Study of change in thermal diffusivity of amorphous polymers during glass transition

The Macromolecular Design of Poly(styrene-isoprene-styrene) (SIS) Copolymers Defines their Performance in Flexible Electrothermal Composite Heaters

Electric cars are desirable for their environmental and economic benefits yet face limitations in range in cold weather due to the increased energy demands for cabin heating. To provide efficient heating for vehicles, flexible composite electrothermal heaters offer a viable solution owing to their lightweight design, efficiency, and adaptability for use within and beyond vehicle interiors. The current study aims to improve electrothermal heater stability and performance by understanding the impact of the polymer structure on composite properties. We explore how the presence and molecular structure of olefinic bonds within the polyisoprene block of styrenic triblock copolymers affect thermal stability and performance. Composite electrothermal heaters were fabricated by dispersing carbon black (CB) as the heating material in three triblock copolymer matrices, poly(styrene-1,4-isoprene-styrene) (1,4-SIS), poly(styrene-3,4-isoprene-styrene) (3,4-SIS), and its hydrogenated version poly(styrene-ethylene-propylene-styrene) (SEPS). The chemical structure and thermal properties of each copolymer were linked to electrothermal performance measurements of composite heaters to establish structure-function relationships. Notably, 3,4-SIS with 28 wt % CB demonstrated the highest thermal and electrical conductivity, resulting in uniform heat distribution. The outcomes unambiguously demonstrate that the olefinic structure of SIS copolymers enhances the electric and thermal conductivity, leading to enhanced electrothermal performance of prototype heaters compared to that of the hydrogenated copolymer.

Lock-in photothermal method for in-plane thermal diffusivity measurements using arrayed temperature sensors on suspended SiNx membranes

A device consisting of a line- or spiral-shaped temperature sensor array on a two-dimensional (2D) silicon nitride (SiNx) membrane of thickness 50 or 150 nm is developed for use in the lock-in photothermal method to determine the in-plane thermal diffusivity of SiNx membranes in air and in vacuum. The results of 2D heat diffusion are analyzed by the quadrupole method, and the system is approximated to the one-dimensional (1D) fin standing in a surrounding media (the fin approximation). The results show that 2D thermal diffusion on the membrane is affected not only by heat exchange with the surrounding environment but also by parallel thermal diffusion caused by heat conduction in the air along the membrane surface. The measurement using photothermal heating and contact detection of the temperature response enables the phenomenon to be detected consistently at a wide frequency range of temperature waves (50-1000 Hz). The measured thermal diffusivity values of the SiNx membrane are much smaller than those of bulk material, which can be reasonably considered an effect of the confined state of the phonon in the nanoscale geometry of the membrane.

Probe-based microscale measurement setup for the thermal diffusivity of soft materials

Based on the principle of the periodic heating method by using cantilever thermocouple nanoprobes, we developed a method and an apparatus to measure the thermal diffusivity of soft materials on a microscale. The contact position of the probe tip with the sample surface was defined by using the phenomenon that the DC component of the thermal electromotive force (EMF) of the probe changes significantly upon contact (i.e., the vertical temperature gradient near the sample surface changes significantly). This contact position was set as the surface reference position where the variation of the thermal contact conductance between the sample surface and the sensor probe is minimized. The phase shift from the micro-heater was measured by the AC component of the probe's thermal EMF and used to accurately determine the thermal diffusivity of micro-sized soft materials. The thermal diffusivity of the microstructured photoresist was determined with a deviation of ±3%.

Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges

Organic molecules and polymers have a broad range of applications in biomedical, chemical, and materials science fields. Traditional design approaches for organic molecules and polymers are mainly experimentally-driven, guided by experience, intuition, and conceptual insights. Though they have been successfully applied to discover many important materials, these methods are facing significant challenges due to the tremendous demand of new materials and vast design space of organic molecules and polymers. Accelerated and inverse materials design is an ideal solution to these challenges. With advancements in high-throughput computation, artificial intelligence (especially machining learning, ML), and the growth of materials databases, ML-assisted materials design is emerging as a promising tool to flourish breakthroughs in many areas of materials science and engineering. To date, using ML-assisted approaches, the quantitative structure property/activity relation for material property prediction can be established more accurately and efficiently. In addition, materials design can be revolutionized and accelerated much faster than ever, through ML-enabled molecular generation and inverse molecular design. In this perspective, we review the recent progresses in ML-guided design of organic molecules and polymers, highlight several successful examples, and examine future opportunities in biomedical, chemical, and materials science fields. We further discuss the relevant challenges to solve in order to fully realize the potential of ML-assisted materials design for organic molecules and polymers. In particular, this study summarizes publicly available materials databases, feature representations for organic molecules, open-source tools for feature generation, methods for molecular generation, and ML models for prediction of material properties, which serve as a tutorial for researchers who have little experience with ML before and want to apply ML for various applications. Last but not least, it draws insights into the current limitations of ML-guided design of organic molecules and polymers. We anticipate that ML-assisted materials design for organic molecules and polymers will be the driving force in the near future, to meet the tremendous demand of new materials with tailored properties in different fields.

