Open Access

Review

Article ID: 4042

Advanced engineered nanomaterials for next-generation flexible wearable bioelectronics interface: A comprehensive review

by Salaman Ahamad, Shaista Fatima, Sameera Zafar, Mohd Hasan Mujahid

Materials Technology Reports, Vol.4, No.1, 2026;

Nanomaterials have been found to possess tremendous potential as novel enabling elements in the highly dynamic field of flexible wearable bioelectronics. This is owing to their ability to allow for smooth interfacing between artificially designed devices and complex biological systems at both the molecular and cellular levels. Their highly desirable physicochemical properties, including elevated surface-area-to-volume ratios, quantum confinement, electronic conductivity, and mechanical flexibility, make nanomaterials promising candidates for novel wearable electronic devices that can find applications in continuous biosensing, bioactuation, neural interfacing, and real-time bioimaging. Most importantly, they can allow for the realization of basic elements of bioelectronics, such as bio-memory devices, biological logic gates, and biomolecule-integrated processors. These can potentially allow for overcoming the limitations of conventional rigid silicon-based electronic devices through intelligent integration with biomolecular recognition. This review article presents a systematic and comprehensive discussion on the most prominent classes of engineered nanomaterials utilized in the development of flexible wearable bioelectronics, including carbon-based nanostructured materials, intrinsically conducting polymers, metallic and bimetallic nanomaterials, as well as multifunctional nanocomposites. In addition, the review article places significant emphasis on the elucidation of the most significant structure-function relationships in the context of the most prominent application areas, including epidermal biosensing devices, soft neural interfaces, as well as biomimetic tissue engineering constructs. In addition, the most promising trends in the development of flexible, stretchable, as well as skin-conformable bioelectronic architectures are also critically discussed in the article. The current challenges in the development of flexible wearable bioelectronics, including the most prominent issues in the context of biocompatibility, long-term stability, and scalability, are also discussed in the article.

Open Access

Article

Article ID: 4128

Realization and optimization of super-junction structures for high-efficiency silicon carbide power devices

by Shijing Wang, Mingyu Zhang, Jie Liang, Leyi Tu, Jian Li, Zhiqian Gui, Jiale Zhu, Qian Wu, Deqin He, Haixin Qiu, Zhaoxiang Wang

Materials Technology Reports, Vol.4, No.1, 2026;

In this study, various silicon carbide (SiC) trench and via pattern etching processes are investigated, and high-aspect-ratio super-junction (SJ) structures are successfully fabricated. SiC SJ trenches are promising for ultra-high-voltage power device applications. Using a SiO₂ hard mask, SiC trenches with aspect ratios from 3:1 to 15:1 and depths exceeding 21 μm are prepared. Etch selectivity (SiC/SiO₂) is calculated based on the etched thicknesses of SiC and SiO₂ under the same process, and the selectivity can exceed 10:1 by optimizing hardware configuration and process parameters, especially gas combination and equipment settings. The significant effect of sidewall roughness transfers from the oxide hard mask to the SiC substrate is revealed. A smooth and optimized oxide hard mask sidewall is the key to reducing the final SiC sidewall roughness during pattern transfer. Full-wafer uniformity is improved by multiple tuning methods, including power ratio split, gas ratio split, temperature distribution control, and refined process parameters. Excellent uniformity is achieved: SiC trench critical dimension (CD) variation below 2%, SiC etch depth uniformity below 1%, and sidewall angles above 88° across the entire wafer. Long-term tool stability is verified over 10 consecutive months of etch rate monitoring with standard monitor wafers. The etch rate variation is controlled within 3% and uniformity below 2%, demonstrating reliable mass-production manufacturability of the SiC trench process.

Open Access

Review

Article ID: 4107

Electrochemical adsorption of water pollutants based on carbon materials: Materials, mechanisms, and applications

by Xiaoling Wu, Manni Liang, Ruiqing Su, Shui He, Jieyi Yang, Xingyuan Gao

Materials Technology Reports, Vol.4, No.1, 2026;

Carbon-based electrochemical adsorption technology has become an increasingly important method in the field of advanced water pollution treatment, and it is expected to provide critical technical support for water environmental restoration and drinking water safety. Traditional water treatment technologies have obvious limitations, such as poor selectivity, high energy consumption, difficulty in material regeneration, and the risk of secondary pollution. In this context, it is crucial to develop new water treatment technologies that are efficient, stable, low-consumption, and environmentally friendly. Carbon-based electrochemical adsorption technology makes full use of the superior electrical conductivity, high specific surface area, tunable surface chemistry, and relatively low cost of carbon materials, showing great potential in water pollution control. This paper systematically reviews carbon-based electrochemical adsorption technology, summarizes key adsorption materials, removal mechanisms for various pollutants, optimization strategies related to system configuration and operating parameters, and the latest application developments in different water treatment fields. The article clearly distinguishes the roles of non-Faradaic (capacitive) processes based on double-layer charging and Faradaic processes involving electron transfer in pollutant enrichment and transformation, constructing a clear mechanistic framework. Furthermore, the paper critically analyzes the main challenges faced by this technology, including the synergistic optimization of material performance, in-depth analysis of interfacial mechanisms, the complexity of actual water bodies, system-scale application, and long-term operational stability, and proposes future research directions to promote its engineering and large-scale application.

