Electronics for Polymer Engineers

Khalifeh, Sulaiman

doi:10.1016/b978-1-927885-67-3.50007-9

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…The p-SMILES represent the polymer structures as sequences of characters, including the information of atoms, rings, and branches. The p-SMILES used in this work are limited to homopolymers, which consists of only one type of monomer and two polymerization points, but the same strategy may be extended to heteropolymer built on two or more types of monomers in future when relevant data are accumulated. We collect the MD-calculated TC data for 469 amorphous polymers from ref.…”

Section: Methodsmentioning

confidence: 99%

Exploring High Thermal Conductivity Amorphous Polymers Using Reinforcement Learning

Zhang

Luo

2022

ACS Appl. Mater. Interfaces

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Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.400 W/m•K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all generated polymers with thermal conductivity ≥0.400 W/m•K, we have evaluated their synthesizability by calculating the synthetic accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has an MD-calculated thermal conductivity of 0.693 W/m•K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also be generalized to other material development tasks for different applications.

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Section: Methodsmentioning

confidence: 99%

Exploring High Thermal Conductivity Amorphous Polymers Using Reinforcement Learning

Zhang

Luo

2022

ACS Appl. Mater. Interfaces

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“…9–12 Polymeric/oligomeric materials possess several advantages as compared to the small molecules, such as processability, ease of functionalization and their variety, tunable emission, variable topologies (linear, star-shaped, hyperbranched), and higher mechanical strength. 13,14 Indeed, the luminescent polymers expand the application scope to explosive detection, 15 polymer light-emitting diodes (PLEDs), 16,17 stimuli-responsive materials, 18–20 etc. The fluorescent property of the polymers can be tuned with composition, structure, and morphology to meet the great potential of practical applications such as light-harvesting photoactive polymer brushes, imaging and theranostics, fluorescent chemosensors and biosensing, and drug delivery.…”

Section: Introductionmentioning

confidence: 99%

Multi-stimuli distinct responsive D–A based fluorogen oligomeric tool and efficient detection of TNT vapor

et al. 2022

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“…Organic donor–acceptor composites are considered promising materials for creating various molecular devices, such as field-effect transistors, switches, sensors, and photovoltaic elements. − The latter normally consist of a polymer matrix with embedded allotropes of carbon (fullerenes, graphene, and carbon nanotubes) and other small molecules. Light irradiation initiates a formation in such bulk heterojunctions (BHJ) of excitons, which may dissociate into positively charged polarons on polymer chains and negatively charged fullerene or nonfullerene radical anions situated between the polymer chains.…”

Section: Introductionmentioning

confidence: 99%

Light-Induced EPR Study of Polymorphic Acene-Stipulated Transition in P3DDT:PC₆₁BM Composite

2022

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We report the light-induced electron paramagnetic resonance (LEPR) and optics study of spin charge carriers initiated by light photons with the energy of 1.34–4.52 eV in the initial and slightly acene-treated composites of a regioregular poly(3-dodecylthiophene) (P3DDT) with a [6,6]-phenyl-C61-butyric acid methyl ester (PC61BM). Light irradiation leads to the formation in both polymorphs of spin charge carriers, polarons, and methanofullerene radical anions. Magnetic resonance, relaxation, and dynamic parameters of such carriers depend on the energy of exciting light photons as well as on a balance of a chaotically structured polymer α-polymorph with spin traps and a more structured its β-polymorph. The charge carriers, photoinitiated in the α-polymorph, are characterized by an extreme dependence of their main parameters from the energy of exciting photons. The increase in the concentration and stability of both mobile charge carriers were reached upon modification of the initial composite by the simplest conjugated acenes. The greatest effect was registered for the composite, weakly doped by naphthalene molecules. The concentration and other properties of charge carriers photoinitiated in a modified composite were found to correlate with the band structure of the acene introduced. Such an effect was interpreted in favor of a triggerlike polymorphic α–β transition stipulated in the system by planar acene molecules. It leads to an acceleration of spin–lattice relaxation of mobile charge carriers, a decrease in the anisotropic diffusion of polarons between polymer chains, and a slow down of the recombination of charge carriers. This opens horizons to use optimally acene-doped polymers and their composites for the creation of electronic and spintronic devices with spin-photon-assisted parameters.

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Electronics for Polymer Engineers

Cited by 4 publications

References 8 publications

Exploring High Thermal Conductivity Amorphous Polymers Using Reinforcement Learning

Exploring High Thermal Conductivity Amorphous Polymers Using Reinforcement Learning

Multi-stimuli distinct responsive D–A based fluorogen oligomeric tool and efficient detection of TNT vapor

Light-Induced EPR Study of Polymorphic Acene-Stipulated Transition in P3DDT:PC₆₁BM Composite

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Electronics for Polymer Engineers

Cited by 4 publications

References 8 publications

Exploring High Thermal Conductivity Amorphous Polymers Using Reinforcement Learning

Exploring High Thermal Conductivity Amorphous Polymers Using Reinforcement Learning

Multi-stimuli distinct responsive D–A based fluorogen oligomeric tool and efficient detection of TNT vapor

Light-Induced EPR Study of Polymorphic Acene-Stipulated Transition in P3DDT:PC61BM Composite

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Light-Induced EPR Study of Polymorphic Acene-Stipulated Transition in P3DDT:PC₆₁BM Composite