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The recovery of valuable materials from spent lithium-ion batteries (LIBs) has experienced increasing demand in recent years. Current recycling technologies are typically energy-intensive and are often plagued by high operation costs, low processing efficiency, and environmental pollution concerns. In this study, an efficient and environmentally friendly dielectrophoresis (DEP)-based approach is proposed to separate the main components of "black mass" mixtures from LIBs, specifically lithium iron phosphate (LFP) and graphite, based on their polarizability differences.

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Deciphering pH Mismatching at the Electrified Electrode-Electrolyte Interface towards Understanding Intrinsic Water Molecule Oxidation Kinetics.

Angew Chem Int Ed Engl

January 2025

Research Center for Energy and Environmental Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan.

Unveiling the key influencing factors towards electrode/electrolyte interface control is a long-standing challenge for a better understanding of microscopic electrode kinetics, which is indispensable to building up guiding principles for designer electrocatalysts with desirable functionality. Herein, we exemplify the oxygen evolution reaction (OER) via water molecule oxidation with the iridium dioxide electrocatalyst and uncovered the significant mismatching effect of pH between local electrode surface and bulk electrolyte: the intrinsic OER activity under acidic or near-neutral condition was deciphered to be identical by adjusting this pH mismatching. This result indicates that the local pH effect at the electrified solid-liquid interface plays the main role in the "fake" OER performance.

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Echolalia, the immediate or delayed repetition of speech, is a core diagnostic criterion for autism spectrum disorder. It has been studied for over 50 years and is well-described; however, no consensus on prevalence estimates exists for echolalia's occurrence in autistic youth. The current study sought to (1) describe endorsement of echolalia-related items using parent-, teacher-, and clinician-reports in a well-validated sample of autistic youth and (2) characterize relations between echolalia and other key factors, including age, language ability, and repetitive behaviors.

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Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?

Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.

What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.

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Objectives: This pilot study aimed to identify early predictors of drug retention in patients with clinically active peripheral psoriatic arthritis who initiated or switched to therapy with biologic and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs).

Methods: Clinical and ultrasound assessments were conducted at baseline (t0) and subsequently at 1 (t1), 3 (t3), and 6 (t6) months. Ultrasound evaluations targeted joints/entheses according to PsASon-Score13 and the most clinically involved joint/enthesis/tendon or the two most clinically involved joints/entheses/tendons (MIJET and 2MIJET).

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