The ambient metastability of the rock-salt phase in well-defined model systems comprising nanospheres or nanorods of cadmium selenide, cadmium sulfide, or both was investigated as a function of composition, initial crystal phase, particle structure, shape, surface functionalization, and ordering level of their assemblies. Our experiments show that these nanocrystal systems exhibit ligand-tailorable reversibility in the rock salt–to–zinc blende solid-phase transformation. Interparticle sintering was used to engineer kinetic barriers in the phase transformation to produce ambient-pressure metastable rock-salt structures in a controllable manner. Interconnected nanocrystal networks were identified as an essential structure that hosted metastable high-energy phases at ambient conditions. These findings suggest general rules for transformation-barrier engineering that are useful in the rational design of next-generation materials.

Health care systems are at the front line to fight the COVID-19 pandemic. Emergent questions for each hospital are how many general ward and intensive care unit beds are needed, and additionally, how to optimally allocate these resources during demand surge to effectively save lives. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a hybrid computer simulation approach, with a system dynamic model to predict COVID-19 cases and a discrete-event simulation to evaluate hospital bed utilization and subsequently determine bed allocations. Two control policies, the type-dependent admission control policy and the early stepdown policy, based on patient risk profiling, were proposed to lower the overall death rate of the patient population in need of intensive care. The model was validated using historical patient census data from the

Health care systems are at the frontline to fight the COVID-19 pandemic. An emergent question for each hospital is how many general ward and intensive care unit beds are needed and how much personal protective equipment to be purchased. However, hospital pandemic preparedness has been hampered by a lack of sufficiently specific planning guidelines. In this paper, we developed a computer simulation approach to evaluating bed utilizations and the corresponding supply needs based on the operational considerations and constraints in individual hospitals. We built a data-driven SEIR model which is adaptive to control policies and can be utilized for regional forecast targeting a specific hospital’s catchment area. The forecast model was integrated into a discrete-event simulation which modeled the patient flow and the interaction with hospital resources. We tested the simulation model outputs against patient census data from UF Health Jacksonville, Jacksonville, FL. Simulation results were consistent with the observation that the hospital has ample bed resources to accommodate the regional COVID patients. After validation, the model was used to predict future bed utilizations given a spectrum of possible scenarios to advise bed planning and stockpiling decisions. Lastly, how to optimally allocate hospital resources to achieve the goal of reducing the case fatality rate while helping a maximum number of patients to recover was discussed. This decision support tool is tailored to a given hospital setting of interest and is generalizable to other hospitals to tackle the pandemic planning challenge.

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