Machine intelligence accelerated design of conductive MXene aerogels with programmable properties.

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti3C2Tx MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels' structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels' physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management.

Bacterial Cross-contamination Potential Associated with Contaminated Currency.

Previous studies have shown a significant amount of contaminants on paper currencies. It is important to study the transfer of microorganisms between paper currencies to determine whether it meets the level of a human health threat. This cross-contamination potential was analyzed by seeding new US 1-dollar bills with Bacillus thuringiensis, and pressing or rubbing them against clean currency to determine the amount of bacteria transfer to the unseeded currency. The transferred amount of bacteria was recovered, plated, incubated, and the colony-forming units were quantified. Among the recovery methods tested, the most efficient method, vortexing for 10 min with a recovery efficiency of 40 ± 8.1%, was used in this analysis. The resulting transfer rates were 4.8%, 8.6%, and 14.3% when pressed for 24 h, 72 h, and rubbed together, respectively. These transferred amounts of bacteria are significant and have the potential to spread infectious diseases.