Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model.

Projects such as the European Covid-19 Forecast Hub publish forecasts on the national level for new deaths, new cases, and hospital admissions, but not direct measurements of hospital strain like critical care bed occupancy at the sub-national level, which is of particular interest to health professionals for planning purposes. We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underperformed compared to the ensemble baseline for 5 of them at the 90% confidence level (n = 38). However, for the same level at the 4 week horizon, our model was never statistically outperformed for any unit despite outperforming the baselines 10 times spanning 7 out of 14 geographical units. This implies modest forecasting utility for longer horizons which may justify the application of non-Markovian compartmental models in the context of hospital-strain surveillance for future pandemics.

Facile Synthesis of Electrically Conductive and Heatable Nanoparticle/Nanocarbon Hybrid Aerogels.

Joule heating studies on nanoparticle/nanocarbon hybrid aerogels have been reported, but systematic investigations on hydrotalcite-derived catalysts supported onto reduced graphene oxide (rGO) aerogels are rare. In this study, hydrotalcite-derived Cu-Al2O3 nanoparticles were incorporated into a porous and multifunctional rGO aerogel support for fabricating electrically conducting Cu-Al2O3/rGO hybrid aerogels, and their properties were investigated in detail. The hybridization of Cu-Al2O3 with a 3D nanocarbon support network imparts additional functionalities to the widely used functional inorganic nanoparticles, such as direct electrical framework heating and easy regeneration and separation of spent nanoparticles, with well-spaced nanoparticle segregation. 3D variable-range hopping model fitting confirmed that electrons were able to reach the entire aerogel to enable uniform resistive heating. The conductivity of the nanocarbon support framework facilitates uniform and fast heating (up to 636 K/min) of the embedded nanoparticles at very low energy consumption, while the large porosity and high thermal conductivity enable efficient heat dissipation during natural cooling (up to 336 K/min). The thermal stability of the hybrid aerogel was demonstrated by repeated heating/cooling cycling at different temperatures that were relevant to important industrial applications. The facile synthetic approach can be easily adapted to fabricate other types of multifunctional nanoparticle/nanocarbon hybrid aerogels, such as the MgAl-MMO/rGO aerogel and the Ni-Al2O3/rGO aerogel. These findings open up new routes to the functionalization of inorganic nanoparticles and extend their application ranges that involve electrical/thermal heating, temperature-dependent catalysis, sorption, and sensing.

Autonomous optimisation of a nanoparticle catalysed reduction reaction in continuous flow.

An automated continuous flow reactor system equipped with inline analysis, was developed and applied in the self-optimisation of a nanoparticle catalysed reaction. The system was used to optimise the experimental conditions of a gold nanoparticle catalysed 4-nitrophenol reduction reaction, towards maximum conversion in under 2.5 hours. The data obtained from this optimisation was then used to generate a kinetic model, allowing us to predict the outcome of the reaction under different conditions. By combining continuous flow nanoparticle synthesis with this approach, the development timeline for these emerging catalysts could be significantly accelerated.

Utilizing Cyclic Voltammetry to Understand the Energy Storage Mechanisms for Copper Oxide and its Graphene Oxide Hybrids as Lithium-Ion Battery Anodes.

Invited for this month's cover is the group of Robert A. W. Dryfe at the University of Manchester in collaboration with William Blythe Ltd. (Lancashire). The image shows bees building a graphene-containing battery that powers an external circuit, depicted by a purple curve. This curve represents a cyclic voltammogram, specifically of copper oxide during a charge/discharge cycle in a Li-ion half-cell, as presented in the article. The use of bees has both scientific and geographic significance. For the former, bees are assisting in building the honeycomb-like hexagonal graphene lattice, just as they do in nature. Geographically, bees are symbolic to the locations of both collaborators on this project, the University of Manchester and William Blythe (Lancashire). The Full Paper itself is available at 10.1002/cssc.201902784.

Utilizing Cyclic Voltammetry to Understand the Energy Storage Mechanisms for Copper Oxide and its Graphene Oxide Hybrids as Lithium-Ion Battery Anodes.

Graphene-based materials have been extensively researched as a means improve the electrochemical performance of transition metal oxides in Li-ion battery applications, however an understanding of the effect of the different synthesis routes, and the factors underlying the oft-stated better performance of the hybrid materials (compared to the pure metal oxides) is not always demonstrated. For the first time, we report a range of synthetic routes to produce graphene oxide (GO)-coated CuO, micro-particle/GO "bundles" as well as nano-particulates decorated on GO sheets to enable a comparison with CuO and its carbon-coated analogue, as confirmed using scanning electron microscopy (SEM) imaging and Raman spectroscopy. Cyclic voltammetry was utilized to probe the lithiation/delithiation mechanism of CuO by scanning at successively decreasing vertex potentials, uncovering the importance of a full reduction to Cu metal on the reduction step. The GO hybrid materials clearly show enhanced specific capacities and cycling stabilities comparative to the CuO, with the most promising material achieving a capacity of 746 mAh g-1 and capacity retention of 92 % after 30 cycles, which is the highest stable capacity quoted in literature for CuO. The simple cyclic voltammetry technique used in this work could be implemented to help further understand any conversion-type anode materials, in turn accelerating the research and industrial development of conversion anodes.