Tuning Catalyst Selectivity for Ammonia vs Hydrogen: An Investigation into the Coprecipitation of Mo and Fe Sulfides.

Iron sulfides are key materials in metalloprotein catalysis. One interesting aspect of iron sulfides in biology is the incorporation of secondary metals, for example, Mo, in nitrogenase. These secondary metals may provide vital clues as to how these enzymes first emerged in nature. In this work, we examined the materials resulting from the coprecipitation of molybdenum with iron sulfides using X-ray absorption spectroscopy (XAS). The materials were tested as catalysts, and direct reductants using nitrite (NO2-) and protons (H+) as test substrates. It was found that Mo will coprecipitate with iron as sulfides, however, in distinct ways depending on the stoichiometric ratios of Mo, Fe, and HS-. It was observed that the selectivity of reduction products depends on the amount of molybdenum, with the presence of approximately at 10% Mo optimizing ammonium/ammonia (NH4+/NH3) production from NO2- and minimizing competitive hydrogen (H2) formation from protons (H+) with a secondary reductant.

Anomalous Structural Evolution and Glassy Lattice in Mixed-Halide Hybrid Perovskites.

Hybrid halide perovskites have emerged as highly promising photovoltaic materials because of their exceptional optoelectronic properties, which are often optimized via compositional engineering like mixing halides. It is well established that hybrid perovskites undergo a series of structural phase transitions as temperature varies. In this work, the authors find that phase transitions are substantially suppressed in mixed-halide hybrid perovskite single crystals of MAPbI3-x Brx (MA = CH3 NH3 + and x = 1 or 2) using a complementary suite of diffraction and spectroscopic techniques. Furthermore, as a general behavior, multiple crystallographic phases coexist in mixed-halide perovskites over a wide temperature range, and a slightly distorted monoclinic phase, hitherto unreported for hybrid perovskites, is dominant at temperatures above 100 K. The anomalous structural evolution is correlated with the glassy behavior of organic cations and optical phonons in mixed-halide perovskites. This work demonstrates the complex interplay between composition engineering and lattice dynamics in hybrid perovskites, shedding new light on their unique properties.

Steric Interaction of Polyglycerol-Functionalized Detonation Nanodiamonds.

Detonation nanodiamonds have found numerous potential applications in a diverse array of fields such as biomedical imaging and drug delivery. Here, we systematically characterized non-functionalized and polyglycerol-functionalized detonation nanodiamond particles (DNPs) dispersed in aqueous suspensions at different ionic strengths (∼1.0 × 10-7 to 1.0 × 10-2 M) via dynamic light scattering and cryogenic transmission electron microscopy. For these colloidal suspensions, the total potential energies of interactions between a pair of DNPs were theoretically calculated using the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory plus the fitting of the Boltzmann distribution to the interparticle spacing distribution of the colloidal DNPs. These investigations revealed that the non-functionalized DNPs are dispersed in aqueous media through the long-range (>10 nm) and weak (<7 kBT) electrical double-layer repulsive interaction, while the driving force on dispersion of polyglycerol-functionalized DNPs is mostly derived from the short-range (<2 nm) and strong (∼55 kBT) steric repulsive potential barrier generated by the polyglycerol. Moreover, our results show that the truly monodispersed and individually dispersed DNP colloids, forming no aggregates in aqueous suspensions, are available by both functionalizing DNPs by polyglycerol and increasing ionic strength of suspending media to ≳1.0 × 10-2 M.

Solution phase growth and analysis of super-thin zigzag tin selenide nanoribbons.

Tin selenide (SnSe), a highly promising layered material, has been garnering particular interest in recent times due to its significant promise for future energy devices. Herein we report a simple solution-phase approach for growing highly crystalline layered SnSe nanoribbons. Polyvinylpyrrolidone (PVP) was used as a templating agent to selectively passivates the (100) and (001) facets of the SnSe nanoribbons resulting in the unique growth of nanoribbons along theirb-axis with a defined zigzag edge state along the sidewalls. The SnSe nanoribbons are few layers thick (∼20 layers), with mean widths of ∼40 nm, and achievable length of >1µm. Nanoribbons could be produced in relatively high quantities (>150 mg) in a single batch experiment. The PVP coating also offers some resistance to oxidation, with the removal of the PVP seen to lead to the formation of a SnSe/SnOxcore-shell structure. The use of non-toxic PVP to replace toxic amines that are typically employed for other 1D forms of SnSe is a significant advantage for sustainable and environmentally friendly applications. Heat transport properties of the SnSe nanoribbons, derived from power-dependent Raman spectroscopy, demonstrate the potential of SnSe nanoribbons as thermoelectric material.

