One-Pot Synthesis of CHA/ERI-Type Zeolite Intergrowth from a Single Multiselective Organic Structure-Directing Agent.

We report the one-pot synthesis of a chabazite (CHA)/erionite (ERI)-type zeolite intergrowth structure characterized by adjustable extents of intergrowth enrichment and Si/Al molar ratios. This method utilizes readily synthesizable 6-azaspiro[5.6]dodecan-6-ium as the exclusive organic structure-directing agent (OSDA) within a potassium-dominant environment. High-throughput simulations were used to accurately determine the templating energy and molecular shape, facilitating the selection of an optimally biselective OSDA from among thousands of prospective candidates. The coexistence of the crystal phases, forming a distinct structure comprising disk-like CHA regions bridged by ERI-rich pillars, was corroborated via rigorous powder X-ray diffraction and integrated differential-phase contrast scanning transmission electron microscopy (iDPC S/TEM) analyses. iDPC S/TEM imaging further revealed the presence of single offretite layers dispersed within the ERI phase. The ratio of crystal phases between CHA and ERI in this type of intergrowth could be varied systematically by changing both the OSDA/Si and K/Si ratios. Two intergrown zeolite samples with different Si/Al molar ratios were tested for the selective catalytic reduction (SCR) of NOx with NH3, showing competitive catalytic performance and hydrothermal stability compared to that of the industry-standard commercial NH3-SCR catalyst, Cu-SSZ-13, prevalent in automotive applications. Collectively, this work underscores the potential of our approach for the synthesis and optimization of adjustable intergrown zeolite structures, offering competitive alternatives for key industrial processes.

Aluminum alloy compositions and properties extracted from a corpus of scientific manuscripts and US patents.

Researchers continue to explore and develop aluminum alloys with new compositions and improved performance characteristics. An understanding of the current design space can help accelerate the discovery of new alloys. We present two datasets: 1) chemical composition, and 2) mechanical properties for predominantly wrought aluminum alloys. The first dataset contains 14,884 entries on aluminum alloy compositions extracted from academic literature and US patents using text processing techniques, including 550 wrought aluminum alloys which are already registered with the Aluminum Association. The second dataset contains 1,278 entries on mechanical properties for aluminum alloys, where each entry is associated with a particular wrought series designation, extracted from tables in academic literature.

Emission impacts of China's solid waste import ban and COVID-19 in the copper supply chain.

Climate change will increase the frequency and severity of supply chain disruptions and large-scale economic crises, also prompting environmentally protective local policies. Here we use econometric time series analysis, inventory-driven price formation, dynamic material flow analysis, and life cycle assessment to model each copper supply chain actor's response to China's solid waste import ban and the COVID-19 pandemic. We demonstrate that the economic changes associated with China's solid waste import ban increase primary refining within China, offsetting the environmental benefits of decreased copper scrap refining and generating a cumulative increase in CO2-equivalent emissions of up to 13 Mt by 2040. Increasing China's refined copper imports reverses this trend, decreasing CO2e emissions in China (up to 180 Mt by 2040) and globally (up to 20 Mt). We test sensitivity to supply chain disruptions using GDP, mining, and refining shocks associated with the COVID-19 pandemic, showing the results translate onto disruption effects.

Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks.

Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA-zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates.

Opportunities and challenges of text mining in aterials research.

Research publications are the major repository of scientific knowledge. However, their unstructured and highly heterogenous format creates a significant obstacle to large-scale analysis of the information contained within. Recent progress in natural language processing (NLP) has provided a variety of tools for high-quality information extraction from unstructured text. These tools are primarily trained on non-technical text and struggle to produce accurate results when applied to scientific text, involving specific technical terminology. During the last years, significant efforts in information retrieval have been made for biomedical and biochemical publications. For materials science, text mining (TM) methodology is still at the dawn of its development. In this review, we survey the recent progress in creating and applying TM and NLP approaches to materials science field. This review is directed at the broad class of researchers aiming to learn the fundamentals of TM as applied to the materials science publications.

Perspectives on Cobalt Supply through 2030 in the Face of Changing Demand.

Lithium-ion battery demand, particularly for electric vehicles, is projected to increase by over 300% throughout the next decade. With these expected increases in demand, cobalt (Co)-dependent technologies face the risk of significant impact from supply concentration and mining limitations in the short term. Increased extraction and secondary recovery form the basis of modeling scenarios that examine implications on Co supply to 2030. Demand for Co is estimated to range from 235 to 430 ktonnes in 2030. This upper bound on Co demand in 2030 corresponds to 280% of world refinery capacity in 2016. Supply from scheduled and unscheduled production as well as secondary production is estimated to range from 320 to 460 ktonnes. Our analysis suggests the following: (1) Co price will remain relatively stable in the short term, given that this range suggests even a supply surplus, (2) future Co supply will become more diversified geographically and mined more as a byproduct of nickel (Ni) over this period, and (3) for this demand to be met, attention should be paid to sustained investments in refined supply of Co and secondary recovery.

Strategies for improving the sustainability of structural metals.

