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Enregistrement W4416602565 · doi:10.1149/ma2025-02341692mtgabs

<i>(Invited)</i> Two-Dimensional Molecular Auxetic Materials and Their Characterization <i>via</i> Nanophotonics Imaging Tools

2025· article· W4416602565 sur OpenAlexaff
Giovanni Fanchini

Notice bibliographique

RevueECS Meeting Abstracts · 2025
Typearticle
Langue
DomaineEngineering
ThématiqueCellular and Composite Structures
Établissements canadiensWestern University
Organismes subventionnairesnon disponible
Mots-clésAuxeticsMesoscopic physicsThermal expansionCharacterization (materials science)MetamaterialThermal conductivityMaterial propertiesNanolithographyDegree (music)

Résumé

récupéré en direct d'OpenAlex

Metamaterials exhibiting auxeticity, defined as positive strain normal to an applied strain, have been receiving tremendous interest in recent years as they have found unique applications as shock absorbers, for thermal expansion compensation, and in smart interfaces.[1] This highly unusual behavior can quantified in terms of Poisson's ratio (ν) defined as the amount of the response strain over applied strain, where ν &gt; 0 in most typical materials, while ν &lt; 0 in auxetics. Molecular auxetic materials are crystalline solids which unit cell is auxetic.[2] A number of two-dimensional (2D) materials, including but not limited to single-layer and few-layer graphene,[3] are weakly auxetic due to their expansion along both in-plane longitudinal and transversal directions, caused by their thinning along the z-axis. Nonetheless, the achievement of a significant degree of auxeticity beyond the mesoscopic level has been theoretically predicted to require the modification of 2D materials through the engineering of periodic arrays of defects,[3] which is hardly feasible with commonly available nanofabrication techniques, and has never been demonstrated in practice. An alternative approach towards achieving a significant degree of auxeticity rests on the design of novel 2D crystals with specific electronic structure and unit cells, with 2D carbides and semicarbides (a sub-class of 2D M-Xenes) at the forefront of such efforts. The term giant auxeticity refers to molecular auxetic metals in which the density-of-electronic states at the Fermi level increases upon stretching thus leading to an increase in electrical and thermal conductivity in both longitudinal and transversal directions. Theoretical studies [4] seem to indicate that the root cause for giant auxeticity in 2D (semi)carbides resides on the increasing delocalization of the coupling between p-electrons (from C atoms) and d-electrons (from metal atoms) which causes a strong planarization and elongation of the metal-carbon bonds. For example, tungsten semicarbide (2D-W 2 C) was theoretically predicted to combine very high negative Poisson's ratio (ν &lt; -0.4) with giant auxeticity.[4] In the initial part of our presentation, we will report on a specially designed dual-zone remote plasma system conceived to grow giant-auxetic 2D semicarbides out of thermodynamic equilibrium with a well-tuned ratio of precursors.[1] Although 2D-W 2 C was theoretically predicted to exhibit giant auxeticity, it had yet to be synthesized before the use of our and other out-of-equilibrium techniques, as the full carbide is energetically favored under thermodynamic equilibrium synthesis processes, such as dual-zone furnace-based chemical vapor deposition (CVD), and this is a common issue of multiple semicarbides. We will report on the specific conditions under which dual-zone, remote-plasma enhanced CVD allowed for the synthesis of flakes of few-layer W 2 C which are 2D in nature due to retained periodicity at the mesoscopic level in a Stranski–Krastanov growth process.[5] In the next part of our presentation, we will show how nanooptical dilatometry techniques based on pump-probe scanning near-field optical microscopy (SNOM) [6] are essential nanophotonics imaging tools towards the identification of 2D molecular auxetic materials, and the quantification of their negative Poisson ratio. To date, there are very few experimental techniques, if any, that are suitable for the purpose of acquiring quantitative maps of the expansivity of flakes of 2D materials with nanoscale lateral resolution in spite of huge demand, for example in the characterization of auxetics. Our group for has pioneered near-field thermoreflectance techniques as nanophotonic tools for precisely mapping the thermal and mechanical properties at the nanoscale. In these pump-probe experiments, samples are periodically heated by a pump laser beam from an inverted optical microscope, while periodic changes in the sample's local reflectivity are measured, in phase and amplitude, through an upright aperture-type SNOM operating in reflection mode. Here we will show that ω-2ω near-field thermoreflectance experiments, in which the probe is locked in for detection at double frequency (2ω) as the pump, are spectacularly sensitive to the local expansivity of surfaces, and can thus be successfully used as nanophotonics dilatometers to characterize a wide class of auxetic materials. Collectively, our presentation focuses on potental applications of 2D giant auxetics in photonics and energy, which include robust signalk detection and the photomodulation of charge collection, for example in electrodes for stretchable solar cells and phototransducers. [1] Ren et al Smart Mater Struct 27 (2018) 023001 [2] Grima et al, Advanced Mater 27 (2014) 1455 [3] Grima et al, Ann Phys 530 (2018) 1700330 [4] Wu et al, Phys Chem Chem Phys 20 (2018) 18893 [5] Stocek, Ullah and Fanchini, Mater Horizons 11 (2024) 3066 [6] Wong, Ezugwu and Fanchini, Adv Mater Interfaces 11 (2024) 2300806 Figure 1

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,061
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,194
Écart entre enseignants0,190 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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