Advanced Engineering (Czechia)

Understanding the decalcification mechanism of calcium silicate hydrate (C–S–H) under marine environments is crucial for concrete durability. In this study, the decalcification behavior of C–S–H under a water–heat–salt environment was systematically investigated through immersion experiments and reactive molecular dynamics simulations. Experimental results showed that elevated temperature and NaCl solution significantly accelerated Ca leaching and reduced the Ca/Si ratio of C–S–H. Simulation results further revealed that both high temperature and NaCl reduce the dissolution free energy of Ca, making its release more thermodynamically and kinetically favorable. Local structure analysis indicated that Cl– disrupts Ca–Os (O atoms in silicate tetrahedra) connection through electrostatic attraction, while Na+ competes with Ca for silicate coordination sites. Elevated temperature further weakens the Ca–Os interactions and enhances the ion mobility. In contrast, the dissolution of Si atoms shows a clear energy barrier and requires higher energy due to strong Si–Os bonding. Based on these findings, an atomic-scale decalcification mechanism is proposed involving thermal activation, Cl– attraction, and Na+ competition, providing new insights into the degradation processes of C–S–H under the water–heat–salt environment.

Herein, we present a strategy for selective detection of volatile organic compounds (VOCs) by modulating the reaction pathways using SnO2-based gas sensors via loading with acidic WO3 as the receptor material. The neat SnO2 nanoparticles (NPs) were synthesized using hydrothermal treatment and calcination. Two methods were utilized to fabricate WO3–SnO2 NPs: the impregnation method and mechanical mixing. The sensing responses to various VOCs were significantly improved by finely dispersed WO3-loaded SnO2 compared to SnO2 with low-dispersed WO3. Crucially, the introduction of WO3 enhances the material’s surface acidity, which in turn changes the main reaction pathway of ethanol oxidation from dehydrogenation to dehydration. The interaction between WO3 and SnO2 NPs had a notable impact on sensor properties. WO3 loading enhanced the response to ethanol by altering the reaction pathway at 350 °C and demonstrated selective detection of acetone at 250 °C due to its receptor effect. These findings highlight the importance of material design in SnO2-based sensors, where acidic oxide receptors like WO3 effectively modulate the reaction pathway to enhance gas selectivity, offering valuable insights for developing selective gas sensors for medical diagnostics.

Raman spectroscopy is a powerful and versatile tool for battery research, elucidating the structural and bonding states of key components such as electrodes and electrolytes. This review highlights recent advancements in Raman spectroscopic techniques for both static material characterization and operando analysis of electrochemical reactions. Regarding static characterization, studies on the solvation structure of organic liquid electrolytes and the crystallographic analysis of electrode active materials are discussed. In addition, Raman-based image analysis is considered for visualizing the spatial distribution of structural components within composite electrodes. In Sec. III, we provide a comprehensive overview of ionic transport phenomena inferred from concentration variations, electrode degradation processes occurring during cycling, and the fundamental electrochemical reactions involved in electrode processes. Furthermore, a multi-scale analytical approach for all-solid-state sodium batteries has been introduced, enabling a comprehensive understanding of electrochemical processes from the atomic level to the μm scale. These advancements in Raman spectroscopy provide valuable insights into battery chemistry and significantly contribute to the development of next-generation energy storage systems.

The smart Electric Vehicle (EV)-Improved KY Converter monitoring system, which is Internet of Things (IoT)-based, provides an advanced method for improving the dependability and efficiency of EV power systems. By utilizing IoT technology, this system keeps a close eye on the enhanced KY converter's efficiency, which is essential for controlling power conversion in electric vehicles. The Adaptive Neuro-Fuzzy Inference System (ANFIS) based Maximum Power Point Tracking (MPPT) effectively operates the system at maximum power level with fast response and low oscillations. It effectively optimizes energy conversion from Photo Voltaic (PV) by dynamically modifying to the operating point under varying environmental conditions. The deployment of this intelligent monitoring system is expected to yield notable progress in the administration and functioning of electric vehicle power systems, hence augmenting the general sustainability of electric-powered transportation. The proposed system is implemented in MATLAB, employing its sophisticated simulation capabilities to analyse the PV system. This simulation framework enables for the testing and validation of key performance metrics of 98% efficiency exhibiting in boosting power extraction and grid stability.

Digital differentiator is digital circuit for differentiation of digital signal. It is often used in a wide range of fields such as sound processing, image processing, measurement and control. In this work, we propose the design of the sparse integer order differentiators which contain zero coefficients. We show the fast search algorithm for obtaining the solution of sparse integer order differentiators. Design examples show that the fast search algorithm can often find the optimal solution which is obtained by the tree structure. Thus, the effectiveness of the fast algorithm can be demonstrated.

ドライバ起因の事故を防止するために，日常の運転行動に内在する事故危険性を把握することが重要である．そのためには，ドライバの状態や行動情報をプライバシー侵害の懸念を抑えつつ，簡便・安価に，また安定して日常的に収集する手法が求められる．この課題に対して，著者らは手首装着型センサを用いたドライバ状態推定手法の開発に取り組んでいる．一方で，これまでの検証は実験室環境下に限定されていた．そこで本研究では，実際の運転環境下への適用に向けて，手首装着型センサを利用した車載計測システムを試作し，実車両上での運転行動データの収集実験を実施した．また，収集した手先加速度のデータからドライバの行動内容の推定を行い，実環境下での課題点を検証した．得られた結果より，実環境下におけるデータ収集から分析のためのデータセット構築までの一連のスキームを確立した．

本研究は, 農業集落排水施設に広く採用されている回分式活性汚泥法の処理水について, 「処理水の灌漑プロジェクトに対するISOガイドライン」の水質レベルを達成するために必要な運転管理方法を検討した。処理水のBOD濃度には, 回分槽の有機物の酸化分解や窒素化合物の硝化促進とともに活性汚泥の流出制御を通じた運転管理が重要であり, 処理水のBOD濃度とSS濃度は回分槽のDO濃度を2～3 mg L-1で管理し, SVIを100 mL g-1以下に維持することで水質が最も良好なカテゴリーAを達成できる可能性が高いことが分かった。処理水の大腸菌群数には, 消毒槽の残留塩素濃度と水理学的滞留時間, 処理水のSS濃度と水温が影響し, CT値 (消毒槽の残留塩素濃度に水理学的滞留時間を乗じた値) を2.8 mg min L-1以上に確保することでカテゴリーAの水質レベルを達成できる可能性が高く, 処理水のSS濃度水準を下げることは大腸菌群に対する不活性化効果を高める観点からも重要であると考えられた。

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