For the thermal management of high watt density circuit layers, it is common to use a filled epoxy system to provide an electrically insulating but thermally conducting bond to a metal substrate. An epoxy-thiol system filled with boron nitride (BN), in the form of 2, 30 and 180 µm platelets, has been investigated with a view to achieving enhanced thermal conductivity. The effect of BN content on the cure reaction kinetics has been studied by differential scanning calorimetry and the thermal conductivity of the cured samples has been measured by the Transient Hot Bridge method. The heat of reaction and the glass transition temperature of the fully cured samples are both independent of the BN content, but the cure reaction kinetics is systematically affected by both BN content and particle size. These results can be correlated with the thermal conductivity of the cured systems, which is found to increase with both BN content and particle size. For a given BN content, the thermal conductivity found here is significantly higher than most others reported in the literature; this effect is attributed to a Lewis acid-base interaction between filler and matrix.

Epoxy resin composites filled with thermally conductive but electrically insulating particles play an important role in the thermal management of modern electronic devices. Although many types of particles are used for this purpose, including oxides, carbides and nitrides, one of the most widely used fillers is boron nitride (BN). In this review we concentrate specifically on epoxy-BN composites for high thermal conductivity applications. First, the cure kinetics of epoxy composites in general, and of epoxy-BN composites in particular, are discussed separately in terms of the effects of the filler particles on cure parameters and the cured composite. Then, several fundamental aspects of epoxy-BN composites are discussed in terms of their effect on thermal conductivity. These aspects include the following: the filler content; the type of epoxy system used for the matrix; the morphology of the filler particles (platelets, agglomerates) and their size and concentration; the use of surface treatments of the filler particles or of coupling agents; and the composite preparation procedures, for example whether or not solvents are used for dispersion of the filler in the matrix. The dependence of thermal conductivity on filler content, obtained from over one hundred reports in the literature, is examined in detail, and an attempt is made to categorise the effects of the variables and to compare the results obtained by different procedures.

To be fully recyclable, covalent adaptable networks must ultimately be able to overcome all topological restrictions and flow. By using a statistically based method, it was shown that the extent of stress relaxation in disulfide-containing thiol−ene polymers is closely correlated to the fraction of permanent cross-links. Given sufficient disulfide content, the cured materials can be recycled at moderate temperatures with no apparent loss in tensile properties. Since the materials also feature ester bonds, even when the disulfide content is low, one can increase the reprocessing temperature to trigger transesterification reactions which ensure full stress relaxation and recyclability.

Thermally conducting and electrically insulating materials have been prepared by filling an epoxy-thiol system with boron nitride (BN) particles of different shapes (platelets and agglomerates) and sizes (from 2 to 180 μm), and hence with different specific surface areas. The cure kinetics has been studied by differential scanning calorimetry in both non-isothermal and isothermal modes, and it has been shown that there is a systematic dependence of the cure kinetics on the BN content, the cure reaction generally being retarded by the addition of the BN particles. For filler loadings greater than about 30 vol%, the retardation of the cure, in both isothermal and non-isothermal mode, appears also to decrease as the specific surface area decreases. For the smallest (2 μm) platelets, which have a significantly higher specific surface area (10 m 2 g −1), the retardation is particularly pronounced, and this aspect is rationalized in terms of the activation energy and frequency factor of the reaction. The thermal conductivity of the cured epoxy-thiol-BN composites has been measured using the transient hot bridge method and is found to increase in the usual way with increasing BN content for all the particle types and sizes. For the platelets, the thermal conductivity increases with increasing particle size, mirroring the effect of BN content on the cure kinetics. The agglomerates, though, give the highest values of thermal conductivity, contrary to what might be expected in the light of their specific surface areas. Scanning electron microscopy of the fracture surfaces of the cured composites has been used to show that the interface between epoxy matrix and filler particles is better for the agglomerates. This, together with the reduced interfacial area, explains their higher thermal conductivity.

This work demonstrates that the application of even moderate pressures during cure can result in a remarkable enhancement of the thermal conductivity of composites of epoxy and boron nitride (BN). Two systems have been used: epoxy-thiol and epoxy–diamine composites, filled with BN particles of different sizes and types: 2, 30 and 180 μm platelets and 120 μm agglomerates. Using measurements of density and thermal conductivity, samples cured under pressures of 175 kPa and 2 MPa are compared with the same compositions cured at ambient pressure. The thermal conductivity increases for all samples cured under pressure, but the mechanism responsible depends on the composite system: For epoxy–diamine composites, the increase results principally from a reduction in the void content; for the epoxy–thiol system with BN platelets, the increase results from an improved matrix-particle interface; for the epoxy–thiol system with BN agglomerates, which has a thermal conductivity greater than 10 W/mK at 44.7 vol.% filler content, the agglomerates are deformed to give a significantly increased area of contact. These results indicate that curing under pressure is an effective means of achieving high conductivity in epoxy-BN composites.

When an amorphous polymer is cooled under pressure from above its glass transition temperature to room temperature, and then the pressure is released, this results in a densified state of the glass. This procedure applied to an epoxy composite system filled with boron nitride (BN) particles has been shown to increase the density of the composite, reduce its enthalpy, and, most importantly, significantly enhance its thermal conductivity. An epoxy-BN composite with 58 wt% BN platelets of average size 30 µm has been densified by curing under pressures of up to 2.0 MPa and then cooling the cured sample to room temperature before releasing the pressure. It is found that the thermal conductivity is increased from approximately 3 W/mK for a sample cured at ambient pressure to approximately 7 W/mK; in parallel, the density increases from 1.55 to 1.72 ± 0.01 g/cm3. This densification process is much more effective in enhancing the thermal conductivity than is either simply applying pressure to consolidate the epoxy composite mixture before curing or applying pressure during cure but then removing the pressure before cooling to room temperature; this last procedure results in a thermal conductivity of approximately 5 W/mK. Furthermore, it has been shown that the densification and corresponding effect on the thermal conductivity is reversible; it can be removed by heating above the glass transition temperature and then cooling without pressure and can be reinstated by again heating above the glass transition temperature and then cooling under pressure. This implies that a densified state and an enhanced thermal conductivity can be induced even in a composite prepared without the use of pressure.

Quantum machine learning has experienced a significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique (\({\text{log}}_{2}N\)) for embedding classical data into quantum states and estimating the inner product on 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the \({\text{log}}_{2}N\) encoding increases predictive performance with up to +2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers.

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