Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemble motion. Coarse-grained models are therefore used in place of costly all-atom simulations, accessing longer time scales and larger systems. Nevertheless, constructing the many-body potential of mean force that defines the structure and dynamics of a coarse-grained system can be complicated and computationally intensive. Machine learning shows great promise for the linked challenges of dimensionality reduction and learning the potential of mean force. To improve the coarse-graining of ILs, we present a neural network model trained on all-atom classical molecular dynamics simulations. The potential of mean force is expressed as two jointly trained neural network interatomic potentials that learn the coupled short-range and many-body long range molecular interactions. These interatomic potentials treat temperature as an explicit input variable to capture its influence on the potential of mean force. The model reproduces structural quantities with high fidelity, outperforms the temperature-independent baseline at capturing dynamics, generalizes to unseen temperatures, and incurs low simulation cost.
Biodegradable polymers are increasingly employed at the heart of therapeutic devices. Particularly in the form of thin and elongated fibers, they offer an effective strategy for controlled release in a variety of biomedical configurations such as sutures, scaffolds, wound dressings, surgical or imaging probes, and smart textiles. So far however, the fabrication of fiber-based drug delivery systems has been unable to fulfill significant requirements of medicated fibers such as multifunctionality, adequate mechanical strength, drug loading capability, and complex release profiles of multiple substances. Here, a novel paradigm in the design and fabrication of microstructured biodegradable fibers with tailored mechanical properties and capable of predefined release patterns from multiple reservoirs is proposed. Different biodegradable polymers compatible with the scalable thermal drawing process are identified, and their release properties as thin films of various thicknesses in the fiber form are experimentally investigated and modeled. Multimaterial microstructured fibers with predictable complex release profiles of potentially different substances are then designed and fabricated. Moreover, the tunability of the mechanical properties via tailoring the drawing process parameters is demonstrated, as well as the ability to weave such fibers. This work establishes a novel platform for biodegradable microstructured fibers for applications in implants, sutures, wound dressing, or tissue scaffolds.
Solid polymer electrolytes
(SPEs) have the potential to improve
lithium-ion batteries by enhancing safety and enabling higher energy
densities. However, SPEs suffer from significantly lower ionic conductivity
than liquid and solid ceramic electrolytes, limiting their adoption
in functional batteries. To facilitate more rapid discovery of high
ionic conductivity SPEs, we developed a chemistry-informed machine
learning model that accurately predicts ionic conductivity of SPEs.
The model was trained on SPE ionic conductivity data from hundreds
of experimental publications. Our chemistry-informed model encodes
the Arrhenius equation, which describes temperature activated processes,
into the readout layer of a state-of-the-art message passing neural
network and has significantly improved accuracy over models that do
not encode temperature dependence. Chemically informed readout layers
are compatible with deep learning for other property prediction tasks
and are especially useful where limited training data are available.
Using the trained model, ionic conductivity values were predicted
for several thousand candidate SPE formulations, allowing us to identify
promising candidate SPEs. We also generated predictions for several
different anions in poly(ethylene oxide) and poly(trimethylene carbonate),
demonstrating the utility of our model in identifying descriptors
for SPE ionic conductivity.
Solid-state narrow band gap semiconductor heterostructures with a Z-scheme charge-transfer mechanism are the most promising photocatalytic systems for water splitting and environmental remediation under visible light. Herein, we construct all-solid Z-scheme photocatalytic systems from earth abundant elements (Ca and Fe) using an aqueous synthesis procedure. A novel Z-scheme two-component Fe2O3/Ca2Fe2O5 heterostructure is obtained in a straightforward manner by soaking various iron-containing nanoparticles (amorphous and crystalline) with Ca(NO3)2 and performing short (20 min) thermal treatments at 820 °C. The obtained powder materials show high photocatalytic performances for methylene blue dye degradation under visible light (45 mW/cm 2), exhibiting a rate constant up to 0.015 min −1. The heterostructure exhibits a five-fold higher activity compared to that of pristine hematite. The experiments show that amorphous iron-containing substrate nanoparticles trigger the Fe2O3/Ca2Fe2O5 heterostructure formation. We extended our study to produce Fe2O3/Ca2Fe2O5 nanoheterostructure photoanodes via the electrochemical deposition of amorphous iron-containing sediment were used. The visiblelight (15 mW/cm 2) photocurrent increases from 183 μA/cm 2 to 306 μA/cm 2 after coupling hematite and Ca2Fe2O5. Notably, the powders and photoanodes exhibit distinct charge-transfer mechanisms evidenced by the different stabilities of the heterostructures under different working conditions.
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