Three-Dimensional Branched and Faceted Gold-Ruthenium Nanoparticles: Using Nanostructure to Improve Stability in Oxygen Evolution Electrocatalysis.

Achieving stability with highly active Ru nanoparticles for electrocatalysis is a major challenge for the oxygen evolution reaction. As improved stability of Ru catalysts has been shown for bulk surfaces with low-index facets, there is an opportunity to incorporate these stable facets into Ru nanoparticles. Now, a new solution synthesis is presented in which hexagonal close-packed structured Ru is grown on Au to form nanoparticles with 3D branches. Exposing low-index facets on these 3D branches creates stable reaction kinetics to achieve high activity and the highest stability observed for Ru nanoparticle oxygen evolution reaction catalysts. These design principles provide a synthetic strategy to achieve stable and active electrocatalysts.

Surface and Wetting Properties of Embiopteran (Webspinner) Nanofiber Silk.

Insects of the order Embioptera, known as embiopterans, embiids, or webspinners, weave silk fibers together into sheets to make shelters called galleries. In this study, we show that silk galleries produced by the embiopteran Antipaluria urichi exhibit a highly hydrophobic wetting state with high water adhesion macroscopically equivalent to the rose petal effect. Specifically, the silk sheets have advancing contact angles above 150°, but receding contact angle approaching 0°. The silk sheets consist of layered fiber bundles with single strands spaced by microscale gaps. Scanning and transmission electron microscopy (SEM, TEM) images of silk treated with organic solvent and gas chromatography mass spectrometry (GC-MS) of the organic extract support the presence of a lipid outer layer on the silk fibers. We use cryogenic SEM to demonstrate that water drops reside on only the first layer of the silk fibers. The area fraction of this sparse outer silk layers is 0.1 to 0.3, which according to the Cassie-Baxter equation yields an effective static contact angle of ∼130° even for a mildly hydrophobic lipid coating. Using high magnification optical imaging of the three phase contact line of a water droplet receding from the silk sheet, we show that the high adhesion of the drop stems from water pinning along bundles of multiple silk fibers. The bundles likely form when the drop contact line is pinned on individual fibers and pulls them together as it recedes. The dynamic reorganization of the silk sheets during the droplet movement leads to formation of "super-pinning sites" that give embiopteran silk one of the strongest adhesions to water of any natural hydrophobic surface.

Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds.

Morphologies of nanoparticles and aggregates play an important role in their properties for a range of applications. In particular, significant synthesis efforts have been directed toward controlling nanoparticle morphology and aggregation behavior in biomedical applications, as their size and shape have a significant impact on cellular uptake. Among several techniques for morphological characterization, transmission electron microscopy (TEM) can provide direct and accurate characterization of nanoparticle/aggregate morphology details. Nevertheless, manually analyzing a large number of TEM images is still a laborious process. Hence, there has been a surge of interest in employing machine learning methods to analyze nanoparticle size and shape. In order to achieve accurate nanoparticle analysis using machine learning methods, reliable and automated nanoparticle segmentation from TEM images is critical, especially when the nanoparticle image contrast is weak and the background is complex. These challenges are particularly pertinent in biomedical applications. In this work, we demonstrate an efficient, robust, and automated nanoparticle image segmentation method suitable for subsequent machine learning analysis. Our method is robust for noisy, low-electron-dose cryo-TEM images and for TEM cell images with complex, strong-contrast background features. Moreover, our method does not require any a priori training datasets, making it efficient and general. The ability to automatically, reliably, and efficiently segment nanoparticle/aggregate images is critical for advancing precise particle/aggregate control in biomedical applications.

On-demand activatable peroxidase-mimicking enzymatic polymer nanocomposite films.

