RAISE: A self-driving laboratory for interfacial property formulation discovery
🤖 AI Summary
To address the challenge of rapidly optimizing liquid formulation wettability (contact angle) for biomedical devices, functional coatings, and smart textiles, this work introduces RAISE—a closed-loop, self-driving experimental platform. RAISE integrates robotic liquid formulation, high-throughput droplet deposition, automated imaging, and contact angle analysis, and—novelty—embeds multi-objective Bayesian optimization directly into the autonomous experimental loop. It compensates for raw-material purity variations via tunable formulations and simultaneously optimizes wettability, surfactant consumption, and cost using a composite expected improvement metric. The system achieves ~1 contact-angle measurement per minute, substantially accelerating formulation exploration and enhancing experimental reproducibility. The core contribution lies in the deep integration of multi-objective Bayesian optimization with autonomous experimentation, establishing a transferable, intelligent paradigm for materials formulation development.
Application Category
📝 Abstract
Surface wettability is a critical design parameter for biomedical devices, coatings, and textiles. Contact angle measurements quantify liquid-surface interactions, which depend strongly on liquid formulation. Herein, we present the Robotic Autonomous Imaging Surface Evaluator (RAISE), a closed-loop, self-driving laboratory that is capable of linking liquid formulation optimization with surface wettability assessment. RAISE comprises a full experimental orchestrator with the ability of mixing liquid ingredients to create varying formulation cocktails, transferring droplets of prepared formulations to a high-throughput stage, and using a pick-and-place camera tool for automated droplet image capture. The system also includes an automated image processing pipeline to measure contact angles. This closed loop experiment orchestrator is integrated with a Bayesian Optimization (BO) client, which enables iterative exploration of new formulations based on previous contact angle measurements to meet user-defined objectives. The system operates in a high-throughput manner and can achieve a measurement rate of approximately 1 contact angle measurement per minute. Here we demonstrate RAISE can be used to explore surfactant wettability and how surfactant combinations create tunable formulations that compensate for purity-related variations. Furthermore, multi-objective BO demonstrates how precise and optimal formulations can be reached based on application-specific goals. The optimization is guided by a desirability score, which prioritizes formulations that are within target contact angle ranges, minimize surfactant usage and reduce cost. This work demonstrates the capabilities of RAISE to autonomously link liquid formulations to contact angle measurements in a closed-loop system, using multi-objective BO to efficiently identify optimal formulations aligned with researcher-defined criteria.
Problem
Research questions and friction points this paper is trying to address.
Autonomously links liquid formulations to surface wettability assessment
Optimizes surfactant combinations using multi-objective Bayesian optimization
Achieves high-throughput contact angle measurements for biomedical applications
Innovation
Methods, ideas, or system contributions that make the work stand out.
Closed-loop robotic system autonomously mixes liquid formulations
Automated imaging pipeline measures contact angles for wettability
Bayesian optimization iteratively explores formulations meeting multi-objective criteria
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