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Abstract The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end.
Y.E, B.Wang, Y.Lin, et al. “Enhancing Bio-Electrochemical Removal of Hydrophobic o-Dichlorobenzene Through Interfacial Microenvironment Modulation.” Advanced Functional Materials (2026): e76390. https://doi.org/10.1002/adfm.76390 Conflicts of Interest The authors declare no conflicts of interest. Data Availability Statement The data that support the findings of this study are available from the corresponding author upon reasonable request.
Tuning valence-variable single atomic metal for efficient antibiotic degradation and in situ chlorinated byproduct elimination under current pulsation† Electrocatalytic oxidation is a promising technique for antibiotic-contaminated wastewater treatment; however, the concomitant production of toxic chlorinated byproducts remains an obstacle.
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