Solvent-Templated Methylammonium-Based Ruddlesden-Popper Perovskites with Short Interlayer Distances.

Two-dimensional (2D) halide perovskites are exquisite semiconductors with great structural tunability. They can incorporate a rich variety of organic species that not only template their layered structures but also add new functionalities to their optoelectronic characteristics. Here, we present a series of new methylammonium (CH3NH3+ or MA)-based 2D Ruddlesden-Popper perovskites templated by dimethyl carbonate (CH3OCOOCH3 or DMC) solvent molecules. We report the synthesis, detailed structural analysis, and characterization of four new compounds: MA2(DMC)PbI4 (n = 1), MA3(DMC)Pb2I7 (n = 2), MA4(DMC)Pb3I10 (n = 3), and MA3(DMC)Pb2Br7 (n = 2). Notably, these compounds represent unique structures with MA as the sole organic cation both within and between the perovskite sheets, while DMC molecules occupy a tight space between the MA cations in the interlayer. They form hydrogen-bonded [MA···DMC···MA]2+ complexes that act as spacers, preventing the perovskite sheets from condensing into each other. We report one of the shortest interlayer distances (∼5.7-5.9 Å) in solvent-incorporated 2D halide perovskites. Furthermore, the synthesized crystals exhibit similar optical characteristics to other 2D perovskite systems, including narrow photoluminescence (PL) signals. The density functional theory (DFT) calculations confirm their direct-band-gap nature. Meanwhile, the phase stability of these systems was found to correlate with the H-bond distances and their strengths, decreasing in the order MA3(DMC)Pb2I7 > MA4(DMC)Pb3I10 > MA2(DMC)PbI4 ∼ MA3(DMC)Pb2Br7. The relatively loosely bound nature of DMC molecules enables us to design a thermochromic cell that can withstand 25 cycles of switching between two colored states. This work exemplifies the unconventional role of the noncharged solvent molecule in templating the 2D perovskite structure.

Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization.

The skew and shape of the molecular weight distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we demonstrate a computer-controlled HTE platform that is able to run up to 8 unique variable conditions in parallel for the free radical polymerization of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time-dependent conversion and MWD, respectively. Using ML forward models, we first predict monomer conversion, intrinsically learning varying polymerization kinetics that change for each experimental condition. In addition, we predict entire MWDs including the skew and shape as well as SHAP analysis to interpret the dependence on reagent concentrations and reaction time. We then used a transfer learning approach to use the data from our high-throughput flow reactor to predict batch polymerization MWDs with only three additional data points. Overall, we demonstrate that the combination of HTE and ML provides a high level of predictive accuracy in determining polymerization outcomes. Transfer learning can allow exploration outside existing parameter spaces efficiently, providing polymer chemists with the ability to target the synthesis of polymers with desired properties.

Large enhancement of thermoelectric performance in MoS2/h-BN heterostructure due to vacancy-induced band hybridization.

Local impurity states arising from atomic vacancies in two-dimensional (2D) nanosheets are predicted to have a profound effect on charge transport due to resonant scattering and can be used to manipulate thermoelectric properties. However, the effects of these impurities are often masked by external fluctuations and turbostratic interfaces; therefore, it is challenging to probe the correlation between vacancy impurities and thermoelectric parameters experimentally. In this work, we demonstrate that n-type molybdenum disulfide (MoS2) supported on hexagonal boron nitride (h-BN) substrate reveals a large anomalous positive Seebeck coefficient with strong band hybridization. The presence of vacancies on MoS2 with a large conduction subband splitting of 50.0 ± 5.0 meV may contribute to Kondo insulator-like properties. Furthermore, by tuning the chemical potential, the thermoelectric power factor can be enhanced by up to two orders of magnitude to 50 mW m-1 K-2 Our work shows that defect engineering in 2D materials provides an effective strategy for controlling band structure and tuning thermoelectric transport.

Tuning Electronic Structure and Composition of FeNi Nanoalloys for Enhanced Oxygen Evolution Electrocatalysis via a General Synthesis Strategy.

