Depolymerization of plastics by means of electrified spatiotemporal heating.

Depolymerization is a promising strategy for recycling waste plastic into constituent monomers for subsequent repolymerization1. However, many commodity plastics cannot be selectively depolymerized using conventional thermochemical approaches, as it is difficult to control the reaction progress and pathway. Although catalysts can improve the selectivity, they are susceptible to performance degradation2. Here we present a catalyst-free, far-from-equilibrium thermochemical depolymerization method that can generate monomers from commodity plastics (polypropylene (PP) and poly(ethylene terephthalate) (PET)) by means of pyrolysis. This selective depolymerization process is realized by two features: (1) a spatial temperature gradient and (2) a temporal heating profile. The spatial temperature gradient is achieved using a bilayer structure of porous carbon felt, in which the top electrically heated layer generates and conducts heat down to the underlying reactor layer and plastic. The resulting temperature gradient promotes continuous melting, wicking, vaporization and reaction of the plastic as it encounters the increasing temperature traversing the bilayer, enabling a high degree of depolymerization. Meanwhile, pulsing the electrical current through the top heater layer generates a temporal heating profile that features periodic high peak temperatures (for example, about 600 °C) to enable depolymerization, yet the transient heating duration (for example, 0.11 s) can suppress unwanted side reactions. Using this approach, we depolymerized PP and PET to their monomers with yields of about 36% and about 43%, respectively. Overall, this electrified spatiotemporal heating (STH) approach potentially offers a solution to the global plastic waste problem.

A stable atmospheric-pressure plasma for extreme-temperature synthesis.

Plasmas can generate ultra-high-temperature reactive environments that can be used for the synthesis and processing of a wide range of materials1,2. However, the limited volume, instability and non-uniformity of plasmas have made it challenging to scalably manufacture bulk, high-temperature materials3-8. Here we present a plasma set-up consisting of a pair of carbon-fibre-tip-enhanced electrodes that enable the generation of a uniform, ultra-high temperature and stable plasma (up to 8,000 K) at atmospheric pressure using a combination of vertically oriented long and short carbon fibres. The long carbon fibres initiate the plasma by micro-spark discharge at a low breakdown voltage, whereas the short carbon fibres coalesce the discharge into a volumetric and stable ultra-high-temperature plasma. As a proof of concept, we used this process to synthesize various extreme materials in seconds, including ultra-high-temperature ceramics (for example, hafnium carbonitride) and refractory metal alloys. Moreover, the carbon-fibre electrodes are highly flexible and can be shaped for various syntheses. This simple and practical plasma technology may help overcome the challenges in high-temperature synthesis and enable large-scale electrified plasma manufacturing powered by renewable electricity.

Modelling the effect of surface charging on plasma synthesis of ammonia using DFT.

Non-equilibrium plasma has been found to have a synergistic effect on catalytic synthesis of NH3. The non-equilibrium plasma and catalyst surface together could affect NH3 synthesis through several mechanisms. Charging of the catalyst surface in the presence of non-equilibrium plasma is one such mechanism. We employed density functional theory (DFT) calculations to understand the effect of surface charge on surface reactivity of γ-Al2O3 supported single metal atom catalysts and a metal cluster. We investigated the effect of surface charge on adsorption energies of common adsorbates involved in NH3 synthesis. It is found that adsorption energy of N, N2, H, H2, NH and NH2 on metal atoms increases by up to ∼1.2 eV, whereas NH3 desorption is increased by up to 0.45 eV upon surface charging. The present results provide a new mechanism of plasma enhanced catalysis potentially explaining why Ni, Pt and Co have better catalytic performance compared to Ru and Fe in ammonia plasma catalysis. Furthermore, we found that the correlations between adsorption energies of adsorbates change significantly with surface charging. These findings suggest that surface charging might play an important role in plasma synthesis of NH3.

Quasi-Classical Trajectory Calculation of Rate Constants Using an Ab Initio Trained Machine Learning Model (aML-MD) with Multifidelity Data.

Machine learning (ML) provides a great opportunity for the construction of models with improved accuracy in classical molecular dynamics (MD). However, the accuracy of a ML trained model is limited by the quality and quantity of the training data. Generating large sets of accurate ab initio training data can require significant computational resources. Furthermore, inconsistent or incompatible data with different accuracies obtained using different methods may lead to biased or unreliable ML models that do not accurately represent the underlying physics. Recently, transfer learning showed its potential for avoiding these problems as well as for improving the accuracy, efficiency, and generalization of ML models using multifidelity data. In this work, ab initio trained ML-based MD (aML-MD) models are developed through transfer learning using DFT and multireference data from multiple sources with varying accuracy within the Deep Potential MD framework. The accuracy of the force field is demonstrated by calculating rate constants for the H + HO2 → H2 + 3O2 reaction using quasi-classical trajectories. We show that the aML-MD model with transfer learning can accurately predict the rate constants while reducing the computational cost by more than five times compared to the use of more expensive quantum chemistry training data sets. Hence, the aML-MD model with transfer learning shows great potential in using multifidelity data to reduce the computational cost involved in generating the training set for these potentials.