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Understanding Humidity‐Enhanced Adhesion of Geckos: Deep Neural Network‐Assisted Multi‐Scale Molecular Modeling

Materzok, Tobias ; Eslami, Hossein ; Gorb, Stanislav N. ; Müller‐Plathe, Florian (2023)
Understanding Humidity‐Enhanced Adhesion of Geckos: Deep Neural Network‐Assisted Multi‐Scale Molecular Modeling.
In: Small : nano micro, 2023, 19 (22)
doi: 10.26083/tuprints-00024319
Artikel, Zweitveröffentlichung, Verlagsversion

Kurzbeschreibung (Abstract)

A higher relative humidity leads to an increased sticking power of gecko feet to surfaces. The molecular mechanism responsible for this increase, however, is not clear. Capillary forces, water mediating keratin‐surface contacts and water‐induced softening of the keratin are proposed as candidates. In previous work, strong evidence for water mediation is found but indirect effects via increased flexibility are not completely ruled out. This article studies the latter hypothesis by a bottom‐up coarse‐grained mesoscale model of an entire gecko spatula designed without explicit water particles, so that capillary action and water‐mediation are excluded. The elasticity of this model is adjusted with a deep neural network to atomistic elastic constants, including water at different concentrations. Our results show clearly that on nanoscopic flat surfaces, the softening of keratin by water uptake cannot nearly account for the experimentally observed increase in gecko sticking power. Here, the dominant mechanism is the mediation of keratin‐surface contacts by intervening water molecules. This mechanism remains important on nanostructured surfaces. Here, however, a water‐induced increase of the keratin flexibility may enable the spatula to follow surface features smaller than itself and thereby increase the number of contacts with the surface. This leads to an appreciable but not dominant contribution to the humidity‐increased adhesion. Recently, by atomistic grand‐canonical molecular dynamics simulation, the room‐temperature isotherm is obtained for the sorption of water into gecko keratin, to the authors’ knowledge, the first such relation for any beta‐keratin. In this work, it relates the equilibrium water content of the keratin to the ambient relative humidity.

Typ des Eintrags: Artikel
Erschienen: 2023
Autor(en): Materzok, Tobias ; Eslami, Hossein ; Gorb, Stanislav N. ; Müller‐Plathe, Florian
Art des Eintrags: Zweitveröffentlichung
Titel: Understanding Humidity‐Enhanced Adhesion of Geckos: Deep Neural Network‐Assisted Multi‐Scale Molecular Modeling
Sprache: Englisch
Publikationsjahr: 2023
Ort: Darmstadt
Publikationsdatum der Erstveröffentlichung: 2023
Verlag: Wiley-VCH
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Small : nano micro
Jahrgang/Volume einer Zeitschrift: 19
(Heft-)Nummer: 22
Kollation: 9 Seiten
DOI: 10.26083/tuprints-00024319
URL / URN: https://tuprints.ulb.tu-darmstadt.de/24319
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Herkunft: Zweitveröffentlichung DeepGreen
Kurzbeschreibung (Abstract):

A higher relative humidity leads to an increased sticking power of gecko feet to surfaces. The molecular mechanism responsible for this increase, however, is not clear. Capillary forces, water mediating keratin‐surface contacts and water‐induced softening of the keratin are proposed as candidates. In previous work, strong evidence for water mediation is found but indirect effects via increased flexibility are not completely ruled out. This article studies the latter hypothesis by a bottom‐up coarse‐grained mesoscale model of an entire gecko spatula designed without explicit water particles, so that capillary action and water‐mediation are excluded. The elasticity of this model is adjusted with a deep neural network to atomistic elastic constants, including water at different concentrations. Our results show clearly that on nanoscopic flat surfaces, the softening of keratin by water uptake cannot nearly account for the experimentally observed increase in gecko sticking power. Here, the dominant mechanism is the mediation of keratin‐surface contacts by intervening water molecules. This mechanism remains important on nanostructured surfaces. Here, however, a water‐induced increase of the keratin flexibility may enable the spatula to follow surface features smaller than itself and thereby increase the number of contacts with the surface. This leads to an appreciable but not dominant contribution to the humidity‐increased adhesion. Recently, by atomistic grand‐canonical molecular dynamics simulation, the room‐temperature isotherm is obtained for the sorption of water into gecko keratin, to the authors’ knowledge, the first such relation for any beta‐keratin. In this work, it relates the equilibrium water content of the keratin to the ambient relative humidity.

Freie Schlagworte: deep neural networks, gecko adhesion, humidity, molecular dynamics, multiscale molecular model, pull‐off, spatula
ID-Nummer: 2206085
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-243198
Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 540 Chemie
500 Naturwissenschaften und Mathematik > 590 Tiere (Zoologie)
Fachbereich(e)/-gebiet(e): 07 Fachbereich Chemie
07 Fachbereich Chemie > Theoretische Chemie (am 07.02.2024 umbenannt in Quantenchemie)
Hinterlegungsdatum: 18 Jul 2023 12:47
Letzte Änderung: 19 Jul 2023 05:00
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