Luca Bergamasco

Artificial Intelligence (AI) tools are revolutionizing the way we approach complex problems in engineering and science. Such tools are playing an increasingly important role also in the analysis of energy problems, as they offer novel approaches to, for example, improve the efficiency and optimize energy processes. Here, we will particularly focus on Physics-Informed Neural Networks (PINNs), a particular class of neural networks able to embed information on the governing physical laws for a given problem. The working principles of these networks, and their potential for heat transfer analysis are discussed based on three simple examples relevant for thermal science.

With the advent of Industry 4.0, Artificial Intelligence (AI) has created a favorable environment for the digitalization of manufacturing and processing, helping industries to automate and optimize operations. In this work, we focus on a practical case study of a brake caliper quality control operation, which is usually accomplished by human inspection and requires a dedicated handling system, with a slow production rate and thus inefficient energy usage. We report on a developed Machine Learning (ML) methodology, based on Deep Convolutional Neural Networks (D-CNNs), to automatically extract information from images, to automate the process. A complete workflow has been developed on the target industrial test case. In order to find the best compromise between accuracy and computational demand of the model, several D-CNNs architectures have been tested. The results show that, a judicious choice of the ML model with a proper training, allows a fast and accurate quality control; thus, the proposed workflow could be implemented for an ML-powered version of the considered problem. This would eventually enable a better management of the available resources, in terms of time consumption and energy usage.

We show here that soap films—typically expected to host symmetric molecular arrangements—can be constructed with differing opposite surfaces, breaking their symmetry, and making them reminiscent of functional biological motifs found in nature. Using fluorescent molecular probes as dopants on different sides of the film, resonance energy transfer could be employed to confirm the lack of symmetry, which was found to persist on timescales of several minutes. Further, a theoretical analysis of the main transport phenomena involved yielded good agreement with the experimental observations. Published by the American Physical Society 2024

The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimisation approaches are poorly suited to multi-variable multi-metric photocatalytic systems because they aim to optimise one performance metric while sacrificing the others and thereby limit overall system performance. Herein, we address this multi-metric challenge by defining a metric for holistic system performance that takes all figures of merit into account, and employ a machine learning algorithm to efficiently guide our experiments through the large parameter matrix to make holistic optimisation accessible for human experimentalists. As a test platform, we employ a five-component system that self-assembles into photocatalytic micelles for CO2-to-CO reduction, which we experimentally optimised to simultaneously improve yield, quantum yield, turnover number, and frequency while maintaining high selectivity. Leveraging the dataset with machine learning algorithms allows quantification of each parameter’s effect on overall system performance. The buffer concentration is unexpectedly revealed as the dominating parameter for optimal performance, and is nearly four times more important than the catalyst concentration. The expanded use and standardisation of this methodology to define and optimise holistic performance will accelerate progress in different areas of catalysis by providing unprecedented insights into performance bottlenecks, enhancing comparability, and taking results beyond comparison of subjective figures of merit.