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This work discusses the imprinting of optoelectronic chirality on hybrid-organic inorganic perovskites, which are a new class of chiral nanomaterials for manipulation and control of spin polarized current and circularly polarized light.
Metal–organic nanotubes (MONTs) are porous, tunable 1D nanomaterials akin to metal–organic frameworks (MOFs). MONTs are synthesized via metal salts and coordinating ligands akin to MOFs, but crucially they are anisotropic, unlike most MOFs. Recently, MONTs have been shown to form statistically random copolymers; however, their mechanism of growth remains largely unexplored. Full realization of the potential of MONTs necessitates a thorough understanding of the mechanism of MONT growth. Herein, small-angle neutron scattering (SANS) was employed to investigate the copolymerization mechanism of two 1,2,4-ditriazole ligands and to quantify the inclusion of a solvent within the MONT pores. The results show parallelepiped-shaped structures are initially formed, which then aggregate to form larger lamellar structures. Additional experimentation with a deuterated ligand showed that the reactivities of all ligands are approximately equal, causing random ligand distribution within the resulting MONT. Finally, the results quantify the amount of solvent incorporated within the nanostructure pores at different stages of the formation process. These results show that early in the reaction the MONTs contain ca. 45% solvent, and they contain ca. 55% solvent late in the reaction when the MONTs are nearly fully formed.
Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.
The development of structural hierarchy on various length scales during the crystallization process is ubiquitous in biological systems and minerals and is common in synthetic nanomaterials. The driving forces for the formation of complex architectures range from local interfacial interactions, that modify interfacial speciation, local supersaturation, and nucleation barriers, to macroscopic interparticle forces. Although it is enticing to interpret the formation of hierarchical architectures as the assembly of independently nucleated building blocks, crystallization pathways often follow monomer-by-monomer addition with structural complexity arising from interfacial chemical coupling and strongly correlated fluctuation dynamics in the electric double layers. Here, we show that the development of structural hierarchy through heterogeneous nucleation is driven by dipolar and solvation forces. Specifically, coupled simulations and experimental studies revealed that dipole build-up along the slow growth direction can trigger twinning and the development of branched architectures. Enthalpic solvation interactions were shown to either enhance or reduce the dipole moment of the nanoparticles and, thereby, control crystal morphology and architecture. The systematic studies of chemical coupling between different solvents and undercoordinated surface atoms of the growing nanocrystals revealed the mechanism of dimensionality control and the development of structural hierarchy without ligands or structure-directing agents.
Microwave (MW) irradiation has emerged as a powerful tool for accelerating materials synthesis, yet the origins of its specific influence on reaction kinetics remain elusive. While multiple studies have attributed the observed enhancements in reaction rates under MW heating to reduced activation energies, other accounts have suggested modifications to the Arrhenius pre-exponential factor as the predominant cause. Distinguishing between these parameters in modern applications of MW processing in nanomaterials requires experimental approaches capable of resolving the dynamic and nuanced structural kinetics that govern MW-assisted chemistry. Here, we combine in-situ synchrotron X-ray total scattering with pair distribution function (PDF) analysis to track the structural evolution of SnO 2 nanoparticles synthesized via MW-assisted and conventional hydrothermal conditions. Avrami modeling and Arrhenius analysis suggest that although MW irradiation yields a higher apparent activation energy, the enhanced crystallization is better explained by a pre-exponential factor several orders of magnitude larger than that of conventional heating. These findings suggest that the MW field induces a higher frequency of successful molecular rearrangements rather than lowering the intrinsic activation barrier. The results contribute further insights clarifying the role of the applied MW field for materials design where MW-specific effects can be deliberately harnessed. Furthermore, this work presents a framework promoting the utility of in-situ PDF characterization coupled with kinetic analysis for developing more sophisticated descriptions of nanoscale transformations for MW-driven reaction kinetics.
Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS Experiment at Europe's Large Hadron Collider (LHC) at CERN (Switzerland) and the Belle II Experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL in the near term and enable the Lab to address the needs of the future experiments at the High-Luminosity LHC at CERN and the Electron-Ion Collider (EIC) at BNL in the long term. The Computing Facility Revitalization (CFR) project is aimed at repurposing the former National Synchrotron Light Source (NSLS-I) building as the new data center for BNL. The construction of the new data center was finished in 2021Q3, and it was delivered for production in early FY2022 for all collaborations supported by the Scientific Data and Computing Center (SDCC), including STAR, PHENIX and sPHENIX experiments at RHIC collider at BNL, the Belle II Experiment at KEK (Japan), and the Computational Science Initiative at BNL (CSI). This paper highlights the key mechanical, electrical, and networking components of the new data center in its final configuration as used in production since 2021Q4 and gives an overview for the extension of the central network systems into the new data center and the migration of a significant portion of IT load and services from the old data center to the new data center carried out in 20212023, with expected completion of the main phase of the gradual IT equipment replacement and migration from the old data center into the new one set to the end of FY2023 (Sep 30, 2023).