Predicting Materials Properties with Little Data Using Shotgun Transfer Learning

There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have emerged as part of a digital transformation of materials science. However, recent technological advances in ML are not fully exploited because of the insufficient volume and diversity of materials data. An ML framework called "transfer learning" has considerable potential to overcome the problem of limited amounts of materials data. Transfer learning relies on the concept that various property types, such as physical, chemical, electronic, thermodynamic, and mechanical properties, are physically interrelated. For a given target property to be predicted from a limited supply of training data, models of related proxy properties are pretrained using sufficient data; these models capture common features relevant to the target task. Repurposing of such machine-acquired features on the target task yields outstanding prediction performance even with exceedingly small data sets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. In this study, to facilitate widespread use of transfer learning, we develop a pretrained model library called XenonPy.MDL. In this first release, the library comprises more than 140 000 pretrained models for various properties of small molecules, polymers, and inorganic crystalline materials. Along with these pretrained models, we describe some outstanding successes of transfer learning in different scenarios such as building models with only dozens of materials data, increasing the ability of extrapolative prediction through a strategic model transfer, and so on. Remarkably, transfer learning has autonomously identified rather nontrivial transferability across different properties transcending the different disciplines of materials science; for example, our analysis has revealed underlying bridges between small molecules and polymers and between organic and inorganic chemistry.

Chain length effect on thermal transport in amorphous polymers and a structure–thermal conductivity relation

The physics of thermal transport in polymers is important in many applications, such as in heat exchangers and electronics packaging.

Thermal conductivity analysis and applications of nanocellulose materials

In this review, we summarize the recent progress in thermal conductivity analysis of nanocellulose materials called cellulose nanopapers, and compare them with polymeric materials, including neat polymers, composites, and traditional paper. It is important to individually measure the in-plane and through-plane heat-conducting properties of two-dimensional planar materials, so steady-state and non-equilibrium methods, in particular the laser spot periodic heating radiation thermometry method, are reviewed. The structural dependency of cellulose nanopaper on thermal conduction is described in terms of the crystallite size effect, fibre orientation, and interfacial thermal resistance between fibres and small pores. The novel applications of cellulose as thermally conductive transparent materials and thermal-guiding materials are also discussed.

Thermal Conductivity of High-Strength Polyethylene Fiber and Applications for Cryogenic Use

The local temperature rise of the tape is one of instabilities of the conduction-cooled high temperature superconducting (HTS) coils. To prevent the HTS tape from locally raising a temperature, high thermal conductive fiber reinforced plastic was applied to coil bobbin or spacer for heat drain from HTS tape. The thermal conductivity of ramie fibers increases by increasing orientation of molecular chains with drawing in water, and decreases by chain scission with γ-rays irradiation or by bridge points in molecular chains with vapor-phase-formaldehyde treatments. Thermal conductivity of high strength ultra-high-molecular-weight (UHMW) polyethylene (PE) fiber increases lineally in proportion to tensile modulus and decreases by molecular chain scissions with γ-rays irradiation. This result suggested the contribution of the long extended molecular chains due to high molecular weight on the high thermal conductivity of high strength UHMW PE fiber. Thermal conductivity of high strength UHMW PE fiber reinforced plastics in parallel to fiber direction is proportional to the cross sectional ratio of reinforcement oriented in the conduction direction. Heat drain effect of high strength UHMW PE fiber reinforced plastic from HTS tape is higher than that of glass fiber reinforced plastic (GFRP) and lower than that of aluminum nitride (AlN). In the case of HTS coil, the thermal stability wound on coil bobbin made of high strength UHMW PE fiber reinforced plastic is good as that of AlN, and better than that of GFRP.

Study of Phase Transitions by Transient Methods

The present paper deals with the application of the transient techniques for thermophysical analysis of the structural changes in materials. The technique has been applied for study of equilibrium transitions as well as for kinetic transitions. A special methodology has been developed to study kinetic transitions like crystallization, melting, etc. in a “pseudo-equilibrium states” by the help of porous structures. The paper includes three different issues: the transient methods for measuring thermodynamic and transport parameters, data analysis and application of the pulse transient method for measurements of materials in thermodynamic equilibrium, pseudoequilibrium and in non-equilibrium (quasi-equilibrium) states. Equilibrium transitions in CsPbCl3 and CsPbBr3 single crystals, kinetic transitions of freezing and thawing water in porous stones and non-equilibriums states in E-glass and Al2O3 ceramics during sintering have been studied.

Thermodynamic properties of several soil- and sediment-derived natural organic materials

Improved understanding of the structure of soil- and sediment-derived organic matter is critical to elucidating the mechanisms that control the reactivity and transport of contaminants in the environment. This work focuses on an experimental investigation of thermodynamic properties that are a function of the macromolecular structure of natural organic matter (NOM). A suite of thermal analysis instruments were employed to quantify glass transition temperatures (Tg), constant-pressure specific heat capacities (Cp), and thermal expansion coefficients (alpha) of several International Humic Substances Society (IHSS) soil-, sediment-, and aquatic-derived NOMs. Thermal mechanical analysis (TMA) of selected NOMs identified Tgs between 36 and 72 degrees C, and alphas ranging from 11 mum/m degrees C below the Tg to 242 mum/m degrees C above the Tg. Standard differential scanning calorimetry (DSC) and temperature-modulated differential scanning calorimetry (TMDSC) measurements provided additional evidence of glass transition behavior, including identification of multiple transition behavior in two aquatic samples. TMDSC also provided quantitative measures of Cp at 0 and 25 degrees C, ranging from 1.27 to 1.44 J/g degrees C. Results from TMA, DSC, and TMDSC analyses are consistent with glass transition theories for organic macromolecules, and the glass transition behavior of other NOM materials reported in previous studies. Discussion of the importance of quantifying these thermodynamic properties is presented in terms of improved physical and chemical characterization of NOM structures, and in terms of providing constraints to molecular simulation models of NOM structures.