Literature DB >> 32975930

Controlled Natural Biomass Deoxygenation Allows the Design of Reusable Hot-Melt Adhesives Acting in a Multiple Oxygen Binding Mode.

Fedor A Kucherov1, Evgeniy G Gordeev1, Alexey S Kashin1, Valentine P Ananikov1.   

Abstract

The present article describes a conceptual view on the design of reusable bioderived high-value-added materials. The translation of a highly complex irregular structure of natural biopolymer into a well-defined hierarchically organized molecular chain led to the discovery of unique adhesive properties enhanced by a novel multiple binding effect. For practical applications, biomass-derived furanic polyesters were found as reusable thermoplastic adhesives. Examined poly(ethylene-2,5-furandicarboxylate) (PEF) and poly(hexamethylene-2,5-furandicarboxylate) (PHF) showed strong adhesion to aluminum in single-lap shear tests (1.47 ± 0.1 and 1.18 ± 0.1 kN/cm2, respectively). After the separation, the joints could be easily restored by reheating of the metal parts. Three consecutive cycles of regluing were successfully performed without a significant drop in the adhesive strength. Strong adhesion of the biomass-derived polymers to glass surfaces was also observed (0.93 ± 0.11 kN/cm2 for PEF and 0.84 ± 0.06 kN/cm2 for PHF). An in-depth study of the surfaces after the shear tests, carried out by means of scanning electron microscopy, revealed predominantly cohesive failure in the case of aluminum samples and adhesive failure in the case of glass samples. Computational modeling revealed a multiple oxygen binding mode for the interaction of furanic polyester molecules with the glass surface and metal atoms. Only sustainable materials were used as a carbon source for the production of target polymers, which showed excellent compatibility with the practically most demanding constructing materials (a universal reusable hot-melt adhesive for copper, brass, Be-copper, Mn-bronze, zinc, aluminum, titanium, and glass).

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Keywords:  5-hydroxymethylfurfural; adhesive; biomass; renewable materials; sustainable development

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Year:  2020        PMID: 32975930     DOI: 10.1021/acsami.0c14986

Source DB:  PubMed          Journal:  ACS Appl Mater Interfaces        ISSN: 1944-8244            Impact factor:   9.229


  1 in total

1.  Integration of thermal imaging and neural networks for mechanical strength analysis and fracture prediction in 3D-printed plastic parts.

Authors:  Daniil A Boiko; Victoria A Korabelnikova; Evgeniy G Gordeev; Valentine P Ananikov
Journal:  Sci Rep       Date:  2022-05-27       Impact factor: 4.996

  1 in total

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