Strain-engineering of bimetallic nanomaterials is an important design strategy for developing new catalysts. In this paper, we introduce an approach for including strain effects into a recently introduced, density functional theory (DFT)-based alloy stability model. The model predicts adsorption site stabilities in nanoparticles and connects these site stabilities with catalytic reactivity and selectivity. Strain-based dependencies will increase the model’s accuracy for nanoparticles affected by finite-size effects. In addition to the stability of small nanoparticles, strain also influences the heat of adsorption of epitaxially grown metal-on-metal adlayers. In this respect, we successfully benchmark the strain-including alloy stability model with previous experimentally determined trends in the heats of adsorption of Au and Cu adlayers on Pt (111). For these systems, our model predicts stronger bimetallic interactions in the first monolayer than monometallic interactions in the second monolayer. We explicitly quantify the interplay between destabilizing strain effects and the energy gained by forming new metal–metal bonds. While tensile strain in the first Cu monolayer significantly destabilizes the adsorption strength, compressive strain in the first Au monolayer has a minimal impact on the heat of adsorption. Hence, this study introduces and, by comparison with previous experiments, validates an efficient DFT-based approach for strain-engineering the stability, and, in turn, the catalytic performance, of active sites in bimetallic alloys with atomic level resolution.
The energetics and dynamics of ion assembly in solution has broad influence in nanomaterials and inorganic synthesis. To investigate the fundamental processes involved, we present a time-resolved x-ray solution scattering (TR-XSS) study of the trinuclear silver and thallium complexes of the diplatinum ion PtPOP [Pt 2 (H 2 P 2 O 5 )$_4^{4−}$] in aqueous solution. These complexes, their structural properties, and their electronic structure are not well understood and afford a unique opportunity to study the metal–metal bond formation that influences molecular and material assembly in solution. We present model-independent analysis of the observed dynamics as well as an analysis incorporating time-resolved structural refinements of key bond lengths with $<$100 fs time resolution. We find that upon photoexcitation, the Pt atoms contract ∼0.25 Å toward the center of both the Ag- and the Tl-PtPOP complexes, as previously observed for the PtPOP anion. For the AgPtPOP system, an ultrafast Ag-Pt bond expansion of ∼0.2 Å is observed, whereas in contrast, the TlPtPOP system exhibits a Tl-Pt bond contraction of ∼0.3 Å upon photoexcitation. For both complexes, the change in electronic state leads to coherent (“wave-packet”) oscillations along the metal–Pt coordinates. Based on these structural dynamics, we propose an electronic structure model that describes the metal–metal bonding behavior in both the ground and excited state for both complexes.
Pulsed anodic arc discharge is a novel synthesis method of nanomaterials by means of low-temperature atmospheric plasma. The technique consists in periodically supplying DC power to two vertically aligned electrodes in the form of short unipolar pulses with peak currents of a few hundred Amperes in helium atmosphere. The pulsed arc plasmas are sustained at frequencies of the order of 1 Hz with around 10% of duty cycle. It constitutes a versatile technique thanks to a series of advantages compared to continuous DC arc processes, in particular: flexibility in the experimental conditions, process stability and repeatability, better utilization of ablating anode material, lower production of macroparticles, and lower thermal loads. Such features are discussed in this article. A brief overview concerning the recent accomplishments of pulsed arc discharge on deposition of carbon nanostructures (graphene and carbon nanotubes) and few-layer flakes of molybdenum disulphide (MoS 2 ) is provided, as well as an outlook on future applications of this method for the discovery of new materials with tailored functional properties.
The dynamics of nanoparticle growth in pulsed anodic arc discharge has been studied in time-resolved mode. To this end, a fast moving probe was employed to extract the material generated in a pulsed arc plasma held between two graphite electrodes. The probe motion was synchronized with the pulse phase and the exposure time to the plasma was set to 10 ms. The graphite anode was eroded in a helium atmosphere (300 Torr) by an arc plasma pulsed at 1 Hz with a 10% duty cycle and showing 250 A of peak current. The structure and morphology of the probe depositions were characterized by Raman spectroscopy, scanning electron microscopy, and focused ion beam. A maximal deposition rate of 260 μm/s was measured 5 mm away from the arc core during the active 0.1 s of the pulse. Such a rate yields a growth flux of 1.3 × 10 21 cm –2 s –1 , rich in carbon nanostructures (graphene platelets, nanotubes) with a characteristic aggregate size within 1–10 μm. The deposition during the inactive 0.9 s of the pulse was several orders of magnitude slower and consisted of amorphous carbon traces. Furthermore, the nanoparticle distribution along the collecting probe is correlated with the pulse phase, thereby providing information on particle transport. Pulsed nanosynthesis can be modeled as a periodical growth process, where the volume and propagation velocity of the growth region can be adjusted through modulation of the pulse signal waveform. The proposed model constitutes a suitable framework to investigate the pulsed arc synthesis of nanomaterials with tailored physical and chemical properties.
