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The formation of sparingly soluble ternary Na/Mg–Np( v )–borate(s) solid phases in alkaline, dilute-to-concentrated, NaCl and MgCl 2 solutions is confirmed by a multimethod experimental approach.
Structural, spectroscopic and theoretical analyses of mer-MBr3(OPcy3)3 (M = Am, Nd, Pr, Ce, La) reveal significant amounts of metal based p-orbital contribution.
This Letter describes the use of CdSe quantum dots (QDs) as photocatalysts for photoinduced electron transfer reversible addition–fragmentation chain transfer (PET-RAFT) polymerization of a series of aqueous acrylamides and acrylates. In this work, the high colloidal solubility and photostability of these QDs allowed polymerization to occur with high efficiency (>90% conversion in 2.5 h), low dispersity (PDI < 1.1), and ultralow catalyst loading (<0.5 ppm). The use of protein concentrators enabled the removal of the photocatalyst from the polymer and monomer with tolerable metal contamination (8.41 ug/g). These isolated QDs could be recycled for four separate polymerizations without a significant decrease in efficiency. By changing the pore size of the protein concentrators, the QDs and polymer could be separated from the remaining monomer, allowing for the synthesis of block copolymers using a single batch of QDs with minimal purification steps and demonstrating the fidelity of chain ends.
Organic photovoltaics incorporating non-fullerene acceptors based on perylenediimide (PDI) now rival fullerene acceptor-based devices in performance, although the mechanisms of charge generation in PDI-based devices are not yet fully understood. Fullerene-based systems are proposed to undergo electron transfer directly from the photoexcited donor into a band of delocalized acceptor states, thus increasing charge generation efficiency. Similarly, anion delocalization has been shown to enhance the rate of electron transfer from a photoexcited donor to two electronically coupled PDI acceptors. Here we investigate how additional electron acceptors may further increase the rate of electron transfer from the donor zinc meso-tetraphenylporphyrin (ZnTPP) to an aggregate of PDI acceptors (PDI[Formula: see text]. Femtosecond transient visible and mid-infrared absorption spectroscopies show that the rate of electron transfer from 1 *ZnTPP to the PDI assembly ZnTPP 2 -PDI 3 is statistically identical to that of the previously examined ZnTPP-PDI 2 . A Marcus theory analysis indicates that the parameters governing electron transfer are nearly identical for the two molecules, suggesting that the maximum electron transfer rate enhancement has been achieved in a cofacial PDI dimer because the ZnTPP directly couples to the first two PDI acceptors whereas the coupling to the third PDI is too weak.
Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. Furthermore, while multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.
In combining organometallic with organic precursors, molecular layer deposition (MLD) offers not only an expanded portfolio of molecular combinations but specifically the possibility of tuning mechanical properties for more robust functionality. This is appealing for applications in energy storage, where ion transport in and out of electrodes causes significant stress/strain cycling. It is particularly opportune for Li-ion solid state batteries (LISSBs), where electrode and solid electrolyte structures are usually arranged densely for high power and energy. Despite diverse MLD applications to date, little prior research has been aimed at Li-containing MLD materials and processes. Here, we report the MLD growth and process for a lithium-containing organic thin film using lithium tert-butoxide (LiO t Bu) and 1,3-propanediol, leading to an MLD film of lithium propane dioxide, Li 2 O 2 C 3 H 6 (LPDO), identified through X-ray photoelectron spectroscopy (XPS) and ab initio calculations. The growth showed self-limiting behavior for both precursors, with significant nucleation delay before linear growth at 0.23 Å/cycle at 150 °C, and 0.15 Å/cycle at 200 °C. XPS-determined stoichiometry was Li 1.6 O 2.2 C 3 H 6 at both 150 and 200 °C, while additional species, presumably from incomplete reaction, were found at 100 °C, leading to a notably higher (0.84 Å/cycle) growth rate. The LPDO film showed crystallinity and high surface roughness when grown on the crystalline substrate, while on the amorphous substrate, an amorphous LPDO film with low surface roughness was observed. In addition, high air sensitivity of LPDO film was observed, with Li propyl carbonate and Li carbonate formation under air exposure. Further modification strategies were proposed in order to achieve a MLD or atomic layer deposition-/MLD-based solid electrolyte material.
