Authors: Han, Yushu
Source: University of Liverpool (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Accelerating Porosity Assessment in Solid Materials via Semi-Automated Platforms The design and discovery of porous materials have become a central theme in materials science, driven by their applications in gas storage, separation, carbon capture, and catalysis. Rapid advances in synthetic chemistry, particularly in metal–organic frameworks, porous organic cages, and conjugated microporous polymers, have enabled the generation of increasingly large and diverse material libraries. At the same time, the emergence of automation and data-driven workflows has created unprecedented opportunities for accelerating materials discovery. Despite these advances, the characterization of porosity remains a persistent bottleneck. Conventional gas adsorption methods, although highly reliable, are slow, labor-intensive, and require substantial sample quantities, making them poorly suited to high-throughput or automated environments. This imbalance between synthesis and characterization constrains the efficiency of discovery pipelines and limits the potential of self-driving laboratory concepts. In response to this challenge, this thesis develops two complementary high-throughput pre-screening methodologies for porosity assessment. Chapter 2 develops a multichannel colorimetric dye sorption array that uses dyes of varied size/polarity plus computer-vision analytics to classify porosity from simple images. Chapter 3 applies this method to different classes of materials spanning POCs, CMPs, MOFs and related solids, achieving 98% classification accuracy against gas-uptake benchmarks, while reducing sample demand to the milligram scale and shortening analysis times from weeks to days; the chapter also details threshold tunability, semi-automation on a robotic station, reproducibility tests, and limitations for strongly water-adsorbing materials, including an exploration of an oil-soluble dye array (found to be supplementary rather than core). Chapter 4 introduces a 96-well infrared calorimetry platform that detects adsorption-induced heat during CO2 dosing to provide a semi-quantitative proxy for accessible porosity. Across up to ninety-six samples in parallel, the platform shows 100% accuracy in distinguishing porous from nonporous solids versus conventional adsorption and offers multi-dimensional kinetic/thermal features informative of uptake behavior. Chapter 5 compares the strengths, limitations, and applicability of the two platforms; sets out directions for future work in porous-materials discovery (broader material scope, DAC screening, multi-gas operation, sensor-embedded plates, and Bayesian-optimization–guided exploration); and outlines best-practice integration with automation workflows for synthesis, characterization, and closed-loop optimization. Collectively, these contributions establish the first integrated framework for high-throughput porosity pre-screening. By complementing rather than replacing conventional adsorption techniques, the methods enable efficient prioritization of candidate materials, alleviate a longstanding experimental bottleneck, and provide a scalable foundation for integration into automated, data-driven discovery pipelines. Porosity ; Characterization (materials science) ; Microporous material ; Automation ; Porous medium ; Sorption ; Sample (material) Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Mesoporous Materials and Catalysis