School of Materials Science and Engineering (MSE)

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2,021 to 2,030 of 8,021 Results

watermodels.dat Nov 15, 2023 - Data for: Harnessing Gradients for Self-Assembly of Peptide-Based Nanocapsules: A Pathway to Advanced Drug Delivery Systems Fixed Field Text Data - 301 B - MD5: 2ad4aa3810e3e673d48829130e6cf865
write_mdp_25-Lambda-states.pl Nov 15, 2023 - Data for: Harnessing Gradients for Self-Assembly of Peptide-Based Nanocapsules: A Pathway to Advanced Drug Delivery Systems Unknown - 747 B - MD5: 974cef7d04adb8c91fde26eb8841f874
Zeta Potenial.tab Nov 15, 2023 - Data for: Harnessing Gradients for Self-Assembly of Peptide-Based Nanocapsules: A Pathway to Advanced Drug Delivery Systems Tabular Data - 117 B - 4 Variables, 6 Observations - UNF:6:cPweXLOJSBbXkhCwOCk84g==
Li Haopeng(Nanyang Technological University) Nov 14, 2023
Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation Nov 14, 2023 - KONG Lingxuan Kong, Lingxuan; Wen, Hanqi; Luo, Yifei; Chen, Xiaodong; Sheng, Xing; Liu, Yuxin; Chen, Peng, 2023, "Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation", https://doi.org/10.21979/N9/4D5WR2, DR-NTU (Data), V1 The use of conductive microneedles presents a promising solution for achieving high-fidelity electrophysiological recordings with minimal impact on interfaced tissue. However, conventional metal-based microneedle suffers from high electrochemical impedance and mechanical mismatch...
Figure 1. Scheme.png Nov 14, 2023 - Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation PNG Image - 12.3 MB - MD5: 9809f9167ffdc9834582ce222d7f5e38
Figure 2. Characterization.png Nov 14, 2023 - Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation PNG Image - 10.2 MB - MD5: e1f2626eb642971fc874411057f4d670
Figure 3. In Planta Biopotential Monitoring.png Nov 14, 2023 - Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation PNG Image - 9.6 MB - MD5: bd1d9f357edfd8c880c1b0c68b13208c
Figure 4. Cation Fluctuation Monitoring.png Nov 14, 2023 - Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation PNG Image - 391.5 KB - MD5: 82541471f40264dde726aaa038661f8c
Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Oct 31, 2023 - Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning Chen Xi, 2023, "Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning", https://doi.org/10.21979/N9/QR3JP5, DR-NTU (Data), V1 Data generated during project: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

watermodels.dat

Nov 15, 2023 - Data for: Harnessing Gradients for Self-Assembly of Peptide-Based Nanocapsules: A Pathway to Advanced Drug Delivery Systems

Fixed Field Text Data - 301 B -

write_mdp_25-Lambda-states.pl

Nov 15, 2023 - Data for: Harnessing Gradients for Self-Assembly of Peptide-Based Nanocapsules: A Pathway to Advanced Drug Delivery Systems

Unknown - 747 B -

Zeta Potenial.tab

Nov 15, 2023 - Data for: Harnessing Gradients for Self-Assembly of Peptide-Based Nanocapsules: A Pathway to Advanced Drug Delivery Systems

Tabular Data - 117 B - 4 Variables, 6 Observations -

Li Haopeng(Nanyang Technological University)

Nov 14, 2023

Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation

Nov 14, 2023 - KONG Lingxuan

Kong, Lingxuan; Wen, Hanqi; Luo, Yifei; Chen, Xiaodong; Sheng, Xing; Liu, Yuxin; Chen, Peng, 2023, "Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation", https://doi.org/10.21979/N9/4D5WR2, DR-NTU (Data), V1

The use of conductive microneedles presents a promising solution for achieving high-fidelity electrophysiological recordings with minimal impact on interfaced tissue. However, conventional metal-based microneedle suffers from high electrochemical impedance and mechanical mismatch...

Figure 1. Scheme.png

Nov 14, 2023 - Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation

PNG Image - 12.3 MB -

Figure 2. Characterization.png

Nov 14, 2023 - Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation

PNG Image - 10.2 MB -

Figure 3. In Planta Biopotential Monitoring.png

Nov 14, 2023 - Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation

PNG Image - 9.6 MB -

Figure 4. Cation Fluctuation Monitoring.png

Nov 14, 2023 - Dual-Conductive and Stiffness-Morphing Microneedle Patch Enables Continuous in Planta Monitoring of Electrophysiological Signal and Ion Fluctuation

PNG Image - 391.5 KB -

Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Oct 31, 2023 - Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

Chen Xi, 2023, "Replication Data for: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning", https://doi.org/10.21979/N9/QR3JP5, DR-NTU (Data), V1

Data generated during project: Designing degradable polyethene with desirable physical properties via molecular dynamics simulation and machine learning

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