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Journal Abstract Search
314 related items for PubMed ID: 22799439
1. Metal-organic frameworks for removal of Xe and Kr from nuclear fuel reprocessing plants. Liu J, Thallapally PK, Strachan D. Langmuir; 2012 Aug 07; 28(31):11584-9. PubMed ID: 22799439 [Abstract] [Full Text] [Related]
6. Adsorptive separation of xenon/krypton mixtures using a zirconium-based metal-organic framework with high hydrothermal and radioactive stabilities. Lee SJ, Yoon TU, Kim AR, Kim SY, Cho KH, Hwang YK, Yeon JW, Bae YS. J Hazard Mater; 2016 Dec 15; 320():513-520. PubMed ID: 27597151 [Abstract] [Full Text] [Related]
7. Understanding the Adsorption Mechanism of Xe and Kr in a Metal-Organic Framework from X-ray Structural Analysis and First-Principles Calculations. Ghose SK, Li Y, Yakovenko A, Dooryhee E, Ehm L, Ecker LE, Dippel AC, Halder GJ, Strachan DM, Thallapally PK. J Phys Chem Lett; 2015 May 21; 6(10):1790-4. PubMed ID: 26263249 [Abstract] [Full Text] [Related]
8. Selective Xenon Recovery Using Aluminum-Based Metal-Organic Frameworks with Conserved Pore Topology. Hurley T, Henle A, Gładysiak A, Remcho VT, Stylianou KC. ACS Appl Mater Interfaces; 2024 Jul 10; 16(27):35333-35341. PubMed ID: 38946070 [Abstract] [Full Text] [Related]
11. Efficient Xe/Kr Separation Based on a Lanthanide-Organic Framework with One-Dimensional Local Positively Charged Rhomboid Channels. Wang X, Ma F, Xiong S, Bai Z, Zhang Y, Li G, Chen J, Yuan M, Wang Y, Dai X, Chai Z, Wang S. ACS Appl Mater Interfaces; 2022 May 18; 14(19):22233-22241. PubMed ID: 35507505 [Abstract] [Full Text] [Related]
16. Self-Adjusting Metal-Organic Framework for Efficient Capture of Trace Xenon and Krypton. Niu Z, Fan Z, Pham T, Verma G, Forrest KA, Space B, Thallapally PK, Al-Enizi AM, Ma S. Angew Chem Int Ed Engl; 2022 Mar 07; 61(11):e202117807. PubMed ID: 35020976 [Abstract] [Full Text] [Related]
19. Shell-like Xenon Nano-Traps within Angular Anion-Pillared Layered Porous Materials for Boosting Xe/Kr Separation. Zheng F, Guo L, Chen R, Chen L, Zhang Z, Yang Q, Yang Y, Su B, Ren Q, Bao Z. Angew Chem Int Ed Engl; 2022 May 09; 61(20):e202116686. PubMed ID: 34997694 [Abstract] [Full Text] [Related]
20. XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr. Liang H, Jiang K, Yan TA, Chen GH. ACS Omega; 2021 Apr 06; 6(13):9066-9076. PubMed ID: 33842776 [Abstract] [Full Text] [Related] Page: [Next] [New Search]