Flexible Electronics: Ultra‐Robust Flexible Electronics by Laser‐Driven Polymer‐Nanomaterials Integration (Adv. Funct. Mater. 17/2021)

Rodriguez, Raúl D.; Shchadenko, Sergey; Murastov, Gennadiy; Lipovka, Anna; Fatkullin, Maxim; Petrov, Ilia; Tran, Tuan‐Hoang; Khalelov, Alimzhan; Saqib, Muhammad; Villa, Nelson; Bogoslovskiy, Vladimir; Wang, Yan; Hu, Chang‐Gang; Zinovyev, Alexey; Sheng, Wenbo; Chen, Jinju; Amin, Ihsan; Sheremet, Evgeniya

doi:10.1002/adfm.202170114

Cited by 3 publications

(3 citation statements)

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“…Many emerging technologies also rely on the efficient use of mechanical energy storage and release, such as alternative energy systems. − Materials with a high modulus of resilience offer exceptional protection against mechanical deformation. , Additionally, artificial muscles in robots often require a high modulus of resilience as they use large amounts of elastic energy to produce powerful locomotion. ,, Advanced flexible electronic panels require a high modulus of resilience to balance the demands of durability and flexibility. Materials with a high modulus of resilience can provide great protection against mechanical deformation while still maintaining the flexibility needed for these electronic devices. , As shown in Figure , the modulus of resilience of isotropic linear elastic solids is mathematically defined as σ y 2 /(2 E ), where σ y is the yield strength and E is Young’s modulus. According to this definition, achieving high σ y and low E at the same time leads to a high modulus of resilience.…”

Section: Introductionmentioning

confidence: 99%

“…Materials with a high modulus of resilience can provide great protection against mechanical deformation while still maintaining the flexibility needed for these electronic devices. 10,11 As shown in Figure 1, the modulus of resilience of isotropic linear elastic solids is mathematically defined as σ y 2 /(2E), where σ y is the yield strength and E is Young's modulus. According to this definition, achieving high σ y and low E at the same time leads to a high modulus of resilience.…”

Section: Introductionmentioning

confidence: 99%

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High-Throughput Screening and Prediction of High Modulus of Resilience Polymers Using Explainable Machine Learning

Yue

Tao

et al. 2023

J. Chem. Theory Comput.

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The ability to store and release elastic strain energy, as well as mechanical strength, are crucial factors in both natural and man-made mechanical systems. The modulus of resilience (R) indicates a material's capacity to absorb and release elastic strain energy, with the yield strength (σ y ) and Young's modulus (E) as R = σ y 2 /(2E) for linear elastic solids. To improve the R in linear elastic solids, a high σ y and low E combination in materials is sought after. However, achieving this combination is a significant challenge as both properties typically increase together. To address this challenge, we propose a computational method to quickly identify polymers with a high modulus of resilience using machine learning (ML) and validate the predictions through high-fidelity molecular dynamics (MD) simulations. Our approach commences by training single-task ML models, multitask ML models, and Evidential Deep Learning models to forecast the mechanical properties of polymers based on experimentally reported values. Utilizing explainable ML models, we were able to determine the critical substructures that significantly impact the mechanical properties of polymers, such as E and σ y . This information can be utilized to create and develop new polymers with improved mechanical characteristics. Our single-task and multitask ML models can predict the properties of 12 854 real polymers and 8 million hypothetical polyimides and uncover 10 new real polymers and 10 hypothetical polyimides with exceptional modulus of resilience. The improved modulus of resilience of these novel polymers was validated through MD simulations. Our method efficiently speeds up the discovery of high-performing polymers using ML predictions and MD validation and can be applied to other polymer material discovery challenges, such as polymer membranes, dielectric polymers, and more.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

High-Throughput Screening and Prediction of High Modulus of Resilience Polymers Using Explainable Machine Learning

Yue

Tao

et al. 2023

J. Chem. Theory Comput.

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“…[3] To design flexible/stretchable conductors with excellent performances, many approaches have been proposed. [4][5][6][7][8][9] Generally speaking, three aspects are involved comprising selection of materials, formation of conductive network, and integration of functional elements and flexible/stretchable polymer matrix.…”

Section: Introductionmentioning

confidence: 99%

Advanced Printing Transfer of Assembled Silver Nanowire Network into Elastomer for Constructing Stretchable Conductors

Yang

Narayanan

et al. 2023

Adv Eng Mater

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Excellent electrical performance of assemblies of 1D silver nanowires (AgNWs) has been demonstrated in the past years. Up to now, however, there are limited approaches to realize simultaneously deterministic assembly with dense arrangement of AgNWs and desired functional layouts. Herein, an assembly strategy from compressed air‐modulated alignment of AgNWs to heterogeneous integration of stretchable sensing devices through printing transfer is proposed. In this process, a convective flow induced by compressed air brings the AgNWs to the air–droplet interface, where the AgNWs are assembled with excellent alignment and packing due to the surface flow, van der Waals, and capillary interactions. Compared with those random AgNWs networks, the oriented, densely packed AgNWs exhibit a lower and uniform electrical sheet resistance. To incorporate the AgNWs to an elastomer substrate, direct ink writing is employed to transfer the assembled AgNW network to the printed silicone elastomer with desirable patterns. Excellent electrical property is demonstrated including a wide electrical response range from 10% to 120% strain, and high electrical repeatability. An antibacterial property is confirmed, notifying additional benefit as wearable sensors. The printing transfer of preassembled AgNW networks to the printed elastomer patterns provides a facile strategy to construct stretchable electronic devices.

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Electrochemical determination of silver nanoparticles on flexible laser reduced graphene oxide sensor

Aljasar

Марченко

2023

2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

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Flexible Electronics: Ultra‐Robust Flexible Electronics by Laser‐Driven Polymer‐Nanomaterials Integration (Adv. Funct. Mater. 17/2021)

Cited by 3 publications

References 0 publications

High-Throughput Screening and Prediction of High Modulus of Resilience Polymers Using Explainable Machine Learning

High-Throughput Screening and Prediction of High Modulus of Resilience Polymers Using Explainable Machine Learning

Advanced Printing Transfer of Assembled Silver Nanowire Network into Elastomer for Constructing Stretchable Conductors

Electrochemical determination of silver nanoparticles on flexible laser reduced graphene oxide sensor

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