An Open-Source Environmental Chamber for Materials-Stability Testing Using an Optical Proxy

Abstract

This study is motivated by the desire to disseminate a low-cost, high-precision, high-throughput environmental chamber to test materials and devices under elevated humidity, temperature, and light. This paper documents the creation of an open-source tool with a bill of materials as low as US$2,000, and the subsequent evolution of three second-generation tools installed at three different universities spanning thin films, bulk crystals, and thin-film solar-cell devices. We introduce an optical proxy measurement to detect real-time phase changes in materials. We present correlations between this optical proxy and standard X-ray diffraction measurements, describe some edge cases where the proxy measurement fails, and report key learnings from the technology-translation process. By sharing lessons learned, we hope that future open-hardware development and translation efforts can proceed with reduced friction. Throughout the paper, we provide examples of scientific impact, wherein participating laboratories used their environmental chambers to study and improve the stabilities of halide-perovskite materials. All generations of hardware bills of materials, assembly instructions, and operating codes are available in open-source repositories.

Vision-driven Autocharacterization of Perovskite Semiconductors

“…Furthermore, the process of band gap curve fitting using inclination angles developed by Escobedo-Morales et al [4] produces numerical inconsistencies and is removed in our implementation in favor of a simpler and more robust version of RMSE minimization. Keesey & Tiihonen et al [7] develop a procedure of optically extracting a figure of merit to quantify the stability of perovskites but this methodology requires the manual segmentation of samples one by one, which becomes infeasible for a human researcher when the number of samples increases significantly. In this paper, we build from these prior works by demonstrating the performance of three autocharacterization methods that are scalable to arbitrarily many samples using a computer vision-driven approach.…”

“…To determine stability, the perovskite semiconductors were subject to an accelerated degradation chamber, and the three-channel RGB approximation of the color change of the sample was recorded as a proxy to sample stability. [7] The chamber, pictured in Figure 5, maintains a uniform temperature, illumination, and humidity level around the samples, and obtains real-time images of the perovskite semiconductors every time step ∆t for a duration of time T resulting in Ω(∆t) images. [7] The vision segmentation algorithm described in Algorithm 1 is applied to the first droplet image, Ω, to output the segmented locations and reflectance data, Φ, of each spatiallynon uniform sample across the duration of the experiment with a fixed camera position.…”

“…A timeseries of these conditions over the course of the experiment is illustrated in Figure S-5. The exact construction and operation of the degradation chamber setup is detailed in Keesey & Tiihonen et al [7] under the "Second generation chamber at MIT (thin films)" section. During the 6-hour degradation experiment, RGB images of the samples are taken at 10 second intervals, acquired with the Thorlabs, Inc. DCC1645C camera.…”

“…In the perovskite semiconductor system, the performance metrics of band gap and stability are largely governed by the chemical composition of the material [23,7]. However, since the 80 samples are synthesized using the high-throughput method described in Section 2.1, the exact composition of each unique deposit (the value of x) is not known a priori.…”