• Proceedings of the Polymer Society of Korea Conference
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• 2006.10a
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• pp.302-302
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• 2006

Nanoaggregates of triple hydrophilic block copolymers comprised of poly(ethylene oxide), poly(sodium 2-acrylamido)-2-methylpropanesulfonate), and poly(methacrylic acid) (PEO-PAMPS-PMAA) and the cationic surfactant, dodecyltrimethylammonium chloride (DTAC) have been fabricated. The formation of $^{\circ}^{\circ}$the nanoaggregates is based on electrostatic interaction of sulfonate and carboxylate groups of PAMPS and PMAA blocks with the cationic surfactant, which results in insolubilization of these blocks. The formation of micelle is observed by dynamic light scattering measurements. Binding of DTAC to the anionic blocks of PEO-PAMPS-PMAA is confirmed by electrophoresis measurements.

• Journal of the Korean Applied Science and Technology
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• v.12 no.1
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• pp.81-86
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• 1995

Some weight loss accelerating agents, dodecyltrimethylammonium chloride(DTAC), dodecyltrimethylammonium bromide(DTAB), dodecyldimethylammonium chloride(DDBAC), polyoxyethylene(2) dodecylbenzylammonium chloride(PDBAC), and 1-(2-hydroxyethyl)-1-benzyl-2-undecylimidazolinium chloride(AEUIC), were synthesized. As a result of weight loss treatment of the weight loss accelerating agents with NaOH to PET fiber, the increase of weight loss was the order of PDBAC > DDBAC > DTAC > DTAB > AEUIC. Among the weight loss accelerating agents, AEUIC hardly showed weight loss effect, and it was separated into two layer in the NaOH solution at the treatment concentration above 6g/L, but POBAC showed good weight loss effect of 21% that approach almost to a theoretical weight loss, 21.6%, at the concentration above 8g/L.

• Applied Chemistry for Engineering
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• v.10 no.6
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• pp.876-880
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• 1999

The surfactant self-diffusion coefficients of mixed micellar solutions of ionic and nonionic surfactants have been measured by the NMR pulsed field gradient spin echo(FT-PGSE) method. In addition, the line widths of $^1H$ NMR signal have been monitored. The system investgated are $C_{12}EO_5/SDS/D_2O$, $C_{12}EO_5/DTAC/D_2O$, and $C_{12}EO_8/SDS/D_2O$. In the sample series, the molar ratios of $D_2O$ to surfactant(ionic+nonionic) were kept constant while the surfactant mixing ratio was varied. For the $C_{12}EO_5$ system, the surfactant self-diffusion coefficient indicates minimum when the surfactant mixing ratio is about 20% ionic surfactant. The observed decrease in self-diffusion coefficients as nonionic surfactant was replaced by ionic surfactant is interpreted to mainly be due to an increased micelle-micelle repulsion. The increase in self-diffusion coefficients occurring at higher fraction of ionic surfactant is shown to be due to a decrease in micelle size. For the $C_{12}EO_8$ system, the effect of the surfactant mixing ratio is much weaker which can be understood by considering the molecular geometry and large headgroup area. The proton NMR line widths correlate well with the self-diffusion coefficients and broadening of the alkyl chain methylene signals is found when the self-diffusion coefficients is low.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.