A hydrogel crosslinked by hierarchical inorganic hybrid crosslinks via simultaneous in situ sol–gel technique and radical polymerization exhibits excellent mechanical performance.
Herein, poly(acrylamide-co-acrylic acid)/GQDs (poly(AM-co-AA)/GQDs) nanocomposite hydrogels were prepared by in-situ free radical polymerization using graphene quantum dots (GQDs) as multifunctional crosslinker. The appropriate size and plenty of surface functional groups...
Herein, a robust and fluorescent nanocomposite hydrogel polyvinyl alcohol/poly(N‐methylol acrylamide)/graphene quantum dots (PVA/PNMA/GQDs) with interpenetrating polymer network was made‐up by in‐situ radical polymerization and freeze–thaw method simultaneously. Because of GQDs trapped within the gel matrix as a result of the potent hydrogen bonds generated among PVA, PNMA, and GQDs, the nanocomposite hydrogel with an interpenetrating polymer network displayed strong fluorescence and enhanced mechanical properties. The obtained nanocomposite hydrogel could emit stable bright blue fluorescence at 365 nm radiation. Moreover, the obtained nanocomposite hydrogels were selective to Fe3+ ions and demonstrated a rapid and consistent response to Fe3+ ions ranging from 20 to 120 μmol/L. Meanwhile, the PVA/PNMA/GQDs hydrogel showed excellent mechanical properties. The compressive strength and strain of the nanocomposite hydrogel were 20.39 MPa and 94.53%, respectively. The tensile fracture strength was 303.06 kPa and the corresponding fracture strain was 198%. Additionally, the gel also possessed extreme fatigue resistance and could tolerate repeated compression without cracking. Therefore, these robust and fluorescent nanocomposite hydrogels have potential applications in flexible sensors for certain metal ions detection and information storage.
Selecting the appropriate pavement preventive maintenance treatment, together with determining the optimal timing of its placement, is critical to lowering agency costs effectively and improving pavement performance. The objective is to develop a methodology for treatment selection and its optimal timing based on matter element analysis (MEA). MEA methodology aims to solve problems with contradictions, and incompatibilities and pavement preventive maintenance decision making fit into this domain. Using data sets consisting of Specific Pavement Study-3 pavement sections from the Long-Term Pavement Performance database, statistical performance deterioration models for the do-nothing and posttreatment scenarios for four typical preventive treatments (chip seal, slurry seal, crack seal, and thin overlay) are developed. The models capture variations in environment, traffic level, structural condition, and pavement age. The performance indicators are the international roughness index, rutting, and friction number. The posttreatment benefit for each individual indicator and the cost of the treatment are calculated at multiple trial timing scenarios. Integrating the calculated costs and benefits in the MEA as a matter element, the optimal timing for each treatment is determined on the basis of the correlation degree of the matter elements. Consequently, the corresponding benefits and costs at the optimal timing scenarios constitute another matter element. Following the same matter element–based transformation procedure, the appropriate maintenance treatment is selected. A case study is presented to illustrate the proposed procedure. The proposed methodology should be useful both to agencies that already have a preventive maintenance program and to those considering implementation of such a program.
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