The change of the X atom to Br in Br·H2O results in the FeII sites being decoupled due to halogen atom steric bulk, resulting in the independent spin-state transition of Fe1 and Fe2 sites and a two-step spin-state transition pathway. Uniquely, all three possible spin-state transition pathways of a two-site switching system are observed in this family [(1) HS-HS ↔ HS-LS ↔ LS-LS for Br·H2O, (2) HS-HS ↔ LS-HS ↔ LS-LS for F·3H2O, and (3) HS-HS ↔ LS-LS for Cl·H2O for Fe1-Fe2]. Overall, these findings broadly support recent theoretical models but highlight that additional structural and topological complexities are needed to form a holistic picture of the drivers of elastic frustration.Flexible sensors with wide sensing ranges require responsiveness under tiny and large strains. However, the development of strain sensors with wide detection ranges is still a great challenge due to the conflict between the tiny strain requirements of sparse conductive networks and the large strain requirement of dense conductive networks. Herein, we present a facile method for fabricating a gradient conductive network composed of sparse and dense conductive networks. The surface penetration technology in which carbon black (CB) penetrated from the natural rubber latex (NRL) glove surface to the interior was used to fabricate a gradient conductive network. The prolonged immersion time from 1 to 30 min caused the penetration depth of CB to increase from 2 to 80 μm. Moreover, CB formed hierarchical rough micro- and nanoscale structures, creating a superhydrophobic surface. The gradient conductive network of sensors produced an ultrawide detection range of strain (0.05-300%) and excellent reliability and reproducibility. The sensors can detect a wide range of human motions, from tiny (wrist pulse) to large (joint movements) motion monitoring. The flexible sensors attached to a flexible basement can be used to detect pressure in a wide detection range (1.7-2900 kPa). Pressure responsiveness was used to detect the weight, sound pressure, and dripping of tiny droplets. The sensor showed an excellent response to organic solvents, and the response intensity increased with the increasing swelling degree of the solvent for NRL.This work reports the study of ZnO-based anodes for the photoelectrochemical regeneration of the oxidized form of nicotinamide adenine dinucleotide (NAD+). The latter is the most important coenzyme for dehydrogenases. However, the high costs of NAD+ limit the use of such enzymes at the industrial level. The influence of the ZnO morphologies (flower-like, porous film, and nanowires), showing different surface area and crystallinity, was studied. The detection of diluted solutions (0.1 mM) of the reduced form of the coenzyme (NADH) was accomplished by the flower-like and the porous films, whereas concentrations greater than 20 mM were needed for the detection of NADH with nanowire-shaped ZnO-based electrodes. The photocatalytic activity of ZnO was reduced at increasing concentrations of NAD+ because part of the ultraviolet irradiation was absorbed by the coenzyme, reducing the photons available for the ZnO material. The higher electrochemical surface area of the flower-like film makes it suitable for the regeneration reaction. The illumination of the electrodes led to a significant increase on the NAD+ regeneration with respect to both the electrochemical oxidation in dark and the only photochemical reaction. The tests with formate dehydrogenase demonstrated that 94% of the regenerated NAD+ was enzymatically active.A growing body of research focuses on engineering materials for electrochemical detection of dopamine (DA), a critical neurotransmitter involved in motor function, reward processes, and blood pressure regulation. Among various sensing materials, graphene is highly attractive due to its excellent electrical conductivity and, in particular, the π-π interaction between the aromatic rings of DA and graphene. However, the lowest detection limits reported solely using graphene are nominally 1 nM. To improve the sensor sensitivity, various strategies are being explored, including chemical functionalization, heterostructure/composite formation, elemental doping, and modification with biomolecules (aptamers, enzymes, etc.). In this work, we demonstrate that commercially available graphene ink can exhibit selective and highly sensitive detection of DA by tuning the surface chemistry utilizing a simple, one-step annealing process. The annealing condition directly impacts the sensor response to DA, with the optimal conditions (30 min at 300 °C under 3% H2 + Ar) yielding a distinguishable and selective response to DA down to 5 pM. X-ray photoelectron spectroscopy (XPS) confirms that the improved selectivity is due to the increased fraction of oxygen functionalities (in particular, C-OH), while Raman spectroscopy shows a higher degree of defectiveness for this condition compared to others. Evaluation of the interaction of three molecular components of DA (i.e., aromatic ring, hydroxyl groups, and amine group) with graphene confirms that the π-π interaction and -OH groups play a prominent role in the improved adsorption of DA on the graphene surface. Furthermore, we demonstrate a proof-of-concept, all-solution processable sensor on polyimide substrates using graphene ink. Tuning the sensor response by varying the annealing condition offers a simple avenue for developing sensitive, selective, and low-cost point-of-care biosensors, while low-temperature annealing ensures compatibility with flexible substrates, such as polyimide.The most direct approach to determining if two aqueous solutions will phase-separate upon mixing is to exhaustively screen them in a pair-wise fashion. IDF-11774 This is a time-consuming process that involves preparation of numerous stock solutions, precise transfer of highly concentrated and often viscous solutions, exhaustive agitation to ensure thorough mixing, and time-sensitive monitoring to observe the presence of emulsion characteristics indicative of phase separation. Here, we examined the pair-wise mixing behavior of 68 water-soluble compounds by observing the formation of microscopic phase boundaries and droplets of 2278 unique 2-component solutions. A series of machine learning classifiers (artificial neural network, random forest, k-nearest neighbors, and support vector classifier) were then trained on physicochemical property data associated with the 68 compounds and used to predict their miscibility upon mixing. Miscibility predictions were then compared to the experimental observations. The random forest classifier was the most successful classifier of those tested, displaying an average receiver operator characteristic area under the curve of 0.