Developing low-cost and efficient oxygen evolution electrocatalysts is key to decarbonization. A facile, surfactant-free, and gram-level biomass-assisted fast heating and cooling synthesis method is reported for synthesizing a series of carbon-encapsulated dense and uniform FeNi nanoalloys with a single-phase face-centered-cubic solid-solution crystalline structure and an average particle size of sub-5 nm. This method also enables precise control of both size and composition. Electrochemical measurements show that among Fex Ni(1- x ) nanoalloys, Fe0.5 Ni0.5 has the best performance. Density functional theory calculations support the experimental findings and reveal that the optimally positioned d-band center of O-covered Fe0.5 Ni0.5 renders a half-filled antibonding state, resulting in moderate binding energies of key reaction intermediates. By increasing the total metal content from 25 to 60 wt%, the 60% Fe0.5 Ni0.5 /40% C shows an extraordinarily low overpotential of 219 mV at 10 mA cm-2 with a small Tafel slope of 23.2 mV dec-1 for the oxygen evolution reaction, which are much lower than most other FeNi-based electrocatalysts and even the state-of-the-art RuO2 . It also shows robust durability in an alkaline environment for at least 50 h. The gram-level fast heating and cooling synthesis method is extendable to a wide range of binary, ternary, quaternary nanoalloys, as well as quinary and denary high-entropy-alloy nanoparticles.

Metastable 1T'-phase group VIB transition metal dichalcogenide crystals.

Metastable 1T'-phase transition metal dichalcogenides (1T'-TMDs) with semi-metallic natures have attracted increasing interest owing to their uniquely distorted structures and fascinating phase-dependent physicochemical properties. However, the synthesis of high-quality metastable 1T'-TMD crystals, especially for the group VIB TMDs, remains a challenge. Here, we report a general synthetic method for the large-scale preparation of metastable 1T'-phase group VIB TMDs, including WS2, WSe2, MoS2, MoSe2, WS2xSe2(1-x) and MoS2xSe2(1-x). We solve the crystal structures of 1T'-WS2, -WSe2, -MoS2 and -MoSe2 with single-crystal X-ray diffraction. The as-prepared 1T'-WS2 exhibits thickness-dependent intrinsic superconductivity, showing critical transition temperatures of 8.6 K for the thickness of 90.1 nm and 5.7 K for the single layer, which we attribute to the high intrinsic carrier concentration and the semi-metallic nature of 1T'-WS2. This synthesis method will allow a more systematic investigation of the intrinsic properties of metastable TMDs.

Poly(nickel-ethylenetetrathiolate) and Its Analogs: Theoretical Prediction of High-Performance Doping-Free Thermoelectric Polymers.

It is generally deemed that doping is a must for polymeric materials to achieve their high thermoelectric performance. We herein present the first report that intrinsically metallic behaviors and high-performance thermoelectric power factors can coexist within doping-free linear-backbone conducting polymers, poly(nickel-ethylenetetrathiolate) and its analogs. On the basis of density functional calculations, we have corroborated that four crystalline π- d conjugated transition-metal coordination polymers, including poly(Ni-C2S4), poly(Ni-C2Se4), poly(Pd-C2S4) and poly(Pt-C2S4) exhibit intrinsically metallic behavior arising from the formation of dense intermolecular interaction networks between sulfur/selenium atoms. They show moderate carrier concentrations (1019-1021 cm-3) and decent conductivities (103-104 S cm-1), among which, poly(Ni-C2S4), poly(Ni-C2Se4) and poly(Pd-C2S4) possess high power factors (∼103 µW m-1 K-2).

2D Black Phosphorus for Energy Storage and Thermoelectric Applications.

Recent progress in the currently available methods of producing black phosphorus bulk and phosphorene are presented. The effective passivation approaches toward improving the air stability of phosphorene are also discussed. Furthermore, the research efforts on the phosphorene and phosphorene-based materials for potential applications in lithium ion batteries, sodium ion batteries, and thermoelectric devices are summarized and highlighted. Finally, the outlook including challenges and opportunities in these research fields are discussed.

Second-Harmonic Generation from Sub-5 nm Gaps by Directed Self-Assembly of Nanoparticles onto Template-Stripped Gold Substrates.

Strong field enhancement and confinement in plasmonic nanostructures provide suitable conditions for nonlinear optics in ultracompact dimensions. Despite these enhancements, second-harmonic generation (SHG) is still inefficient due to the centrosymmetric crystal structure of the bulk metals used, e.g., Au and Ag. Taking advantage of symmetry breaking at the metal surface, one could greatly enhance SHG by engineering these metal surfaces in regions where the strong electric fields are localized. Here, we combine top-down lithography and bottom-up self-assembly to lodge single rows of 8 nm diameter Au nanoparticles into trenches in a Au film. The resultant "double gap" structures increase the surface-to-volume ratio of Au colocated with the strong fields in ∼2 nm gaps to fully exploit the surface SHG of Au. Compared to a densely packed arrangement of AuNPs on a smooth Au film, the double gaps enhance SHG emission by 4200-fold to achieve an effective second-order susceptibility χ((2)) of 6.1 pm/V, making it comparable with typical nonlinear crystals. This patterning approach also allows for the scalable fabrication of smooth gold surfaces with sub-5 nm gaps and presents opportunities for optical frequency up-conversion in applications that require extreme miniaturization.

