Showing 201 - 220 results of 10,510 for search '((pinna OR ((sping OR sspine) OR shing)) OR (((can OR shengo) OR sshina) OR pin))', query time: 0.18s Refine Results
  1. 201

    Layer-by-layer aided β-cyclodextrin nanofilm for precise organic solvent nanofiltration by Li, Xin, Li, Can, Goh, Kunli, Chong, Tzyy Haur, Wang, Rong

    Published 2022
    “…Besides, the newly developed OSN membrane can discriminate between dye molecules with similar molecular weights and same charge but different molecular configurations. …”
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    Journal Article
  2. 202

    Rejection of pharmaceuticals by forward osmosis membranes by Jin, Xue, Shan, Junhong, Wang, Can, Wei, Jing, Tang, Chuyang Y.

    Published 2013
    “…The pharmaceuticals rejection by CTA-HW membrane at pH 8 followed the order: diclofenac (99%) > carbamazepine (95%) > ibuprofen (93%) ≈ naproxen (93%). These results can be important for FO membrane synthesis, modification and their application in water purification.…”
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    Journal Article
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    Screensavers : for healthier digital kids by Tan, Marlene Yiting, Pek, Charis Kai Le, Ng, Pin Li

    Published 2014
    “…Campaign collaterals, photos of the campaign’s outreach activities, as well as tables, and lists detailing the campaign’s execution, can be found in the appendices.…”
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    Final Year Project (FYP)
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    Engineering nano-nodular pattern on the substrate surface by tuning phase inversion to construct ultrapermselective ion-separation nanofiltration membrane by Zhao, Yali, Li, Can, Liao, Yuan, Yin, Yurong, Wang, Rong, Chen, Dongliang

    Published 2024
    “…This work pioneers a new approach to tuning the PA layer structure, which can be readily achieved, and offers a fresh perspective on constructing high-performance NF membranes.…”
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    Journal Article
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    Dynamic traffic safety risk assessment in road tunnel entrance zone based on drivers' psychophysiological perception states: methodology and case-study insights by Niu, Jia'an, Liang, Bo, Wong, Yiik Diew, He, Shiyong, Qin, Can, Wen, Sen

    Published 2024
    “…The results show that the traffic safety risk identification accuracy of the machine learning model proposed in this study reaches 95.13%, and the model can dynamically assess the real-time driving risk level. …”
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    Journal Article
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