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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...

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.

A spatiotemporal experimental route is reported for the antisolvent vapor diffusion crystal growth of metal halide perovskitoids. A computational analysis combining automated image capture and diffusion modeling enables the determination of the critical concentrations required for nucleation and crystal growth from a single experiment. Five different solvent systems and ten distinct organic ammonium iodide salts were investigated with lead iodide, from which nine previously unreported compounds were discovered. Automated image capture of the mother liquor and antisolvent vials was used to determine changes in solution meniscus positions and detect the nucleation event location. Matching the observations to a numerical solution of Fick’s second law diffusion model enables the calculation of reactant, solvent, and antisolvent concentrations at both the time and position of the first stable nucleation and crystal growth. A machine learning model was trained on the resulting data, and it reveals solvent- and amine-specific crystallization tendencies. Solvent systems that interact more weakly with dissolved lead species promote crystallization, while those with stronger interactions can prevent crystallization through increased solubilities. Organic amines that interact more strongly with inorganic components and exhibit greater rigidity are more likely to be incorporated into crystalline products.