Temperature-gated thermal rectifier for active heat flow control.

Active heat flow control is essential for broad applications of heating, cooling, and energy conversion. Like electronic devices developed for the control of electric power, it is very desirable to develop advanced all-thermal solid-state devices that actively control heat flow without consuming other forms of energy. Here we demonstrate temperature-gated thermal rectification using vanadium dioxide beams in which the environmental temperature actively modulates asymmetric heat flow. In this three terminal device, there are two switchable states, which can be regulated by global heating. In the "Rectifier" state, we observe up to 28% thermal rectification. In the "Resistor" state, the thermal rectification is significantly suppressed (<1%). To the best of our knowledge, this is the first demonstration of solid-state active-thermal devices with a large rectification in the Rectifier state. This temperature-gated rectifier can have substantial implications ranging from autonomous thermal management of heating and cooling systems to efficient thermal energy conversion and storage.

Experimentally validated inverse design of multi-property Fe-Co-Ni alloys.

This study presents a machine learning (ML) framework aimed at accelerating the discovery of multi-property optimized Fe-Ni-Co alloys, addressing the time-consuming, expensive, and inefficient nature of traditional methods of material discovery, development, and deployment. We compiled a detailed heterogeneous database of the magnetic, electrical, and mechanical properties of Fe-Co-Ni alloys, employing a novel ML-based imputation strategy to address gaps in property data. Leveraging this comprehensive database, we developed predictive ML models using tree-based and neural network approaches for optimizing multiple properties simultaneously. An inverse design strategy, utilizing multi-objective Bayesian optimization (MOBO), enabled the identification of promising alloy compositions. This approach was experimentally validated using high-throughput methodology, highlighting alloys such as Fe66.8Co28Ni5.2 and Fe61.9Co22.8Ni15.3, which demonstrated superior properties. The predicted properties data closely matched experimental data within 14% accuracy. Our approach can be extended to a broad range of materials systems to predict novel materials with an optimized set of properties.

Constructing custom thermodynamics using deep learning.

One of the most exciting applications of artificial intelligence is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications.

Closed-Loop Multi-Objective Optimization for Cu-Sb-S Photo-Electrocatalytic Materials' Discovery.

Copper antimony sulfides are regarded as promising catalysts for photo-electrochemical water splitting because of their earth abundance and broad light absorption. The unique photoactivity of copper antimony sulfides is dependent on their various crystalline structures and atomic compositions. Here, a closed-loop workflow is built, which explores Cu-Sb-S compositional space to optimize its photo-electrocatalytic hydrogen evolution from water, by integrating a high-throughput robotic platform, characterization techniques, and machine learning (ML) optimization workflow. The multi-objective optimization model discovers optimum experimental conditions after only nine cycles of integrated experiments-machine learning loop. Photocurrent testing at 0 V versus reversible hydrogen electrode (RHE) confirms the expected correlation between the materials' properties and photocurrent. An optimum photocurrent of -186 µA cm-2 is observed on Cu-Sb-S in the ratio of 9:45:46 in the form of single-layer coating on F-doped SnO2 (FTO) glass with a corresponding bandgap of 1.85 eV and 63.2% Cu1+ /Cu species content. The targeted intelligent search reveals a nonobvious CuSbS composition that exhibits 2.3 times greater activity than baseline results from random sampling.

Accelerated chemical science with AI.

In light of the pressing need for practical materials and molecular solutions to renewable energy and health problems, to name just two examples, one wonders how to accelerate research and development in the chemical sciences, so as to address the time it takes to bring materials from initial discovery to commercialization. Artificial intelligence (AI)-based techniques, in particular, are having a transformative and accelerating impact on many if not most, technological domains. To shed light on these questions, the authors and participants gathered in person for the ASLLA Symposium on the theme of 'Accelerated Chemical Science with AI' at Gangneung, Republic of Korea. We present the findings, ideas, comments, and often contentious opinions expressed during four panel discussions related to the respective general topics: 'Data', 'New applications', 'Machine learning algorithms', and 'Education'. All discussions were recorded, transcribed into text using Open AI's Whisper, and summarized using LG AI Research's EXAONE LLM, followed by revision by all authors. For the broader benefit of current researchers, educators in higher education, and academic bodies such as associations, publishers, librarians, and companies, we provide chemistry-specific recommendations and summarize the resulting conclusions.