Graphite ablation by an electric arc or a laser/solar flux is widely used for the synthesis of carbon nanomaterials. Previously, it was observed in multiple arc experiments that the ablation rate is a strong nonlinear function of the arc current and it drastically increases at some threshold current. As such, we developed an analytical model explaining this transition in the rate of ablation by an electric arc or a laser/solar flux. The model not only explains the observations but can also accurately predict the experimentally observed ablation rates. The model takes into account redeposition of carbon back to the ablated surface, which is the key process responsible for the observed effects.
Neutron and x-ray scattering represent two classes of state-of-the-art materials characterization techniques that measure materials structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems from catalysts to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and x-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and x-ray techniques, including neutron scattering, x-ray absorption, x-ray scattering, and photoemission. We highlight the integration of machine learning methods into the typical workflow of scattering experiments, focusing on problems that challenge traditional analysis approaches but are addressable through machine learning, including leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials representations, mitigating spectral noise, and others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.
We report on the measurement of the electrical properties of individual pristine and doped BaTiO 3 nanocubes by using in situ transmission electron microscopy with a two-electrode configuration. The dimensions of the nanocubes tested are between 10 and 20 nm, which rendered their in situ electrical characterization challenging. We characterized 4%Cr and 5%La (atomic percentage) doped BaTiO 3 nanocubes and compared them with the properties of pristine BaTiO3 nanocubes synthesized by the same solvothermal method. We found that the resistance of all three types of nanocubes when displayed in log-scale shows a nearly linear dependence on the applied electric field (R2 ≥ 0.95) over a wide range of electric fields (50–900 kV/cm). Compared with pristine BaTiO 3 nanocubes, the resistance of both 4%Cr and 5%La doped nanocubes showed reduced variation rates with respect to the electric field, with 5%La doping, demonstrating a better reduction in the variation rate. By developing techniques capable of evaluating the properties of individual BaTiO 3 nanocubes, we expect that our work to open the door to the use of BaTiO 3 nanomaterials in the design of future multilayer ceramic capacitors with improved volumetric efficiency and ferroelectrics-enabled nanodevices with advanced functionality.
Single-walled carbon nanotubes (SWCNTs) are attractive materials for next-generation energy-harvesting technologies, including thermoelectric generators, due to their tunable opto-electronic properties and high charge carrier mobilities. Controlling the Fermi level within these unique 1D nanomaterials is often afforded by charge transfer interactions between SWCNTs and electron or hole accepting species. Conventional methods to dope SWCNT networks typically involve the diffusion of molecular redox dopant species into solid-state thin films, but solution-phase doping could potentially provide routes and/or benefits for charge carrier transport, scalability, and stability. Here, we develop a methodology for solution-phase doping of polymer-wrapped, highly enriched semiconducting SWCNTs using a p-type charge transfer dopant, F4TCNQ. This allows doped SWCNT inks to be cast into thin films without the need for additional post-deposition doping treatments. We demonstrate that the introduction of the dopant at varying stages of the SWCNT dispersion process impacts the ultimate thermoelectric performance and observe that the dopant alters the polymer selectivity for semiconducting vs metallic SWCNTs. In contrast to dense semiconducting polymer films, where solution-phase doping typically leads to disrupted morphologies and poorer TE performance than solid-state doping, thin films of solution-doped s-SWCNTs perform similarly to their solid-state doped counterparts. Interestingly, our results also suggest that solution-phase F4TCNQ doping leads to fully ionized and dimerized F4TCNQ anions in solid-state films that are not observed in films doped with F4TCNQ after deposition. Our results provide a framework for the application of solution-phase doping to a broad array of high-performance SWCNT-based thermoelectric materials and devices that may require high-throughput deposition techniques.
