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Utilizing hot electrons generated from localized surface plasmon resonance is of widespread interest in the photocatalysis of metallic nanoparticles. However, hot holes, especially generated from interband transitions, have not been fully explored for photocatalysis yet. In this study, a photocatalyzed Suzuki-Miyaura reaction using mesoporous Pd nanoparticle photocatalyst served as a model to study the role of hot holes. Quantum yields of the photocatalysts increase under shorter wavelength excitations and correlate to "deeper" energy of the holes from the Fermi level. This work suggests that deeper holes in the d-band catalyze the oxidative addition of aryl halide R-X onto Pd0 at the nanoparticles' surface to form R-PdII-X complex, thus accelerating the rate-determining step of the catalytic cycle. The hot electrons do not play a decisive role. In the future, catalytic mechanisms induced by deep holes should deserve as much attention as the well-known hot electron transfer mechanism.

Application of machine learning (ML)-based algorithms in food drying is an exciting and innovative approach to advance the drying technology. In order to appropriately develop this novel approach in all aspects of food drying field, significant scientific research is required. The main aspects of food drying research are the determination of material properties, microstructural characterization, mathematical modeling, and process optimization. It is essential to express this fundamental information through ML-based algorithms to advance the food drying research. This paper aims to present a comprehensive review of the application of machine learning-based approaches in food drying modeling, property prediction, microstructural characterization, and process parameters optimization. Moreover, this paper discusses the possibilities and challenges to apply ML-based algorithms in multiscale modeling and microwave-based hybrid drying. It is expected that this review paper will be beneficial in advancing the machine learning-based food drying technology.

Nowadays, limited energy resources face ever-growing demands of the modern world. One engineering approach to mitigate this problem which has received considerable attention in recent years is using latent heat thermal storage (LHTS) systems, a significant opportunity which is provided by phase change materials (PCMs). In the present study, a numerical investigation was devoted to estimate the simultaneous freezing and melting processes of a double-layer PCM in terms of heat transfer and fluid flow phenomena. A double-pipe cylindrical channel with two compartments, A and B, was considered for locating two PCMs of RT28 and RT35 in various arrangements. The inner and outer walls were exposed to both hot and cold heat transfer fluids (HHTFs and CHTFs, respectively) beginning with solid or liquid initial state, which led to solid–liquid phase change process through PCMs. The numerical simulation was handled by a two-dimensional finite volume method (FVM) with a fixed Rayleigh number of 106 in which conduction and convection heat transfer mechanisms are taken into account. The effects of employing double-layer PCM and their arrangements, inner and outer walls’ boundary conditions, and initial statuses of PCMs are discussed, and the details of the compared results are shown in the form of temperature and liquid fraction variations over time.

Food processing is a complex, multifaceted problem that requires substantial human interaction to optimize the various process parameters to minimize energy consumption and ensure better-quality products. The development of a machine learning (ML)-based approach to food processing applications is an exciting and innovative idea for optimizing process parameters and process kinetics to reduce energy consumption, processing time, and ensure better-quality products; however, developing such a novel approach requires significant scientific effort. This paper presents and evaluates ML-based approaches to various food processing operations such as drying, frying, baking, canning, extrusion, encapsulation, and fermentation to predict process kinetics. A step-by-step procedure to develop an ML-based model and its practical implementation is presented. The key challenges of neural network training and testing algorithms and their limitations are discussed to assist readers in selecting algorithms for solving problems specific to food processing. In addition, this paper presents the potential and challenges of applying ML-based techniques to hybrid food processing operations. The potential of physics-informed ML modeling techniques for food processing applications and their strategies is also discussed. It is expected that the potential information of this paper will be valuable in advancing the ML-based technology for food processing applications.