from under-fitting surrogate models in high-dimensional problems, which leads to inefficient optimization and suboptimal solutions. To address this issue, we propose a novel approach that integrates a batch Bayesian querying strategy for exploring the global design space and a Gaussian process (GP) enhanced subspace DFO method for exploiting promising regions in effective low-dimensional subspace. The GP is used to approximate the gradient pattern for subspace establishment, significantly enhancing the simulation efficiency. The selection of promising regions is based on an innovative region acquisition function that estimates the weighted local expected improvement. The effectiveness of the proposed method is demonstrated on real-life analog circuits, achieving ${2.05\times - 17.65\times }$ simulation number speedup and ${1.37\times - 16.11\times }$ runtime speedup compared with the state-of-the-art optimization methods.

Dummy filling is widely applied to significantly improve the planarity of topographic patterns for the chemical mechanical polishing (CMP) process in VLSI manufacturing. In the dummy filling flow, dummy synthesis works as the key step to adjust the post- CMP profile height. However, existing dummy synthesis optimization approaches usually fail to balance the filling quality and efficiency. This article proposes a novel model-based dummy filling synthesis framework NeurFill, integrated with multiple starting points-sequential quadratic programming (MSP-SQP) optimization solver. Inside this framework, a full-chip CMP simulator is first migrated to the neural network, achieving $8134\times $ speedup on gradient calculation by backward propagation. Entrenched in the CMP neural network models, we further implement an improved version of NeurFill (pNeurFill) to alleviate the post- CMP height variation caused by dummy perimeter. After each iteration of dummy density optimization, an additional perimeter adjustment based on a given candidate dummy pattern set is applied to search for the optimal perimeter fill amount. The experimental results show that the proposed NeurFill outperforms existing rule- and model-based methods. The extra perimeter adjustment strategy in pNeurFill can achieve an average 66.97Å decreasing in height variation and 8.92% quality improvement compared to NeurFill. This will provide guidance for DFM so as to increase IC chip yield.

The utilization of seawater electrolysis is recognized as a promising method for generating H2as a substitute for conventional technology. Nevertheless, it is impeded by anode catalysts with restricted lifespans, primarily due to chloride-induced competitive chemical reactions. This study reports an La-doped Ni(OH)2nanosheet supported on Ni foam (La–Ni(OH)2/NF) as an outstanding oxygen evolution reaction electrocatalyst, which demonstrates remarkable performance in alkaline seawater. Impressively, the achievement of a high current density (500 mA cm−2) requires a low overpotential of only 434 mV, surpassing that of Ni(OH)2/NF (532 mV). In addition, La–Ni(OH)2/NF exhibits outstanding electrochemical stability for the duration of more than 200 h in alkaline seawater.

This article addresses the hydrolytic instability issue of surface chemistry and the repeatability issue of silicon (Si) nanowire (NW) biosensors. Si multi-NWs or nanograting (NG) field-effect transistors (FETs) were fabricated using semiconductor lithographic processing techniques. Then, the NG surfaces were coated with 3-aminopropyltriethoxysilane (APTES) via the vapor- and solution-phase methods. Their performance, including drift, stability, sensitivity, accuracy, and linearity of pH sensing, was evaluated. Sensors treated with both APTES deposition methods exhibit linear pH response with good sensitivity. Devices treated with vapor APTES show better linearity and sensitivity, and APTES-solution-coated devices are more stable with smaller drift. A hydrolysis process was developed to significantly improve the hydrolytic stability of the APTES-coated sensor surface. As a result, an accuracy of ?0.008 pH was achieved, which is comparable to or better than commercially available ion-sensitive FETs (ISFETs), whereas the sensor drift was significantly reduced for both sensors treated with vapor and solution APTES. This hydrolysis process has greatly improved the stability and repeatability of charge sensing of our Si NG bio-FETs.