The prospect of creating ferroelectric or high permittivity nanomaterials provides motivation for investigating complex transition metal oxides of the form Ba(Ti, MV)O3, where M = Nb or Ta. Solid state processing typically produces mixtures of crystalline phases, rarely beyond minimally doped Nb/Ta. Using a modified sol-gel method, we prepared single phase nanocrystals of Ba(Ti, M)O3. Compositional and elemental analysis puts the empirical formulas close to BaTi0.5Nb0.5O3−δ and BaTi0.5Ta0.5O3−δ. For both materials, a reversible temperature dependent phase transition (non-centrosymmetric to symmetric) is observed in the Raman spectrum in the region 533–583 K (260–310 °C); for Ba(Ti, Nb)O3, the onset is at 543 K (270 °C); and for Ba(Ti, Ta)O3, the onset is at 533 K (260 °C), which are comparable with 390–393 K (117–120 °C) for bulk BaTiO3. The crystal structure was resolved by examination of the powder x-ray diffraction and atomic pair distribution function (PDF) analysis of synchrotron total scattering data. It was postulated whether the structure adopted at the nanoscale was single or double perovskite. Double perovskites (A2B′B″O6) are characterized by the type and extent of cation ordering, which gives rise to higher symmetry crystal structures. PDF analysis was used to examine all likely candidate structures and to look for evidence of higher symmetry. The feasible phase space that evolves includes the ordered double perovskite structure Ba2(Ti, MV)O6 (M = Nb, Ta) Fm-3m, a disordered cubic structure, as a suitable high temperature analog, Ba(Ti, MV)O3Pm-3m, and an orthorhombic Ba(Ti, MV)O3Amm2, a room temperature structure that presents an unusually high level of lattice displacement, possibly due to octahedral tilting, and indication of a highly polarized crystal.
Transient absorption (TA) spectroscopy is one of the most popular experimental methods to measure the excited state lifetimes and charge carrier recombination mechanisms in two dimensional (2D) semiconductors. This fundamental information is essential for designing and optimizing the next generation of ultrathin and lightweight 2D semiconductor-based optoelectronic devices. However, the interpretation of TA spectroscopy data varies across the community. The community lacks a unifying physical explanation for how and why experimental variables such as incident light intensity, sample-substrate interactions, and/or applied bias affect TA spectral data. This Perspective (1) compares the physical chemistry TA literature to nanomaterial physics literature from a historical perspective, (2) reviews multiple physical explanations that the TA community developed to explain spectral features and experimental trends, (3) provides a unifying explanation for how and why trions—and, more generally, Fermi polarons—contribute to TA spectra, and (4) quantifies the extent to which various physical interpretations and data analysis procedures yield different timescales and mechanisms for the same set of experimental results. We highlight the importance of considering trions/Fermi polarons in TA measurements and their implications for advancing our understanding of 2D material properties.
Monitoring of energy infrastructure through robust yet economical sensing platforms is becoming an area of increased importance, with ubiquitous applications including the electrical grid, natural gas and oil transportation pipelines, H2 infrastructure (storage and transportation), carbon storage, power generation, and subsurface environments. Plasmonic and functional nanomaterial enabled fiber optic sensors show excellent promise for a wide range of sensing applications due to their versatility to be engineered for specific analytes of interest while retaining inherent advantages of the optical fiber sensor platform. Through the design of novel sensing layers, the optical transduction mechanism and wavelength dependence can also be tailored for ease of integration with low-cost interrogation systems enabling an inexpensive yet highly functional optical fiber sensing platform. In addition, recent advances in artificial intelligence and machine learning theoretical methods have been leveraged to simultaneously extract multiple parameters through multi-wavelength interrogation such that unique wavelengths can also serve as unique sensing elements, analogous to electronic nose sensor technologies. The concept of an optical fiber based “photonic nose” via multiple interrogation wavelengths and/or sensor nodes offers a compelling platform technology to realize multiparameter speciation of chemical analytes within complex gas mixtures. In this Perspective, we further generalize the notion of multiparameter sensing through the novel “photonic nervous system” concept based upon low-cost, functionalized optical fiber sensor probes monitoring a variety of distinct analyte classes (physical, chemical, electromagnetic, etc.) simultaneously to provide broad situational awareness via integrated sensors.
Significance Lipid membrane fusion is a critical component of biological transport, communication, and developmental processes. The fusion reaction is kinetically constrained since it contains multiple large energy barriers, so proteins and ions are used by natural systems to intervene and catalyze the reaction toward biologically relevant timescales. Here, we demonstrate how amphiphilic nanoparticles can prime lipid membranes for fusion so that complete fusion proceeds only upon exposure to an excess of calcium or other stimulus that would stress the outer leaflet. This is a major advance in the nascent field of controlled fusion and may unlock new avenues of research on how nanomaterials can be designed from first principles to perform targeted functional roles in biological systems.