Autonomous Exploration of Catalyst Layer Drying Process of PEMFC
Authors: Nagato Keisuke , Oya Hirotaka , Tanaka Akihisa , Inoue Gen , Tsujiguchi Takuya 
Published: December 2024

Recent Materials Informatics (MI) has been drastically extended to experiment-based materials exploration methods, especially applied to catalytic materials[1], organic materials[2] and inorganic materials[3]. “Process”, which is located between the materials and actual products, is also important to generate “shape-having” materials. However, the number of candidates in process exploration is much greater than those of material because the process is downstream of the material. Powder-film-formation processes are widely used to produce functional devices such as fuel cells and batteries, and it is difficult to construct a numerical simulation model of powder phenomena because the dynamics of particles and interaction of solvent are occurred in micro-scale and high-speed. In order to discover the optimum parameter from a huge number of parameters, an autonomous-experiment parameter-exploration method is useful. Our group has developed and demonstrated a powder-film-drying process exploration using experimental-based Bayesian optimization for catalyst layer drying process of polymer-electrolyte membrane fuel cells (PEMFCs)[4]. In this study, we investigated an effect of a hyper-parameter on the result of exploration using a virtual exploration.
The prepared ink was made by dispersing carbon particles (Vulcan XC-72R) and Nafion in water and 2-propanol (IPA). 30 μL of the ink was dropped onto a glass substrate placed on a rubber heater. The heating temperature profile to be set for the heater was the parameters to be optimized, and the drying was executed with the heating temperature profile indicated by the machine learning system. The heater temperature was controlled for 10 min. after the drop, and the temperature was specified in eight steps ranging from 30 ℃ to 100 ℃ every 2 min., which meant the total number of parameter candidate was 85 = 32768. The captured images of the dried droplets were binarized to crack areas, and the ratio of the exposed areas to the droplet area was defined as the “crack ratio,” and used for evaluation.
Using the system, we demonstrated an exploration of the heating temperature profile which minimize the defect degree of carbon powder film for PEMFCs. We found an optimal heating pattern with 40 trials (30 random initial trials and 10 learning trials) from 32,768 (85) total patterns. Furthermore, the optimal heating pattern was novel, prepulsed-heating method. We discuss that the prepulsed heating forms a strong surface and the main heating dries the particles inside gradually.
We use the upper confidence bound (UCB) represented by

UCB(x)=μ(x)+cσ(x) (1)
where μ(x) of y for each parameter value combination x, the standard deviation σ(x). Figure 1(a) shows the exploration history published in ref[4]. First, we virtually carry out the exploration after removing the three low crack ratio points among initial data, i.e., we set a severer condition. The exploration result as shown in Figure 1(b) was similar to that of the condition before removing. Next, we varied c factor to 100 from 0.5, that means likelihood was increased. As shown in Figure 1(c), even though the low crack ratio was explored, the system tried to search other parameter candidate widely and the exploration was not saturated even after 70 times iteration.
References
[1] B. Burger et al., Nature 583, 237-241 (2020).
[2] B.P. MacLeod et al., Sci. Adv. 13, eaaz8867 (2020).
[3] R. Shimizu et al., APL Mater. 8, 111110 (2020).
[4] K. Nagai et al., Sci. Rep. 12, 1615 (2022).

Figure 1