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2025年09月嘲风作品集（二）

已有 1451 次阅读 2025-10-31 13:20 |个人分类:作品发表|系统分类:论文交流

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▲ Vol 59 Issue 36 | September 16 , 2025

Janus Electrocatalytic Membrane Enables Tunable Redox via Sequential Tactics toward Ultrafast Water Decontamination

Ni Yan, Jiaming Zhang, Tengfei Ren, Mengxi Yin, Xia Huang, and Xiaoyuan Zhang

Emerging contaminants (ECs) in water are a prominent environmental concern worldwide. Despite advanced oxidation or reduction being appealing transformation approaches, existing technologies face challenges in adaptability to the removal of both electron-rich ECs and ECs with electron-withdrawing moieties. Here, a Janus electrocatalytic membrane was fabricated to induce hydroxyl radicals (•OH) and atomic hydrogen (H*) simultaneously and tune redox processes via sequential tactics to achieve adaptable and ultrafast removal of diverse ECs. The Janus electrocatalytic carbon-fiber membrane with single-atom (SA) Fe and Ni anchored on two different sides, respectively, exhibited an excellent performance in the degradation of various ECs and treatment of the secondary effluent of pharmaceutical wastewater. Model ECs like propranolol and chloramphenicol were 100% removed at a high water flux (680 L m–2 h–1) and low energy consumption (<0.015 kWh m–3 log–1). In the electrofiltration sequence of Side-Fe to -Ni, the •OH yield was enhanced due to the flow-enhanced mass transfer of Side-Fe-induced H2O2 to Side-Ni-induced H* and the subsequent reaction to form •OH, favoring electron-rich organic degradation. While in the opposite sequence, the process of H*-mediated reduction followed by •OH-mediated oxidation achieved thermodynamical superiority, favoring the degradation of ECs with electron-withdrawing groups. This study highlighted a new reversible membrane design enabling tunable redox for the removal of various ECs from wastewater.

https://pubs.acs.org/doi/10.1021/acs.est.5c03417

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▲ Vol 59 Issue 37 | September 23 , 2025

Using Environmental Mixture Exposure-Triggered Biological Knowledge-Driven Machine Learning to Predict Early Pregnancy Loss

Mengyuan Ren, Tianxiang wu, Han Zhang, shuo Yang, Luzhao, Lili zhuang, Qun Ly, Xikun Han, Bo Pan, Tiantian L, ingchuan Xue, Yuanchen chen, Michael S. Bloom, Mingliang Fang, and Bin Wang

The assessment of how environmental mixture exposures affect reproductive health faces difficulties. While knowledge graph networks offer valuable advantages in biological interpretation and prediction, their application in epidemiological studies, particularly in a small sample size setting, remains scarce. We recruited 116 women undergoing in vitro fertilization and embryo transfer (IVF-ET) treatment in Beijing and Yantai City, China. Among them, 55 women were diagnosed with early pregnancy loss (EPL), while 61 achieved clinical pregnancy. Clinical records, and paired hair, serum, and follicular samples were collected, with 16 per- and polyfluoroalkyl substances (PFAS) and 41 metal(loid)s measured. We developed a framework coupled with biological knowledge graph-based networks (BKGNs) and machine learning (ML) to predict EPL. Our BKGNs integrate chemical-specific biological pathways, i.e., Gene Ontology (GO) and protein, with individual-level mixture exposure data. The GO-integrated model, with an area under the curve (AUC) of 0.876, outperformed others (AUC = 0.819), even when the sample size decreased to 60% of the total. Additionally, this framework deciphered critical exposures (e.g., serum selenium and chromium) and biological perturbations (e.g., cell population proliferation and apoptotic nuclear changes), linking mixture exposure to EPL. Our proposed novel framework is both robust and cost-effective, offering a mechanistic lens for predicting exposure-associated health outcomes.

https://pubs.acs.org/doi/10.1021/acs.est.5c05389

▲ Vol 37 Issue 38 | September 25 , 2025

Alkoxy Side Chain Engineering in Metal-Free Covalent Organic Frameworks for Efficient Oxygen Reduction

Zhongping Li, Zhaoying Wang, Songlin Zhao, Jeong-Min Seo, Changqing Li, Yucheng Jin, Siliu Lyu, Jian Li, Feng Tang, Won-Yeong Kim, Zonghoon Lee, Sang-Yong Lee, Jong-Beom Baek

Efficient Oxygen Reduction

In article number 2501603, Jong-Beom Baek and co-workers illustrate the tunable oxygen reduction reaction (ORR) performance of metal-free covalent organic frameworks (COFs) through alkoxy side-chain engineering. The alkoxy-functionalized COFs exhibit enhanced hydrophilicity and optimized pore wall electronic environments, enabling stronger adsorption of water and *OOH intermediates. This study highlights a new strategy to tailor the COF-based electrocatalysts via molecular side-chain modulation.

https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.70601

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