Keyword search (4,164 papers available)

"Mixture" Keyword-tagged Publications:

Title Authors PubMed ID
1 Trajectories of Alcohol-Related Problems Among First-Year Nursing Students: Nature, Predictors, and Outcomes Cheyroux P; Morin AJS; O' Connor RM; Colombat P; Vancappel A; Eltanoukhi R; Gillet N; 41797206
PSYCHOLOGY
2 Scientists warning: we must change paradigm for a revolution in toxicology and world food supply Seralini GE; Jungers G; Andersen A; Antoniou M; Aschner M; Bacon MH; Bertrand M; Bohn T; Bonfleur ML; Bücking E; Defarge N; Djemil R; Domingo JL; Douzelet J; Fagan J; Fournier T; Garcia JLY; Gil S; Hervé-Gruyer P; Hilbeck A; Hilty L; Huber D; Joyeux H; Khan I; Kouretas D; Lemarchand F; Loening U; Longo G; Mesnage R; Nikolopoulou DI; Panoff JM; Parente C; Robinson C; Scherber C; Sprangers D; Sultan C; Tsatsakis A; Vandelac L; Wan NF; Wynne B; Zaller JG; Zerrad-Saadi A; Zhang X; 41551494
CHEMBIOCHEM
3 Optimizing Mixtures of Metal-Organic Frameworks for Robust and Bespoke Passive Atmospheric Water Harvesting Harriman C; Ke Q; Vlugt TJH; Howarth AJ; Simon CM; 41427123
CHEMBIOCHEM
4 Deep clustering analysis via variational autoencoder with Gamma mixture latent embeddings Guo J; Fan W; Amayri M; Bouguila N; 39662201
ENCS
5 Developmental heterogeneity of school burnout across the transition from upper secondary school to higher education: A 9-year follow-up study Nadon L; Morin AJS; Gilbert W; Olivier E; Salmela-Aro K; 39645324
PSYCHOLOGY
6 Self-consolidating concrete: Dataset on mixture design and key properties Amine El Mahdi Safhi 38533116
ENCS
7 Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency Al-Bazzaz H; Azam M; Amayri M; Bouguila N; 37837127
ENCS
8 Entropy-Based Variational Scheme with Component Splitting for the Efficient Learning of Gamma Mixtures Bourouis S; Pawar Y; Bouguila N; 35009726
ENCS
9 Mixtures of rare earth elements show antagonistic interactions in Chlamydomonas reinhardtii Morel E; Cui L; Zerges W; Wilkinson KJ; 34175518
BIOLOGY
10 BioMiCo: a supervised Bayesian model for inference of microbial community structure. Shafiei M, Dunn KA, Boon E, MacDonald SM, Walsh DA, Gu H, Bielawski JP 25774293
BIOLOGY

 

Title:Optimizing Mixtures of Metal-Organic Frameworks for Robust and Bespoke Passive Atmospheric Water Harvesting
Authors:Harriman CKe QVlugt TJHHowarth AJSimon CM
Link:https://pubmed.ncbi.nlm.nih.gov/41427123/
DOI:10.1021/acsengineeringau.5c00051
Publication:ACS engineering Au
Keywords:MOF mixturesatmospheric water harvestinglinear programmingmetal-organic frameworksoptimization
PMID:41427123 Category: Date Added:2025-12-22
Dept Affiliation: CHEMBIOCHEM
1 School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, Oregon 97331, United States.
2 Engineering Thermodynamics, Process & Energy Department, Delft University of Technology, Leeghwaterstraat 39, Delft 2628CB, The Netherlands.
3 Department of Chemistry and Biochemistry, Concordia University, 7141 Sherbrooke St W, Montréal, Quebec H4B 1R6, Canada.

Description:

Atmospheric water harvesting (AWH) is a method to obtain clean water in remote or underdeveloped regions including, but not limited to, those with an arid or desert climate. For passive (i.e., relying on ambient cooling and, for heating, natural sunlight?as opposed to an external power source), adsorbent-based AWH, an adsorbent bed is employed to capture water from cold, humid air at nighttime, while during the daytime the bed is then exposed to natural sunlight to heat it and desorb the water for collection. Metal-organic frameworks (MOFs) are tunable, nanoporous materials with suitable water adsorption properties for comprising this adsorbent bed. The water delivery by the MOF adsorbent bed in a passive AWH device depends on (1) the nighttime, capture conditions (temperature and humidity) and daytime, release conditions (temperature, humidity, and solar flux) and (2) the structure(s) of the MOF(s) comprising the bed, which dictate MOF-water interactions. Notably, the capture and release conditions vary from region-to-region and season-to-season and fluctuate from day-to-day, while different MOFs offer different water adsorption isotherms. Consequently, we propose (1) comprising the adsorbent bed for passive AWH with a mixture of MOFs and (2) tailoring this MOF mixture to particular geographic regions and time frames. We hypothesize each MOF in the mixture can specialize in delivering water under different capture and release conditions, ensuring the adsorbent bed delivers adequate water on every day?despite fluctuations in temperature, humidity, and solar flux. Herein, we develop an optimization framework to determine the total mass and composition of a MOF mixture for comprising a bespoke (i.e., tailored to a declared geographic region and time frame) adsorbent bed for robust (i.e., delivering adequate water every day) passive AWH. We combine weather data in the declared region, equilibrium water adsorption data in the candidate MOFs, and thermodynamic water adsorption models (as a simplifying assumption, we neglect heat and water transfer limitations) to frame a linear program expressing our optimal design principle: adjust the mass of each candidate MOF comprising the adsorbent bed to minimize mass (important for portability and a proxy for cost) while satisfying daily water delivery constraints. Based on case studies in the Chihuahuan and Sonoran Deserts, we find (1) a mixed-MOF adsorbent bed can be, but is not always, lighter (e.g., ˜40% lighter) than the optimized single-MOF counterpart; and (2) the optimal composition and mass of the adsorbent bed differ by both geographic region and time frame. Finally, we visualize the linear program for a reduced problem with a two-dimensional design space to gain intuition, conduct a sensitivity analysis, and compare to an AWH field study. Our work is a starting point for optimizing the composition of bespoke adsorbent beds for robust, passive AWH.





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