WATER RESEARCH
·
2024
·
IF 11.4
Yangmin Ren,Mingcan Cui,Yongyue Zhou,Shiyu Sun,Fengshi Guo,Junjun Ma,Zhengchang Han,Jooyoung Park,Younggyu Son,Jeehyeong Khim
Permeable reactive barrier (PRB) is an important groundwater treatment technology. However, selecting the optimal reactive material and estimating the width remain critical and challenging problems in PRB design. Machine learning (ML) has advantages in predicting evolution and tracing contaminants in temporal and spatial distribution. In this study, ML was developed to design PRB, and its feasibility was validated through experiments and a case study. ML algorithm showed a good prediction about the Freundlich equilibrium parameter (R 2 0.94 for K F , R 2 0.96 for n). After SHapley Additive exPlanation (SHAP) analysis, redefining the range of the significant impact factors (initial concentration and pH) can further improve the prediction accuracy (R 2 0.99 for K F , R 2 0.99 for n). To mitigate model bias and ensure comprehensiveness, evaluation index with expert opinions was used to determine the optimal material from candidate materials. Meanwhile, the ML algorithm was also applied to predict the width of the mass transport zone in the adsorption column. This procedure showed excellent accuracy with R 2 and root‐mean‐square‐error (RMSE) of 0.98 and 1.2, respectively. Compared with the traditional width design methodology, ML can enhance design efficiency and save experiment time. The novel approach is based on traditional design principles, and the limitations and challenges are highlighted. After further expanding the data set and optimizing the algorithm, the accuracy of ML can make up for the existing limitations and obtain wider applications.
研究领域:
环境工程
·
水文地质学
·
污染控制
·
环境修复技术
·
应用机器学习
✓ 已验证
DOI
高影响因子
ACS Sustainable Chemistry & Engineering
·
2025
·
IF 7.3
Yiming Shen,Jiyizhe Zhang,Xiao Liang,Kai Wang
Electrochemical synthesis, with its mild conditions and high atom economy, offers a sustainable alternative for creating high-value products. However, for electrochemical reactions, commonly involving a larger parameter space than traditional chemical reactions, it is challenging to efficiently identify optimal reaction conditions or investigate the underlying principles. In this article, a two-step framework of AI-guided exploration is proposed for investigating the Co(II)-mediated electro-oxidation of p-xylene. A multiobjective diversity-driven Bayesian optimization approach is applied for the first step to search the space of operation, identifying Pareto-optimal solutions that balance maximizing reaction yield and minimizing Faradaic contribution. These solutions are then analyzed by employing SHAP (SHapley Additive Explanations) and additional experiments in the second step to investigate the reaction mechanisms. The optimized conditions achieved higher yields and lower energy consumption compared to reported studies in the literature while avoiding the use of toxic solvents. The framework demonstrates the advantage of AI-guided optimization to accelerate the research of electrochemical reactions by significantly reducing the cost of experiments and reducing reagent consumption as well as waste generation, presenting a new path for exploring sustainable chemical processes.
研究领域:
电化学
·
有机合成
·
催化化学
·
绿色化学
·
计算化学
·
反应工程
·
可持续能源
✓ 已验证
产品关联
DOI
高影响因子
JOURNAL OF THE AMERICAN CERAMIC SOCIETY
·
2025
·Vol. 108
·期 12
·e70209
·
IF 3.8
Zirui Du,Chaokun Song,Kang Guan,Pinggen Rao,Xiaohui Yang,Longteng Bai,Shengbo Shi,Jing Wang,Yongsheng Liu,Qingfeng Zeng
Improving the ablation resistance of carbon fiber-reinforced silicon carbide (C/SiC) composites is essential to meet the stringent demands of ultra-high-temperature applications, but traditional empirical design approaches are resource-intensive and time-consuming. This study presents a novel data-driven methodology integrating machine learning (ML) with experimental validation to optimize C/SiC composite ablation resistance. Through systematic data preprocessing and feature analysis of 102 experimental samples, an XGBoost regression model was developed, achieving satisfactory prediction accuracy (mean absolute error < ∼0.075, mean squared error < ∼0.015, and coefficient of determination [R 2 ] > ∼0.75) for ablation rate. SHapley Additive exPlanations (SHAP) analysis revealed that modifier properties, particularly standard enthalpy and melting point, predominantly influence ablation resistance. The ML-guided design strategy, implemented through Bayesian optimization, led to the successful fabrication of ZrB 2 -modified C/SiC composites with exceptional ablation resistance. The optimized composite, containing approximately 20.0 vol.% ZrB 2 , achieved a linear ablation rate of 2.083 µm/s under oxyacetylene torch testing, representing a significant improvement over conventional compositions. Microstructural analysis confirmed the formation of a dense SiO 2 ‒B 2 O 3 protective layer, validating the predicted mechanism of enhanced ablation resistance. This work establishes a robust framework for accelerated development of ultra-high-temperature ceramics and demonstrates the efficacy of ML-driven approaches in materials design optimization. An object-oriented software with interactive graphical user interface has been developed. These methodologies have been integrated into an interactive software, Modified C/SiC Ablation Rate intelligent design Software (MARS), creating an efficient tool for the accelerated design of C/SiC composites with tailored ablation performance.
研究领域:
材料科学
·
复合材料工程
·
高温结构材料
·
计算材料学
·
先进陶瓷
✓ 已验证
产品关联
DOI
高影响因子
ANALYTICAL CHEMISTRY
·
2025
·
IF 6.7
Jiaming Zhang,Yanxia Ma,Jinbo Cao,Ai Li,Siying Liu,Xixiang Yang,Li Wang,Junhua Li,Xiaogang Hu
To address the challenges in detecting chloramphenicol (CAP) in complex food matrices, this study developed a magnetic solid-phase microextraction coupled with a high-performance liquid chromatography (MSPME-HPLC) system that integrates machine learning and molecular recognition. The system employs magnetic SiO2@Fe3O4 nanoparticles as the carrier and combines the dual recognition functions of carboxylated pillar[5]arene (CP[5]A) and aptamer (Apt) to create a nanocomposite separation material, Apt-MIP-CP[5]A@SiO2@Fe3O4 (AC-MSF). Bayesian optimization and six machine learning models (e.g., XGBoost, SVM) were utilized to dynamically optimize polymerization and extraction conditions. SHAP interpretability analysis identified aptamer dosage and Mg2+ concentration as critical parameters, with ML-recommended conditions reducing cross-linker usage by 22.2% and polymerization time by 57%. Kinetic simulations elucidated the synergistic recognition mechanism: CAP’s nitrobenzene group embeds in CP[5]A’s cavity via strong nonbonded interactions (total energy: −50 to −250 kJ/mol) and H-bond networks (1–4 bonds), while the aptamer binds CAP specifically at DT-18 via H-bonding (ΔG: −34.64 kcal/mol). Circular dichroism spectroscopy confirmed the independent yet synergistic operation of the dual recognition modes, with a synergy factor of 1.4. The system achieved a detection limit of 0.69 μg/L (linear range: 0.004–0.2 mg/L) and recoveries of 85.0%–96.2% in honey, milk, and egg samples.
研究领域:
分析化学
·
食品安全
·
生物传感器
·
纳米技术
·
材料科学
·
机器学习
·
分子建模
·
环境监测
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）