Lunar and Martian Regolith Simulants Desorb and Weather after Exposure to Bioregenerative Life Support System Effluent. [PDF]
Coker HR +8 more
europepmc +1 more source
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
Anaerobic sulfide removal involves an intricate interplay between biomass, biosulfur, and solutes.
Linssen R, de Smit S, Ter Heijne A.
europepmc +1 more source
A peritoneal effluent sequencing assay that removes environmental DNA contamination in peritoneal dialysis patients. [PDF]
Djomnang LK +8 more
europepmc +1 more source
Asking the 5 W's for designing next‐generation bioprocessing
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
Toward Industrial Application of Cyanobacterial Biosorption: Insights From Real Electroplating Effluents. [PDF]
Ciani M +4 more
europepmc +1 more source
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
Pilot-Scale Evaluation of Flat-Sheet Membrane Bioreactor for In Situ Retrofitting Textile Dyeing Wastewater Treatment Plant. [PDF]
Zhou C, Wei C, Yu H, Rong H, Xiao K.
europepmc +1 more source
A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction
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
Chlorella vulgaris for Domestic Wastewater Treatment: Bibliometric Trends and Experimental Evaluation in Synthetic Effluent. [PDF]
Melo LBU +5 more
europepmc +1 more source