Additive manufacturing technologies have emerged as potential disruptive processes whose possible impacts range from supply chain logistics, prototyping, and novel materials synthesis. Numerous works illustrate the ability to control mi- crostructure in fusion based processes and a few recent authors have even produced single crystals. However, a number of open questions remain regarding the process window which enables printing of single crystals. Furthermore, it has been observed that these additively manufactured single crystals exhibit a preferred $\langle$011$\rangle$ secondary orientation. In this work we investigate fabrication conditions which enable printing of single crystals via electron beam melting. A space filling design of experiments is utilized to efficiently explore the fabrication space. Single crystals were success- fully obtained using both commercially available powders and custom melt alloys. Microstructures obtained via these exploratory experiments exhibited a continuum of columnar structures ranging from weakly textured polycrystals, near single crystal, and fully single crystalline material. Complex geometry experiments are performed to study the grain selection mechanism. We find that the grain selection mechanism is independent of the bulk scale geometry and must therefore by driven by local heat transfer and solidification dynamics. Furthermore, grain selection is shown to be driven by competing driving forces; one which prefers epitaxial growth and another which is driven by the imposed scan pattern geometry. Finally, a mechanism is proposed for the anomalous secondary orientation preference.
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Here, we investigated the growth and auto-oxidation of Pd deposited onto a AgO x single-layer on Ag(111) using scanning tunneling microscopy (STM) and X-ray photoelectron spectroscopy (XPS). Palladium initially grows as well-dispersed, single-layer clusters that adopt the same triangular shape and orientation of Ag n units in the underlying AgO x layer. Bi-layer clusters preferentially form upon increasing the Pd coverage to ~0.30 ML (monolayer) and continue to develop until aggregating and forming a nearly conformal Pd bi-layer at a coverage near 2 ML. Analysis of the STM images provides quantitative evidence of a transition from single to bi-layer Pd growth on the AgO x layer, and a continuation of bi-layer growth with increasing Pd coverage from ~0.3 to 2 ML. XPS further demonstrates that the AgO x layer efficiently transfers oxygen to Pd at 300 K, and that the fraction of Pd that oxidizes is approximately equal to the local oxygen coverage in the AgO x layer for Pd coverages up to at least ~0.7 ML. Our results show that oxygen in the initial AgO x layer mediates the growth and structural properties of Pd on the AgO x /Ag(111) surface, enabling the preparation of model PdAg surfaces with uniformly distributed single or bi-layer Pd clusters. Facile auto-oxidation of Pd by AgO x further suggests that oxygen transfer from Ag to Pd could play a role in promoting oxidation chemistry of adsorbed molecules on PdAg surfaces.
Adjacent oxygen adatoms and lattice iron atoms on magnetite surfaces serve as dual Lewis sites that oxidize methanol to formaldehyde.
This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter (WDM) physics. Originating from strongly coupled plasma physics, high pressure physics and high energy density science, the WDM physics community has recently taken a giant leap forward. This is due to spectacular developments in laser technology, diagnostic capabilities, and computer simulation techniques. Only in the last decade has it become possible to perform accurate enough simulations & experiments to truly verify theoretical results as well as to reliably design experiments based on predictions. Consequently, this roadmap discusses recent developments of and contemporary challenges for theoretical methods and experimental techniques needed to describe, create and diagnose WDM. A large part of this roadmap is dedicated to specific WDM systems and applications in astrophysics, inertial confinement fusion and novel material synthesis.
This roadmap presents the state-of-the-art, current challenges and near future developments anticipated in the thriving field of warm dense matter physics. Originating from strongly coupled plasma physics, high pressure physics and high energy density science, the warm dense matter physics community has recently taken a giant leap forward. This is due to spectacular developments in laser technology, diagnostic capabilities, and computer simulation techniques. Only in the last decade has it become possible to perform accurate enough simulations \& experiments to truly verify theoretical results as well as to reliably design experiments based on predictions. Consequently, this roadmap discusses recent developments and contemporary challenges that are faced by theoretical methods, and experimental techniques needed to create and diagnose warm dense matter. A large part of this roadmap is dedicated to specific warm dense matter systems and applications in astrophysics, inertial confinement fusion and novel material synthesis.
Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including robotics to automate materials synthesis and characterization, in conjunction with AI to interpret experimental outcomes and propose new experimental procedures. We focus on efforts to automate inorganic synthesis through solution-based routes, solid-state reactions, and thin film deposition. In each case, connections are made to relevant work in organic chemistry, where automation is more common. Characterization techniques are primarily discussed in the context of phase identification, as this task is critical to understand what products have formed during synthesis. The application of deep learning to analyze multivariate characterization data and perform phase identification is examined. To achieve “closed-loop” materials synthesis and design, we further provide a detailed overview of optimization algorithms that use active learning to rationally guide experimental iterations. Lastly, we highlight several key opportunities and challenges for the future development of self-driving inorganic materials synthesis platforms.