Showing 1,081 - 1,100 results of 24,420 for search '(( significant decrease decrease ) OR ( significant ((sources decrease) OR (resources increase)) ))', query time: 0.63s Refine Results
  1. 1081

    Dynamic Covalent Chemistry Enabled Closed-Loop Recycling of Thermally Modified Polymer Membrane by Ching Yoong Loh (17863097)

    Published 2025
    “…Thermal and mechanical characterizations confirmed the great stability of the membranes, with the Diels–Alder reaction enabling depolymerization and reformation of the network without causing significant degradation. Additionally, the RFMs were recycled the third time, maintaining the fluxes (752 to 823 LMH) from the previous generation with a slight decrease in separation efficiency in dichloromethane-water emulsion separation (98.3 to 97%). …”
  2. 1082

    Dynamic Covalent Chemistry Enabled Closed-Loop Recycling of Thermally Modified Polymer Membrane by Ching Yoong Loh (17863097)

    Published 2025
    “…Thermal and mechanical characterizations confirmed the great stability of the membranes, with the Diels–Alder reaction enabling depolymerization and reformation of the network without causing significant degradation. Additionally, the RFMs were recycled the third time, maintaining the fluxes (752 to 823 LMH) from the previous generation with a slight decrease in separation efficiency in dichloromethane-water emulsion separation (98.3 to 97%). …”
  3. 1083

    Dynamic Covalent Chemistry Enabled Closed-Loop Recycling of Thermally Modified Polymer Membrane by Ching Yoong Loh (17863097)

    Published 2025
    “…Thermal and mechanical characterizations confirmed the great stability of the membranes, with the Diels–Alder reaction enabling depolymerization and reformation of the network without causing significant degradation. Additionally, the RFMs were recycled the third time, maintaining the fluxes (752 to 823 LMH) from the previous generation with a slight decrease in separation efficiency in dichloromethane-water emulsion separation (98.3 to 97%). …”
  4. 1084

    Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation by Renato Soares (20348202)

    Published 2024
    “…Moreover, its capacity to facilitate the exploration of specific compounds’ interactions with environmental pollutants is a significant advancement, aligning with the increasing reliance on data science and machine learning to address environmental challenges. …”
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