Nanozymes continue to attract considerable attention to minimise the dependence on expensive enzymes in bioassays, particularly in medical diagnostics. While there has been considerable effort directed towards developing different nanozymes, there has been limited progress in fabricating composite materials based on such nanozymes. One of the biggest gaps in the field is the control, tuneability, and on-demand catalytic response. Herein, a nanocomposite nanozymatic film that enables precise tuning of catalytic activity through stretching is demonstrated. In a systematic study, we developed poly(styrene-stat-n-butyl acrylate)/iron oxide-embedded porous silica nanoparticle (FeSiNP) nanocomposite films with controlled, highly tuneable, and on-demand activatable peroxidase-like activity. The polymer/FeSiNP nanocomposite was designed to undergo film formation at ambient temperature yielding a highly flexible and stretchable film, responsible for enabling precise control over the peroxidase-like activity. The fabricated nanocomposite films exhibited a prolonged FeSiNP dose-dependent catalytic response. Interestingly, the optimised composite films with 10 wt% FeSiNP exhibited a drastic change in the enzymatic activity upon stretching, which provides the nanocomposite films with an on-demand performance activation characteristic. This is the first report showing control over the nanozyme activity using a nanocomposite film, which is expected to pave the way for further research in the field leading to the development of system-embedded activatable sensors for diagnostic, food spoilage, and environmental applications.

Shape control from thermodynamic growth conditions: the case of hcp ruthenium hourglass nanocrystals.

Recent successes in forming different shaped face centered cubic (fcc) metal nanostructures has enabled a greater understanding of nanocrystal growth mechanisms. Here we extend this understanding to the synthesis of hexagonally close packed (hcp) metal nanostructures, to form uniquely faceted ruthenium nanocrystals with a well-defined hourglass shape. The hourglass nanocrystals are formed in a three-step thermodynamic growth process with dodecylamine as the organic stabilizer. The hourglass nanocrystals are then shown to readily self-assemble to form a new type of nanocrystal superlattice.

Correlative Fluorescence and Transmission Electron Microscopy Assisted by 3D Machine Learning Reveals Thin Nanodiamonds Fluoresce Brighter.

Nitrogen vacancy (NV) centers in fluorescent nanodiamonds (FNDs) draw widespread attention as quantum sensors due to their room-temperature luminescence, exceptional photo- and chemical stability, and biocompatibility. For bioscience applications, NV centers in FNDs offer high-spatial-resolution capabilities that are unparalleled by other solid-state nanoparticle emitters. On the other hand, pursuits to further improve the optical properties of FNDs have reached a bottleneck, with intense debate in the literature over which of the many factors are most pertinent. Here, we describe how substantial progress can be achieved using a correlative transmission electron microscopy and photoluminescence (TEMPL) method that we have developed. TEMPL enables a precise correlative analysis of the fluorescence brightness, size, and shape of individual FND particles. Augmented with machine learning, TEMPL can be used to analyze a large, statistically meaningful number of particles. Our results reveal that FND fluorescence is strongly dependent on particle shape, specifically, that thin, flake-shaped particles are up to several times brighter and that fluorescence increases with decreasing particle sphericity. Our theoretical analysis shows that these observations are attributable to the constructive interference of light waves within the FNDs. Our findings have significant implications for state-of-the-art sensing applications, and they offer potential avenues for improving the sensitivity and resolution of quantum sensing devices.

Complex Dispersion of Detonation Nanodiamond Revealed by Machine Learning Assisted Cryo-TEM and Coarse-Grained Molecular Dynamics Simulations.

Understanding the polydispersity of nanoparticles is crucial for establishing the efficacy and safety of their role as drug delivery carriers in biomedical applications. Detonation nanodiamonds (DNDs), 3-5 nm diamond nanoparticles synthesized through detonation process, have attracted great interest for drug delivery due to their colloidal stability in water and their biocompatibility. More recent studies have challenged the consensus that DNDs are monodispersed after their fabrication, with their aggregate formation poorly understood. Here, we present a novel characterization method of combining machine learning with direct cryo-transmission electron microscopy imaging to characterize the unique colloidal behavior of DNDs. Together with small-angle X-ray scattering and mesoscale simulations we show and explain the clear differences in the aggregation behavior between positively and negatively charged DNDs. Our new method can be applied to other complex particle systems, which builds essential knowledge for the safe implementation of nanoparticles in drug delivery.

Ultrasmall Nanodiamonds: Perspectives and Questions.

