What to Do about Plastics? Lessons from a Study of United Kingdom Plastics Flows.

Plastics are one of the most widely used materials on the planet, owing to their usefulness, durability, and relatively low cost. Yet, making, using, and disposing of plastics create important environmental impacts, most notably greenhouse gas emissions and waste pollution. Reducing these impacts while still enjoying the benefits of plastic use requires an integrated assessment of all of the life cycles of plastics. This has rarely been attempted due to the wide variety of polymers and the lack of knowledge on the final uses and applications of plastics. Using trade statistics for 464 product codes, we have mapped the flows of the 11 most widely used polymers from production into six end-use applications for the United Kingdom (UK) in 2017. With a dynamic material flow analysis, we have anticipated demand and waste generation until 2050. We found that the demand for plastics seems to have saturated in the UK, with an annual demand of 6 Mt, responsible for approximately 26 Mt CO2e/a. Owing to a limited recycling capacity in the UK, only 12% of UK plastic waste is recycled domestically, leading to 21% of the waste being exported, labeled as recycling, but mostly to countries with poor practices of waste management. Increasing recycling capacity in the UK could both reduce GHG emissions and prevent waste pollution. This intervention should be complemented with improved practices in the production of primary plastics, which currently accounts for 80% of UK plastic emissions.

Continuous synthesis of hollow silver-palladium nanoparticles for catalytic applications.

Hollow bimetallic nanoparticles exhibit unique surface plasmonic properties, enhanced catalytic activities and high photo-thermal conversion efficiencies amongst other properties, however, their research and further deployment are currently limited by their complicated multi-step syntheses. This paper presents a novel approach for their continuous synthesis with controllable and tuneable sizes and compositions. This robust manufacturing tool, consisting of coiled flow inverter (CFI) reactors connected in series, allows for the first time the temporal and spatial separation of the initial formation of silver seeds and their subsequent galvanic displacement reaction in the presence of a palladium precursor, leading to the full control of both steps separately. We have also demonstrated that coupling the galvanic replacement and co-reduction leads to a great kinetic enhancement of the system leading to a high yield process of hollow bimetallic nanoparticles, directly applicable to other metal combinations.

Greenhouse gas emissions from nitrogen fertilizers could be reduced by up to one-fifth of current levels by 2050 with combined interventions.

Food security relies on nitrogen fertilizers, but its production and use account for approximately 5% of global greenhouse gas (GHG) emissions. Meeting climate change targets requires the identification and prioritization of interventions across the whole life cycle of fertilizers. Here we have mapped the global flows of synthetic nitrogen fertilizers and manure and their corresponding GHG emissions across their life cycle. We have then explored the maximum mitigation potential of various interventions to reduce emissions by 2050. We found that approximately two-thirds of fertilizer emissions take place after their deployment in croplands. Increasing nitrogen-use efficiency is the single most effective strategy to reduce emissions. Yet this should be combined with decarbonization of fertilizer production. Using currently available technologies, GHG emissions of fertilizers could be reduced up to approximately one-fifth of current levels by 2050.

Force field-inspired transformer network assisted crystal density prediction for energetic materials.

Machine learning has great potential in predicting chemical information with greater precision than traditional methods. Graph neural networks (GNNs) have become increasingly popular in recent years, as they can automatically learn the features of the molecule from the graph, significantly reducing the time needed to find and build molecular descriptors. However, the application of machine learning to energetic materials property prediction is still in the initial stage due to insufficient data. In this work, we first curated a dataset of 12,072 compounds containing CHON elements, which are traditionally regarded as main composition elements of energetic materials, from the Cambridge Structural Database, then we implemented a refinement to our force field-inspired neural network (FFiNet), through the adoption of a Transformer encoder, resulting in force field-inspired Transformer network (FFiTrNet). After the improvement, our model outperforms other machine learning-based and GNNs-based models and shows its powerful predictive capabilities especially for high-density materials. Our model also shows its capability in predicting the crystal density of potential energetic materials dataset (i.e. Huang & Massa dataset), which will be helpful in practical high-throughput screening of energetic materials.

Mechanistic insights of the reduction of gold salts in the Turkevich protocol.

This paper presents fundamental understanding of the mechanism of the Turkevich protocol, the method recommended by the National Institute of Standards and Technology for the synthesis of gold nanoparticles using sodium citrate as reducing agent. Herein, we reveal that the Turkevich mechanism consists of two consecutive reduction steps (Au3+→ Au+→ Au0) rather than a reduction followed by the disproportionation reaction as conventionally believed. This new understanding has profound implications: i. the second reduction step (Au+→ Au0), rather than the previously postulated first reduction step, is the rate-limiting reduction step and ii. the formation of acetone dicarboxylate (DC2-) as an intermediate product through the oxidation of citrate has a key role as stabilizer and as a reducing agent (stronger than sodium citrate). This knowledge enables the synthesis of monodispersed gold nanoparticles with sizes ranging from 5.2 ± 1.7 nm to 21.4 ± 3.4 nm, with the lower end considerably smaller than previously reported through the Turkevich route. This work provides fundamental guidance for the controllable synthesis of nanoparticles using DC2- as a reducing agent directly applicable to other precious metals.