Results 221 to 230 of about 341,166 (307)

Lunar and Martian Regolith Simulants Desorb and Weather after Exposure to Bioregenerative Life Support System Effluent. [PDF]

open access: yesACS Earth Space Chem
Coker HR   +8 more
europepmc   +1 more source

Modeling the separation of water‐in‐oil emulsions in continuously fed gravity settlers using millifluidic experiments

open access: yesAIChE Journal, EarlyView.
Abstract Emulsion separation remains a persistent challenge in chemical and process industries due to the metastable nature of dispersed droplets. In gravity separators, the overall separation rate is governed by the formation of a densely packed zone (DPZ) of deforming and coalescing droplets that mediates between the dispersed and continuous phases ...
Andrei Zlobin   +8 more
wiley   +1 more source

A peritoneal effluent sequencing assay that removes environmental DNA contamination in peritoneal dialysis patients. [PDF]

open access: yesClin Kidney J
Djomnang LK   +8 more
europepmc   +1 more source

Asking the 5 W's for designing next‐generation bioprocessing

open access: yesAIChE Journal, EarlyView.
Abstract Biotechnology is expanding beyond traditional, centralized fermentation and toward next‐generation bioprocessing paradigms that emphasize flexible deployment outside the laboratory with application‐specific performance. However, many bioprocesses fail to translate beyond proof‐of‐concept into industrially viable systems because early design ...
Sangdo Yook   +4 more
wiley   +1 more source

Clinical utility of pharyngeal high‐resolution manometry with impedance for upper esophageal sphincter dysfunction in gastroenterology

open access: yesAdvances in Digestive Medicine, EarlyView.
Abstract Pharyngeal high‐resolution manometry with impedance (P‐HRM‐I) is an established assessment method used to evaluate pharyngeal swallowing. It provides precise quantification of swallowing biomechanics that enable the detection of alterations in swallowing physiology.
Mistyka Schar   +5 more
wiley   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai   +8 more
wiley   +1 more source

Home - About - Disclaimer - Privacy