Metallic materials have enabled technological progress over thousands of years. The accelerated demand for structural (that is, load-bearing) alloys in key sectors such as energy, construction, safety and transportation is resulting in predicted production growth rates of up to 200 per cent until 2050. Yet most of these materials require a lot of energy when extracted and manufactured and these processes emit large amounts of greenhouse gases and pollution. Here we review methods of improving the direct sustainability of structural metals, in areas including reduced-carbon-dioxide primary production, recycling, scrap-compatible alloy design, contaminant tolerance of alloys and improved alloy longevity. We discuss the effectiveness and technological readiness of individual measures and also show how novel structural materials enable improved energy efficiency through their reduced mass, higher thermal stability and better mechanical properties than currently available alloys.

A Methodology for Robust Comparative Life Cycle Assessments Incorporating Uncertainty.

We propose a methodology for conducting robust comparative life cycle assessments (LCA) by leveraging uncertainty. The method evaluates a broad range of the possible scenario space in a probabilistic fashion while simultaneously considering uncertainty in input data. The method is intended to ascertain which scenarios have a definitive environmentally preferable choice among the alternatives being compared and the significance of the differences given uncertainty in the parameters, which parameters have the most influence on this difference, and how we can identify the resolvable scenarios (where one alternative in the comparison has a clearly lower environmental impact). This is accomplished via an aggregated probabilistic scenario-aware analysis, followed by an assessment of which scenarios have resolvable alternatives. Decision-tree partitioning algorithms are used to isolate meaningful scenario groups. In instances where the alternatives cannot be resolved for scenarios of interest, influential parameters are identified using sensitivity analysis. If those parameters can be refined, the process can be iterated using the refined parameters. We also present definitions of uncertainty quantities that have not been applied in the field of LCA and approaches for characterizing uncertainty in those quantities. We then demonstrate the methodology through a case study of pavements.

Increasing secondary and renewable material use: a chance constrained modeling approach to manage feedstock quality variation.

The increased use of secondary (i.e., recycled) and renewable resources will likely be key toward achieving sustainable materials use. Unfortunately, these strategies share a common barrier to economical implementation - increased quality variation compared to their primary and synthetic counterparts. Current deterministic process-planning models overestimate the economic impact of this increased variation. This paper shows that for a range of industries from biomaterials to inorganics, managing variation through a chance-constrained (CC) model enables increased use of such variable raw materials, or heterogeneous feedstocks (hF), over conventional, deterministic models. An abstract, analytical model and a quantitative model applied to an industrial case of aluminum recycling were used to explore the limits and benefits of the CC formulation. The results indicate that the CC solution can reduce cost and increase potential hF use across a broad range of production conditions through raw materials diversification. These benefits increase where the hFs exhibit mean quality performance close to that of the more homogeneous feedstocks (often the primary and synthetic materials) or have large quality variability. In terms of operational context, the relative performance grows as intolerance for batch error increases and as the opportunity to diversify the raw material portfolio increases.

Anisotropic structure and transport in self-assembled layered polymer-clay nanocomposites.

Using the layer-by-layer (LbL) assembly technique, we create a polymer-clay structure from a unique combination of LbL materials: poly(ethylene imine), Laponite clay, and poly(ethylene oxide). This trilayer LbL structure is assembled using a combination of hydrogen bonding and electrostatic interactions. The films were characterized using ellipsometry, profilometry, X-ray photon spectroscopy, atomic force microscopy, scanning electron microscopy, wide-angle X-ray diffraction, grazing-incidence small-angle X-ray scattering, and electrochemical impedance spectroscopy (EIS). We observe a layered, anisotropic structure, which resulted in in-plane ion transport 100 times faster than cross-plane at 0% relative humidity. This study represents a first application of EIS in determining anisotropic ion transport in LbL assemblies and its correlation to structural anisotropy.

Chain Conformations at the Surface of a Polydisperse Amphiphilic Comb Copolymer Film.

Comb copolymers comprising a poly(methyl methacrylate) (PMMA) backbone and short, polyethylene oxide (PEO) side chains, PMMA-g-PEO, have been proposed to self-organize at the polymer/water interface, resulting in the quasi-2D confinement of the backbone for chains at the immediate surface of PMMA-g-PEO films (D.J. Irvine et al., Biomacromolecules2001, 2, 85-94). To directly probe such 2D conformations, combs modified with maleimide groups on the PEO chain ends were blended at 0.5-10 wt% into unmodified PMMA-g-PEO (M(n) 142 kg/mol, PDI 3.2, 32 wt% PEO) and cast into films ∼35 nm thick. Films were immersed in aqueous solution to induce orientation of surface molecules, and maleimide-functionalized chains at the film/water interface were labeled with 1.4 nm dia. Au nanoparticles. Transmission electron microscopy (TEM) was then used to trace the 2D trajectories of nanoparticle-decorated chains. The distribution of observed chain lengths was in good agreement with that from gel permeation chromatography. The 2D radius of gyration (R(g)) calculated from the observed conformations scaled with number of backbone segments (N) as R(g)∼N(0.69±0.02). Monte Carlo simulations of a 2D melt of comparable chain length distribution yielded a scaling exponent ν=0.67±0.03, suggesting that the deviation from 2D melt behavior arose from polydispersity.