Nanodiamonds are at the heart of a plethora of emerging applications in areas ranging from nanocomposites and tribology to nanomedicine and quantum sensing. The development of alternative synthesis methods, a better understanding, and the availability of ultrasmall nanodiamonds of less than 3 nm size with a precisely engineered composition, including the particle surface and atomic defects in the diamond crystal lattice, would mark a leap forward for many existing and future applications. Yet today, we are unable to accurately control nanodiamond composition at the atomic scale, nor can we reliably create and isolate particles in this size range. In this perspective, we discuss recent advances, challenges, and opportunities in the synthesis, characterization, and application of ultrasmall nanodiamonds. We particularly focus on the advantages of bottom-up synthesis of these particles and critically assess the physicochemical properties of ultrasmall nanodiamonds, which significantly differ from those of larger particles and bulk diamond.

Large Electrocaloric Effect in Nanostructure-Engineered (Bi, Na)TiO3-Based Thin Films.

Although the solid-state cooling technology based on electrocaloric response has been considered a promising refrigeration solution for microdevices, the mediocre dipolar entropy change ΔS impedes its practical applications. In this work, ΔS of a conventional ferroelectric thin film, namely, 0.94(Bi0.5Na0.5)TiO3-0.06BaTiO3 (BNBT), was greatly improved through engineering the nanodomain structures. The number of zero-field polar states and saturation polarization were greatly increased concomitant with a weakened strength of polar correlation in the thin films, owing to the local stabilization of strongly tetragonally distorted nanoclusters (tetragonality of ∼1.25) by modulating the growth conditions during the thin film deposition process. Consequently, a giant ΔS value of ∼ -48.5 J K-1 kg-1 (corresponding to ΔT = ∼27.3 K) and a wide window of operating temperature (>70 °C) were obtained near room temperature under a moderate electric field of 1330 kV cm-1. Moreover, our engineered BNBT thin film exhibits decent fatigue endurance; i.e., a substantial electrocaloric effect over a broad span of temperature can be sustained after 5 × 107 cyclic loading of the electric field. This work provides a universal design strategy for significantly improving the close-to-room-temperature electrocaloric performance of Bi-based ferroelectric thin films without the need of compositional or architectural complexity.

Ultrahigh Energy Storage Density in Glassy Ferroelectric Thin Films under Low Electric Field.

The current approach to achieving superior energy storage density in dielectrics is to increase their breakdown strength, which often incurs heat generation and unexpected insulation failures, greatly deteriorating the stability and lifetime of devices. Here, a strategy is proposed for enhancing recoverable energy storage density (Wr ) while maintaining a high energy storage efficiency (η) in glassy ferroelectrics by creating super tetragonal (super-T) nanostructures around morphotropic phase boundary (MPB) rather than exploiting the intensely strong electric fields. Accordingly, a giant Wr of ≈86 J cm-3 concomitant with a high η of ≈81% is acquired under a moderate electric field (1.7 MV cm-1 ) in thin films having MPB composition, namely, 0.94(Bi, Na)TiO3 -0.06BaTiO3 (BNBT), where the local super-T polar clusters (tetragonality ≈1.25) are stabilized by interphase strain. To the knowledge of the authors, the Wr of the engineered BNBT thin films represents a new record among all the oxide perovskites under a similar strength of electric field to date. The phase field simulation results ascertain that the improved Wr is attributed to the local strain heterogeneity and the large spontaneous polarization primarily is originated from the super-T polar clusters. The findings in this work present a genuine opportunity to develop ultrahigh-energy-density thin-film capacitors for low-electric-field-driven nano/microelectronics.

Regulating the Coordination Environment of Mesopore-Confined Single Atoms from Metalloprotein-MOFs for Highly Efficient Biocatalysis.

Single-atom catalysts (SACs) exhibit unparalleled atomic utilization and catalytic efficiency, yet it is challenging to modulate SACs with highly dispersed single-atoms, mesopores, and well-regulated coordination environment simultaneously and ultimately maximize their catalytic efficiency. Here, a generalized strategy to construct highly active ferric-centered SACs (Fe-SACs) is developed successfully via a biomineralization strategy that enables the homogeneous encapsulation of metalloproteins within metal-organic frameworks (MOFs) followed by pyrolysis. The results demonstrate that the constructed metalloprotein-MOF-templated Fe-SACs achieve up to 23-fold and 47-fold higher activity compared to those using metal ions as the single-atom source and those with large mesopores induced by Zn evaporation, respectively, as well as up to a 25-fold and 1900-fold higher catalytic efficiency compared to natural enzymes and natural-enzyme-immobilized MOFs. Furthermore, this strategy can be generalized to a variety of metal-containing metalloproteins and enzymes. The enhanced catalytic activity of Fe-SACs benefits from the highly dispersed atoms, mesopores, as well as the regulated coordination environment of single-atom active sites induced by metalloproteins. Furthermore, the developed Fe-SACs act as an excellent and effective therapeutic platform for suppressing tumor cell growth. This work advances the development of highly efficient SACs using metalloproteins-MOFs as a template with diverse biotechnological applications.