Open Access

Article

Article ID: 3791

Method of forming quantum dots for lenses of technical vision cameras in the infrared and ultraviolet ranges

by Mortin Konstantin

Materials Technology Reports, Vol.4, No.1, 2026;

This paper presents a comprehensive methodology for the formation of quantum dots (QDs) with tailored optical properties, designed for integration into machine vision camera lenses operating in the ultraviolet radiation (UV) and infrared radiation (IR) spectral ranges. Based on a self-consistent solution of the Schrödinger–Poisson equations and the non-equilibrium Green’s function (NEGF) formalism, a predictive model was developed to determine QD energy levels and spectral characteristics with an error below 1%. Experimentally synthesized CdSe QDs with a radius of 4.0 nm exhibited an emission energy of 1.864 eV (λ ≈ 665.2 nm) and a photoluminescence quantum yield of 98.8%. QD integration into a PDMS polymer matrix via spin-coating, followed by dual-layer encapsulation (ALD Al₂O₃ 20 nm and Parylene C 2 μm), ensured optical transparency >95% in the visible range and a controlled refractive index shift of Δn ≈ 0.009 at a 1.0% volume fraction. A surface coverage density of ~1.80 × 10¹² QDs/cm² was achieved, with an inter-dot spacing of 23.6 nm and size control accuracy of ±0.3 nm. Accelerated aging tests confirmed high operational stability: after 100 thermal cycles (−40/+85 °C), the quantum yield decreased by only 4.2%, and after 1,000 h at 85 °C/85% RH, by 7.8%. The proposed methodology is fully compatible with industrial micro- and nanofabrication processes, enabling scalable production of energy-efficient multispectral machine vision cameras with enhanced spectral selectivity, sensitivity, and reliability for industrial inspection, robotics, and scientific applications.

Open Access

Perspective

Article ID: 4095

Quantum dots technologies—On the edge of a boom

by Ayesha Kausar

Materials Technology Reports, Vol.4, No.1, 2026;

Quantum dots are unique nanoentities (few nm in size) having a semiconducting structure and promising surface, optical, electronic, electrical, thermal, conductive, mechanical, fluorescence, quantum confinement, and other quantum characteristics. Remarkable features of these zero-dimensional nanoparticles depend upon their types based on core materials and size differences. Quantum dots have been synthesized by various top-down or bottom-up strategies. This perspective article presents comprehensive info about diverse aspects of quantum dots, including their fundamentals, types, synthesis tactics, intrinsic features, practical applications, and potential for real-world industries. With a focus on their promising structure-property-performance profiles, the respective literature is critically analyzed and conversed. Given the promising outcomes, quantum dots depicted technological worth for wide-ranging industrial areas, including semiconducting devices, protective coatings, environmental, medical, and several other fields. However, several stumbling blocks need to be overcome to attain industrial scale success, including optimized synthesis/procedures, reproducibility, economics, non-toxicity, environment, and sustainability.

Open Access

Article

Article ID: 4151

Automated quality control of recycled aggregates via deep learning: A unified framework for instance segmentation and mass estimation

by Jérôme Lux, Pierre-Yves Mahieux, Philippe Turcry

Materials Technology Reports, Vol.4, No.1, 2026;

The large-scale use of recycled aggregates (RA) in high-grade construction applications is currently hindered by the high variability of their physical properties. Current quality control relies on manual sorting, which is labor-intensive and limits scalability. This study presents RAMSES (Recycled Aggregates Mass estimation and Segmentation), an automated framework based on deep learning, designed to bridge the gap between high-speed production and rigorous material characterization. A central contribution of this work is the introduction of a large-scale, publicly available dataset comprising 90,000 labeled and batch-weighed aggregate instances. This extensive dataset supports a strong statistical robustness across diverse RA compositions and serves as a benchmark for automated waste characterization. Using this dataset, RAMSES performs simultaneous instance segmentation and direct mass estimation from 2D images. By integrating a dual-branch architecture, the model effectively decouples morphological features from instance-dependent density factors. The framework achieves high precision in particle identification (mean Average Precision mAP@[0.5:0.95] = 0.84, mAP@0.5 = 0.91) and a 0.3% relative error in total mass prediction, which meets industrial requirements for batch monitoring. By providing a scalable alternative to manual inspection, this approach improves the consistency of RA-based concrete mixes, directly supporting the transition to a circular construction economy.