Axially engineered metal-insulator phase transition by graded doping VO2 nanowires.

The abrupt first-order metal-insulator phase transition in single-crystal vanadium dioxide nanowires (NWs) is engineered to be a gradual transition by axially grading the doping level of tungsten. We also demonstrate the potential of these NWs for thermal sensing and actuation applications. At room temperature, the graded-doped NWs show metal phase on the tips and insulator phase near the center of the NW, and the metal phase grows progressively toward the center when the temperature rises. As such, each individual NW acts as a microthermometer that can be simply read out with an optical microscope. The NW resistance decreases gradually with the temperature rise, eventually reaching 2 orders of magnitude drop, in stark contrast to the abrupt resistance change in undoped VO2 wires. This novel phase transition yields an extremely high temperature coefficient of resistivity ~10%/K, simultaneously with a very low resistivity down to 0.001 Ω·cm, making these NWs promising infrared sensing materials for uncooled microbolometers. Lastly, they form bimorph thermal actuators that bend with an unusually high curvature, ~900 m(-1)·K(-1) over a wide temperature range (35-80 °C), significantly broadening the response temperature range of previous VO2 bimorph actuators. Given that the phase transition responds to a diverse range of stimuli-heat, electric current, strain, focused light, and electric field-the graded-doped NWs may find wide applications in thermo-opto-electro-mechanical sensing and energy conversion.

Quantifying surface roughness effects on phonon transport in silicon nanowires.

Although it has been qualitatively demonstrated that surface roughness can reduce the thermal conductivity of crystalline Si nanowires (SiNWs), the underlying reasons remain unknown and warrant quantitative studies and analysis. In this work, vapor-liquid-solid (VLS) grown SiNWs were controllably roughened and then thoroughly characterized with transmission electron microscopy to obtain detailed surface profiles. Once the roughness information (root-mean-square, σ, correlation length, L, and power spectra) was extracted from the surface profile of a specific SiNW, the thermal conductivity of the same SiNW was measured. The thermal conductivity correlated well with the power spectra of surface roughness, which varies as a power law in the 1-100 nm length scale range. These results suggest a new realm of phonon scattering from rough interfaces, which restricts phonon transport below the Casimir limit. Insights gained from this study can help develop a more concrete theoretical understanding of phonon-surface roughness interactions as well as aid the design of next generation thermoelectric devices.

Large thermoelectric figure-of-merits from SiGe nanowires by simultaneously measuring electrical and thermal transport properties.

The strongly correlated thermoelectric properties have been a major hurdle for high-performance thermoelectric energy conversion. One possible approach to avoid such correlation is to suppress phonon transport by scattering at the surface of confined nanowire structures. However, phonon characteristic lengths are broad in crystalline solids, which makes nanowires insufficient to fully suppress heat transport. Here, we employed Si-Ge alloy as well as nanowire structures to maximize the depletion of heat-carrying phonons. This results in a thermal conductivity as low as ∼1.2 W/m-K at 450 K, showing a large thermoelectric figure-of-merit (ZT) of ∼0.46 compared with those of SiGe bulks and even ZT over 2 at 800 K theoretically. All thermoelectric properties were "simultaneously" measured from the same nanowires to facilitate accurate ZT measurements. The surface-boundary scattering is prominent when the nanowire diameter is over ∼100 nm, whereas alloying plays a more important role in suppressing phonon transport for smaller ones.

Automated electrokinetic stretcher for manipulating nanomaterials.

In this work, we present an automated platform for trapping and stretching individual micro- and nanoscale objects in solution using electrokinetic forces. The platform can trap objects at the stagnation point of a planar elongational electrokinetic field for long time scales, as demonstrated by the trapping of <100 nm polystyrene beads and DNA molecules for minutes, with a standard deviation in displacement from the trap center <1 µm. This capability enables the stretching of deformable nanoscale objects in a high-throughput fashion, as illustrated by the stretching of more than 400 DNA molecules within ∼4 hours. The flexibility of the electrokinetic stretcher opens up numerous possibilities for complex manipulation, with sequential stretching of a molecule at different voltages and multiple stretch-relaxation cycles of the same molecule as examples. The platform described provides an automated, high-throughput method to track and manipulate objects for real-time studies of micro- and nanoscale systems.