Microspectroscopic visualization of how biochar lifts the soil organic carbon ceiling.

The soil carbon (C) saturation concept suggests an upper limit to the storage of soil organic carbon (SOC). It is set by the mechanisms that protect soil organic matter from mineralization. Biochar has the capacity to protect new C, including rhizodeposits and microbial necromass. However, the decadal-scale mechanisms by which biochar influences the molecular diversity, spatial heterogeneity, and temporal changes in SOC persistence, remain unresolved. Here we show that the soil C storage ceiling of a Ferralsol under subtropical pasture was raised by a second application of Eucalyptus saligna biochar 8.2 years after the first application-the first application raised the soil C storage ceiling by 9.3 Mg new C ha-1 and the second application raised this by another 2.3 Mg new C ha-1. Linking direct visual evidence from one-, two-, and three-dimensional analyses with SOC quantification, we found high spatial heterogeneity of C functional groups that resulted in the retention of rhizodeposits and microbial necromass in microaggregates (53-250 µm) and the mineral fraction (<53 µm). Microbial C-use efficiency was concomitantly increased by lowering specific enzyme activities, contributing to the decreased mineralization of native SOC by 18%. We suggest that the SOC ceiling can be lifted using biochar in (sub)tropical grasslands globally.

Interfacing colloidal graphene oxide sheets with gold nanoparticles.

Aqueous solutions of graphene oxide (GO) and citrate-stabilised gold nanoparticles (AuNPs) are two classic, negatively charged colloids. Using the surface plasmon resonance spectra of AuNPs as a probe, we illustrate how the two like-charged colloids interact with each other and in so doing, reveal the unique solution behaviour of GO. We demonstrate that the electrical double layer of the GO sheets in water plays a key role in controlling the interaction between GO and AuNPs, which displays a one-way gate effect. It is shown that GO can capture and disperse AuNPs in water in a controllable fashion, without the need for additional chemical linkers. This discovery allows the successful synthesis of uncapped, yet solution-dispersible metal-nanoparticle assemblies. Such metal nanostructures have long been pursued for nano-plasmonics and sensing applications, but have remained difficult to prepare using conventional polymer dispersants. This work also makes clear that the combination of the two-dimensional conformation of GO along with its large molecular size and self-contained functional groups allows it to act as a unique soluble nanocarrier/substrate (the thinnest, functionalised flat substrate possible in nature) for the synthesis of new, soluble functional materials.

Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images.

The morphology of nanoparticles governs their properties for a range of important applications. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can provide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.

Metrology of convex-shaped nanoparticles via soft classification machine learning of TEM images.

The shape of nanoparticles is a key performance parameter for many applications, ranging from nanophotonics to nanomedicines. However, the unavoidable shape variations, which occur even in precision-controlled laboratory synthesis, can significantly impact on the interpretation and reproducibility of nanoparticle performance. Here we have developed an unsupervised, soft classification machine learning method to perform metrology of convex-shaped nanoparticles from transmission electron microscopy images. Unlike the existing methods, which are based on hard classification, soft classification provides significantly greater flexibility in being able to classify both distinct shapes, as well as non-distinct shapes where hard classification fails to provide meaningful results. We demonstrate the robustness of our method on a range of nanoparticle systems, from laboratory-scale to mass-produced synthesis. Our results establish that the method can provide quantitative, accurate, and meaningful metrology of nanoparticle ensembles, even for ensembles entailing a continuum of (possibly irregular) shapes. Such information is critical for achieving particle synthesis control, and, more importantly, for gaining deeper understanding of shape-dependent nanoscale phenomena. Lastly, we also present a method, which we coin the "binary DoG", which achieves significant progress on the challenging problem of identifying the shapes of aggregated nanoparticles.