A synergistic strategy via trace cobalt doping: Mesoporous engineering and electronic structure modulation of ZIF-8 derivatives for superior acetone sensing detection.

Advances in cellular and structural engineering of brain organoids for disease modeling: A Comprehensive Review.

Oleic acid modification enables lysosomal escape of phthalocyanine photosensitizer for effective bladder cancer treatment.

Quantitative biaxial stress measurement using ultrasonic LCR wave-based time-of-flight analysis.

DFT-guided design of molecularly imprinted sensor for fluorescence and visualization dual-mode oxytetracycline detection.

Sandwich-type complex-activated DNA circuit for highly sensitive and versatile detection of protein biomarkers for monitoring bone health.

In structural engineering, concrete compressive strength (CS) is among its most essential performance characteristics. Although many machine learning models have been developed for predicting this parameter, many suffer from limited transparency. This study developed an accurate and explainable ML model based on the Levenberg-Marquardt algorithm for estimating concrete CS. A total of 1030 laboratory measured concrete CS data points was used for model development. Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), average absolute percentage relative error (AAPRE), and average percentage relative error (APRE). For the test dataset, the model yielded 0.949 for R2, 3.77MPa for RMSE, 10.32% for AAPRE and - 1.92% for APRE. Sensitivity analysis identified the binder proportion as the most contributing variable with a factor of + 0.52. This is trailed by the amount of superplasticizer (+ 0.39) and the age of curing the sample (+ 0.35). Moreover, the proposed model is presented in an explicit mathematical form for straightforward integration into relevant software; an attribute rarely addressed in existing ML based studies. The physical trend analysis confirmed consistency with established concrete strength behaviour. Finally, the development of a user friendly graphical user interface for the framework facilitates easy deployment of the model for rapid estimation of concrete CS.

4D printing and in vitro studies of bi-active bi-layer self-forming biocompatible tubular structures for advanced vascular tissue engineering

Molecular tailoring of Zn2+solvation structure and interface engineering by PUL polysaccharide additive for Ultrastable Aqueous Zinc-Ion batteries

Accelerated simulation and design optimization of elastic rod networks with a spline-based least-squares formulation

The global transition toward renewable energy and carbon neutrality has sharply increased the demand for energy-storage systems with higher energy density, improved safety, and extended service life. Despite the dominance of lithium-ion batteries, their development is greatly limited by the flammability and electrochemical instability of liquid electrolytes. Solid-state lithium batteries (SSLBs) provide a promising alternative because solid-state electrolytes (SSEs) eliminate electrolyte leakage, enhance thermal stability, and enable the use of high-voltage cathodes and lithium-metal anodes. Among candidate cathode materials, high-nickel layered oxides (LiNixCoyMn1- x - yO2, x≥0.8) are the most viable for practical deployment, owing to their high specific capacity, moderate cost, and industrial maturity. However, their integration with SSEs introduces severe challenges, including structural degradation, oxygen release, and interfacial instability, which collectively impede lithium-ion transport and compromise cycling durability. This review summarizes recent progress in SSLBs with high-nickel cathodes, focusing on (1) structural and surface engineering of high-nickel cathodes, (2) optimization of oxide-, sulfide-, halide-, and polymer-based SSEs, and (3) interface-engineering strategies, including buffer layers and in situ interfacial design. Finally, perspectives are provided on material innovation, interfacial characterization, and scalable manufacturing, aiming to guide the development of next-generation SSLBs that combine high energy density with intrinsic safety.

Embedding ultrasmall Ru nanoparticle catalytic sites on Ni3Fe encapsulated carbon nanotubes for efficient and durable water-splitting.

Phenyl-Driven structural engineering of Pyridyl-Based Schiff bases for enhanced Photophysical and Third-Order nonlinear Optical Responses: An Experimental and DFT Study

This study investigates the influence of temperature on the plastic deformation and hardening behavior of Thermo-Mechanical Controlled Process (TMCP) S1100 ultrahigh-strength steel at different temperatures up to 900 °C. TMCP steels, recognized for their reduced carbon footprint and superior strength-to-weight ratio, are increasingly prevalent in structural engineering applications, necessitating a thorough assessment of their performance under fire conditions. The Swift and Voce constitutive models were evaluated for their efficacy in predicting plastic deformation across various temperatures. While both models captured the general stress–strain response, inconsistencies were observed at low and high strain levels. To address these limitations, a coupled Swift–Voce model was employed, yielding a more robust correlation between plastic strain and stress at elevated temperatures. Microstructural analyses clarified key hardening mechanisms, including dislocation multiplication, dynamic strain aging, dynamic recovery, martensite decomposition, and austenite formation, over the temperature range from room temperature to 900 °C. The observed agreement between the Voce and Kocks-Mecking models, both sensitive to microstructural evolution, substantiates the reliability of the derived correlations between the microstructure and hardening parameters. Finally, equations were proposed to establish the relationship between plastic deformation parameters and deformation temperature. These insights advance the understanding of TMCP S1100’s applicability for fire-resistant structural applications.

Structural engineering of MOF-derived bismuth/carbon hybrids for high-efficiency chloride ions capture via synergistic capacitive-faradaic storage

In this study, the ground-state structural and electronic band structure of cubic SrThO3 (space group Pm3̅m, No. 221) were comprehensively investigated through first-principles calculations within the framework of density functional theory (DFT) at ambient conditions, as implemented in the Quantum Espresso software and code. The structural and electronic characteristics of pristine SrThO3 were calculated using the Generalized Gradient Approximation (GGA) within the Perdew Burke Ernzerhof (PBE) exchange–correlation functional. The structural parameters show good agreement with previously reported theoretical data, validating the reliability of the adopted methodology. The electronic band gap was calculated as 1.986 eV with direct band characterization. This paper focused on the investigation of the structural model and electronic behavior of pristine SrThO3 for understanding potential use in optoelectronics devices.

Secondary structure engineering of an amphipathic arginine-rich CPP using non-proteinogenic amino acids for enhanced cell-membrane permeability.

Structural engineering of CeOx-confined 2D ordered mesoporous carbon for enhanced elemental mercury adsorption and wet desorption-regeneration

Effect of basalt fiber on viscoelastic properties of hydraulic asphalt concrete

Carbon nanotubes (CNTs), combining excellent electrical and optoelectronic properties with low-temperature processability, provide a compelling materials platform for monolithic three-dimensional (M3D) integration that unifies digital logic in complementary field-effect transistor (CFET) architecture and functional sensing elements with three-dimensionally structured nondigital functional blocks. However, such a fully integrated system has not yet been experimentally demonstrated. Here, we report CNT-based digital circuits implemented in a true CFET architecture, in which vertically stacked P- and N-FETs share an identical footprint and exhibit well-balanced performance through structural engineering. A full suite of logic functions, including inverters, NOR, OR, NAND, AND gates, as well as a 4-transistor static random-access memory cell and a five-stage ring oscillator are successfully demonstrated. The CFET inverters exhibit rail-to-rail operation with large noise margins and a peak voltage gain of 147 at a supply voltage of 1 V, while maintaining a gain of 9.7 with only 3.3 pW static power consumption at 0.2 V. In parallel, CNT photodiodes are vertically stacked and cascaded to form a 3D optical sensor that delivers nearly twice the photovoltage of planar counterparts. By monolithically integrating the 3D CNT photodiode with a CNT-based CFET inverter, we further demonstrated a prototype "sensing-and-computing" module in which optical power and spectral information are directly sensed and processed within a single monolithic CNT-based block. This work establishes CNTs as a unified platform for high-density, low-power M3D integration toward near-/in-sensor and edge-computing applications.