Predicting Synthesizability using Machine Learning on Databases of Existing Inorganic Materials.

Defining the metric for synthesizability and predicting new compounds that can be experimentally realized in the realm of data-driven research is a pressing problem in contemporary materials science. The increasing computational power and advancements in machine learning (ML) algorithms provide a new avenue to solve the synthesizability challenge. In this work, using the Inorganic Crystal Structure Database (ICSD) and the Materials Project (MP) database, we represent crystal structures in Fourier-transformed crystal properties (FTCP) representation and use a deep learning model to predict synthesizability in the form of a synthesizability score (SC). Such an SC model, as a synthesizability filter for new materials, enables an efficient and accurate classification to identify promising material candidates. The SC prediction model achieved 82.6/80.6% (precision/recall) overall accuracy in predicting ternary crystal materials. We also trained the SC model by only considering compounds uploaded on the MP before 2015 as the training set and testing on multiple sets of materials uploaded after 2015. In the post-2019 test set, we obtain a high 88.60% true positive rate accuracy, coupled with 9.81% precision, indicating that newly added materials remain unexplored and have high synthesis potential. Further, we provide a list of 100 materials predicted to be synthesizable from this post-2019 dataset (highest SC) for future studies, and our SC model, as a validation filter, is beneficial for future material screening and discovery.

Ordering-Dependent Hydrogen Evolution and Oxygen Reduction Electrocatalysis of High-Entropy Intermetallic Pt4 FeCoCuNi.

Disordered solid-solution high-entropy alloys have attracted wide research attention as robust electrocatalysts. In comparison, ordered high-entropy intermetallics have been hardly explored and the effects of the degree of chemical ordering on catalytic activity remain unknown. In this study, a series of multicomponent intermetallic Pt4 FeCoCuNi nanoparticles with tunable ordering degrees is fabricated. The transformation mechanism of the multicomponent nanoparticles from disordered structure into ordered structure is revealed at the single-particle level, and it agrees with macroscopic analysis by selected-area electron diffraction and X-ray diffraction. The electrocatalytic performance of Pt4 FeCoCuNi nanoparticles correlates well with their crystal structure and electronic structure. It is found that increasing the degree of ordering promotes electrocatalytic performance. The highly ordered Pt4 FeCoCuNi achieves the highest mass activities toward both acidic oxygen reduction reaction (ORR) and alkaline hydrogen evolution reaction (HER) which are 18.9-fold and 5.6-fold higher than those of commercial Pt/C, respectively. The experiment also shows that this catalyst demonstrates better long-term stability than both partially ordered and disordered Pt4 FeCoCuNi as well as Pt/C when subject to both HER and ORR. This ordering-dependent structure-property relationship provides insight into the rational design of catalysts and stimulates the exploration of many other multicomponent intermetallic alloys.

Sub-10 nm Mixing and Alloying of Cu-Ag and Cu-Ni via Accelerated Solid Diffusion.

Development of nanoscale multicomponent solid inorganic materials is often hindered by slow solid diffusion kinetics and poor precursor mixing in conventional solid-state synthesis. These shortcomings can be alleviated by combining nanosized precursor mixtures and low temperature reaction, which could reduce crystal growth and accelerate the solid diffusion at the same time. However, high throughput production of nanoparticle mixtures with tunable composition via conventional synthesis is very challenging. In this work, we demonstrate that ∼10 nm homogeneous mixing of sub-10 nm nanoparticles can be achieved via spark nanomixing at room temperature and pressure. Kinetically driven Spark Plasma Discharge nanoparticle generation and ambient processing conditions limit particle coarsening and agglomeration, resulting in sub-10 nm primary particles of as-deposited films. The intimate mixing of these nanosized precursor particles enables intraparticle diffusion and formation of Cu/Ni nanoalloy during subsequent low temperature annealing at 100 °C. We also discovered that cross-particle diffusion is promoted during the low-temperature sulfurization of Cu/Ag which tends to phase-segregate, eventually leading to the growth of sulfide nanocrystals and improved homogeneity. High elemental homogeneity, small diffusion path lengths, and high diffusibility synergically contribute to faster diffusion kinetics of sub-10 nm nanoparticle mixtures. The combination of ∼10 nm homogeneous precursors via spark nanomixing, low-temperature annealing, and a wide range of potentially compatible materials makes our approach a good candidate as a general platform toward accelerated solid state synthesis of nanomaterials.