Results 251 to 260 of about 288,924 (358)

Impact of Discharging Methods on Electrode Integrity in Recycling of Lithium‐Ion Batteries

open access: yesAdvanced Energy Materials, Volume 16, Issue 12, 25 March 2026.
Electrical and electrochemical discharge methods for end‐of‐life lithium‐ion batteries are compared. Electrochemical discharge better preserves the composition and layered structure of Ni‐rich cathode materials while minimizing residual lithium compounds.
Neha Garg   +3 more
wiley   +1 more source

Research on the possibility of using olive pit biomass in recycling metals from slag. [PDF]

open access: yesSci Rep
Smalcerz A   +7 more
europepmc   +1 more source

Growth Standards for Children With Smith–Magenis Syndrome (SMS)

open access: yesAmerican Journal of Medical Genetics Part A, Volume 200, Issue 3, Page 569-578, March 2026.
ABSTRACT Smith–Magenis syndrome (SMS, OMIM 182290) is a complex syndromic diagnosis marked by neurobehavioral differences and distinct facial dysmorphisms, caused by haploinsufficiency of the retinoic acid‐1 (RAI1) gene either by a pathogenic sequence variant or deletion at chromosome 17p11.2 involving a portion or all of this gene.
Julie Hoover‐Fong   +10 more
wiley   +1 more source

Incorporation of Coconut Fibers Into Clay‐Based Mortars: A Study on Mechanical and Microscopic Behavior

open access: yesInternational Journal of Ceramic Engineering &Science, Volume 8, Issue 2, March 2026.
Clay‐based mortar reinforced with coconut fibers showed improved flexural strength and promising mechanical performance, despite shrinkage‐induced cracks. The study highlights the potential of using waste and natural fibers in eco‐efficient mortars, emphasizing the importance of curing and moisture control.
Gabriela Machado Guimarães Ferreira   +1 more
wiley   +1 more source

Integrated Machine Learning Approach to Predicting the Dissolution of Nonmetallic Inclusions in Metallurgical Slags

open access: yescScience, Volume 2, Issue 1, March 2026.
ABSTRACT The dissolution behavior of nonmetallic inclusions in slag is critical for determining steel cleanliness. In this study, a machine learning (ML) model was developed to predict inclusion dissolution time. A key contribution of this work is the construction of a comprehensive database by integrating literature‐based experimental data with a ...
Jintao Song   +3 more
wiley   +1 more source

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