Keyword search (4,164 papers available)

"optimization" Keyword-tagged Publications:

Title Authors PubMed ID
1 Tuning Deep Learning for Predicting Aluminum Prices Under Different Sampling: Bayesian Optimization Versus Random Search Alicia Estefania Antonio Figueroa 41751647
CONCORDIA
2 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
3 A Deep Learning-Based Ensemble System for Brent and WTI Crude Oil Price Analysis and Prediction Zhang Y; Lahmiri S; 41294965
JMSB
4 Distinguishing Between Healthy and Unhealthy Newborns Based on Acoustic Features and Deep Learning Neural Networks Tuned by Bayesian Optimization and Random Search Algorithm Lahmiri S; Tadj C; Gargour C; 41294952
ENCS
5 Robust and Compact Electrostatic Comb Drive Arrays for High-Performance Monolithic Silicon Photonics Fasihanifard M; Packirisamy M; 41156349
ENCS
6 Cooperative Schemes for Joint Latency and Energy Consumption Minimization in UAV-MEC Networks Cheng M; He S; Pan Y; Lin M; Zhu WP; 40942666
ENCS
7 Lasso Model-Based Optimization of CNC/CNF/rGO Nanocomposites Ramezani G; Silva IO; Stiharu I; Ven TGMV; Nerguizian V; 40283268
ENCS
8 What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA Daniel A; Savary P; Foltête JC; Vuidel G; Faivre B; Garnier S; Khimoun A; 39417711
BIOLOGY
9 Topology optimization of adaptive sandwich plates with magnetorheological core layer for improved vibration attenuation Zare M; Sedaghati R; 39398530
ENCS
10 Discovery and preclinical development of a therapeutically active nanobody-based chimeric antigen receptor targeting human CD22 McComb S; Arbabi-Ghahroudi M; Hay KA; Keller BA; Faulkes S; Rutherford M; Nguyen T; Shepherd A; Wu C; Marcil A; Aubry A; Hussack G; Pinto DM; Ryan S; Raphael S; van Faassen H; Zafer A; Zhu Q; Maclean S; Chattopadhyay A; Gurnani K; Gilbert R; Gadoury C; Iqbal U; Fatehi D; Jezierski A; Huang J; Pon RA; Sigrist M; Holt RA; Nelson BH; Atkins H; Kekre N; Yung E; Webb J; Nielsen JS; Weeratna RD; 38596311
BIOLOGY
11 Design Optimization of a Hybrid-Driven Soft Surgical Robot with Biomimetic Constraints Roshanfar M; Dargahi J; Hooshiar A; 38275456
ENCS
12 Alternating direction method of multipliers for displacement estimation in ultrasound strain elastography Md Ashikuzzaman 38159299
ENCS
13 Lactate's behavioral switch in the brain: An in-silico model Soltanzadeh M; Blanchard S; Soucy JP; Benali H; 37865309
PERFORM
14 Data-Weighted Multivariate Generalized Gaussian Mixture Model: Application to Point Cloud Robust Registration Ge B; Najar F; Bouguila N; 37754943
ENCS
15 Design optimization and experimental evaluation of a large capacity magnetorheological damper with annular and radial fluid gaps Abdalaziz M; Sedaghati R; Vatandoost H; 37521729
ENCS
16 Designing a multi-objective closed-loop supply chain: a two-stage stochastic programming, method applied to the garment industry in Montréal, Canada Shafiee Roudbari E; Fatemi Ghomi SMT; Eicker U; 36747987
ENCS
17 Optimizing Biodegradable Starch-Based Composite Films Formulation for Wound-Dressing Applications Delavari MM; Ocampo I; Stiharu I; 36557445
ENCS
18 A flexible robust model for blood supply chain network design problem Khalilpourazari S; Hashemi Doulabi H; 35474752
ENCS
19 A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary Wan TTH; Matthews S; Luh H; Zeng Y; Wang Z; Yang L; 35372638
ENCS
20 A multiobjective model for the green capacitated location-routing problem considering drivers' satisfaction and time window with uncertain demand Alamatsaz K; Ahmadi A; Mirzapour Al-E-Hashem SMJ; 34415526
ENCS
21 Optimization of the Electrospun Niobium-Tungsten Oxide Nanofibers Diameter Using Response Surface Methodology Fatile BO; Pugh M; Medraj M; 34201513
ENCS
22 A robust optimization model for tactical capacity planning in an outpatient setting Aslani N; Kuzgunkaya O; Vidyarthi N; Terekhov D; 33215335
ENCS
23 Multidisciplinary Design Optimization of a Novel Sandwich Beam-Based Adaptive Tuned Vibration Absorber Featuring Magnetorheological Elastomer. Asadi Khanouki M, Sedaghati R, Hemmatian M 32422988
ENCS
24 Computer-Aided Diagnosis System of Alzheimer's Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model. Lazli L, Boukadoum M, Ait Mohamed O 31652635
ENCS
25 Mining Enzyme Diversity of Transcriptome Libraries through DNA Synthesis for Benzylisoquinoline Alkaloid Pathway Optimization in Yeast. Narcross L, Bourgeois L, Fossati E, Burton E, Martin VJ 27442619
BIOLOGY
26 Optimal positioning of optodes on the scalp for personalized functional near-infrared spectroscopy investigations. Machado A, Cai Z, Pellegrino G, Marcotte O, Vincent T, Lina JM, Kobayashi E, Grova C 30107210
PERFORM

 

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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