Realizing an untethered, low-cost, and comfortably wearable respiratory rate sensor for long-term breathing monitoring application still remains a challenge. In this paper, a conductive-textile-based wearable respiratory rate sensing technique based on the capacitive sensing approach is proposed. The sensing unit consists of two conductive textile electrodes that can be easily fabricated, laminated, and integrated in garments. Respiration cycle is detected by measuring the capacitance of two electrodes placed on the inner anterior and posterior sides of a T-shirt at either the abdomen or chest position. A convenient wearable respiratory sensor setup with a capacitance-to-voltage converter has been devised. Respiratory rate as well as breathing mode can be accurately identified using the designed sensor. The sensor output provides significant information on respiratory flow. The effectiveness of the proposed system for different breathing patterns has been evaluated by experiments.

Solvent interactions with adsorbed moieties involved in surface reactions are often believed to be similar for different metal surfaces. However, solvents alter the electronic structures of surface atoms, which in turn affects their interaction with adsorbed moieties. To reveal the importance of metal identity on aqueous solvent effects in heterogeneous catalysis, we studied solvent effects on the activation free energies of the O–H and C–H bond cleavages of ethylene glycol over the (111) facet of six transition metals (Ni, Pd, Pt, Cu, Ag, Au) using an explicit solvation approach based on a hybrid quantum mechanical/molecular mechanical (QM/MM) description of the potential energy surface. A significant metal dependence on aqueous solvation effects was observed that suggests solvation effects must be studied in detail for every reaction system. The main reason for this dependence could be traced back to a different amount of charge-transfer between the adsorbed moieties and metals in the reactant and transition states for the different metal surfaces.

Computational catalyst discovery involves identification of a meaningful model and suitable descriptors that determine the catalyst properties. We study the impact of combining various descriptors (e.g., reaction energies, metal descriptors, and bond counts) for modeling transition-state energies (TS) based on a database of adsorption and TS energies across transition-metal surfaces for the decarboxylation and decarbonylation of propionic acid, a chemistry characteristic for biomass conversion. Results of different machine learning models for more than 1572 descriptor combinations suggest that there is no statistically significant difference between linear and nonlinear models when using the right combination of reactant energies, metal descriptors, and bond counts. However, linear models are inferior when not including bond count and metal descriptors. Furthermore, when there are missing data for reaction steps on all metals, conventional linear scaling is inferior to linear and nonlinear models with proper choice of descriptors that are surprisingly robust.

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