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DOE OSTI2026

Theory Guided Fine‐Tune of Strain Effects in Pt Ternary Alloy via Rare Earth Templating: Achieving High Performance PEMFCs Catalysts

The sluggish kinetics and insufficient durability of platinum-based catalysts remain crucial barriers limiting proton-exchange-membrane fuel cells (PEMFCs) deployment. Here, we report a theory-guided synthesis combined with rare-earth templating to realize a previously inaccessible Pt 5 Co-like phase with tailored atomic-scale strain. Guided by density functional theory (DFT) calculations, we identified that a Pt 5 Co-like sublayer can induce a unique mild compressive strain (−1.24%) to the Pt(111) shell and an optimal *OH binding energy shift (ΔE ≈ 0.11 eV). This shift positions the alloy catalyst near the apex of the oxygen reduction reaction activity volcano. This prediction guided the synthesis of ternary alloy Pt 5 (Ce)Co@Pt multilayer nanoparticles, featuring a Ce-stabilized core, a Pt 5 Co-like sublayer, and a Pt-rich shell. This catalyst demonstrates both exceptionally high activity and durability, achieving a mass activity of 2.6 A∙mg Pt −1 in rotating disk electrode testing. In fuel cell membrane electrode assembly tests, Pt 5 (Ce)Co@Pt achieves a current density of 1.9 A∙cm −2 at 0.7 V under heavy-duty vehicle conditions. Remarkably, it maintains 1.2 A∙cm −2 after 1 80 000 AST cycles, doubling the U.S. DOE 2025 target. This work demonstrates a rational design strategy that DFT-guided strain engineering integrates with rare-earth templating to advance Pt-based catalysts for fuel cell applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Tuning Catalytic Reactivity via Wetting Control through Oxygen Vacancies: Ru Clusters on Anatase TiO 2 and CeO 2 Supports

The shape of supported metal particles regulates their catalytic reactivity and is determined by the degree of wetting between the metal particle and the support surface. Flattened particles that wet support surfaces were reported in various catalytic systems, particularly in the subnanometer size regime. Such consequential metal–support wetting phenomena are poorly understood, and methods to study them on powder catalysts under realistic conditions are lacking. Here, we investigate the size-dependent wetting behaviors of Ru particles on two reducible-oxide supports, anatase TiO 2 (TiO 2 -A) and CeO 2 , under reducing catalytic conditions. X-ray absorption spectroscopy (XAS), low-energy ion scattering (LEIS), and density functional theory (DFT) are combined to determine the shape of Ru particles. Ru particles remain three-dimensional without wetting the TiO 2 -A support within the coverage range studied (0.06–0.98 Ru nm –2 ). In contrast, at low coverages (<0.25 Ru nm –2 ), Ru wets the CeO 2 support to form flat, disordered structures. The higher wettability of CeO 2 than TiO 2 -A is attributed to oxygen vacancies in the near-surface region. The shape difference between small Ru particles or clusters on the two supports leads to drastically contrasting catalytic reactivities in polyolefin hydrogenolysis, despite similar diameters. As a result, this work highlights the implications of metal–support wetting, or cluster shape, on catalytic behaviors of small metal clusters, while establishing the foundation for future systematic studies of such a phenomenon in realistic systems, by delivering a multitechnique methodology and revealing governing fundamental principles.

42 ENGINEERING↗

DOE OSTI2025

The influence of cooling rates on strain phase diagrams and domain structures of ferroelectric thin films: A case study of PbTiO 3

Strain engineering has been established as an effective approach to control phase equilibria, domain configurations, and functional properties of ferroelectric thin films. Temperature-strain phase diagrams have been used as powerful tools for providing insights into strain engineering. However, almost all existing phase diagrams established using the phase-field approach assume quenching conditions without considering actual cooling rates during the post-deposition annealing process of ferroelectric thin films. Within this work, we systematically investigate the influence of cooling rates on domain structures and the strain-phase diagram of ferroelectric thin films using phase-field simulations, taking PbTiO 3 thin films as a model system. We found that both the position of phase boundaries in the strain phase diagrams and the domain morphology are significantly influenced by the cooling rates. It is revealed that while the paraelectric-ferroelectric phase boundary remains invariant, the phase boundaries between single-phase and multi-phase regions tend to shift toward the corresponding multi-phase region as the cool rate reduces. Slow cooling generally leads to more ordered domain structures with increased domain size. Using the obtained equilibrium domain structures, we calculated effective thermal conductivities and found significant variations that can be tuned by the cooling rates. In conclusion, this work reveals an underexplored yet critical impact of cooling rates on phase equilibria and domain structures in ferroelectric thin films, which may inspire further fine-tuning of domains and domain walls in low-dimensional ferroelectrics for multifunctional applications.

Domain structures↗

DOE OSTI2021

Organic Modifiers Promote Furfuryl Alcohol Ring Hydrogenation via Surface Hydrogen-Bonding Interactions

Interactions between surface adsorbed species can affect catalyst reactivity, and thus, the ability to tune these interactions is of considerable importance. Deposition of organic modifiers provides one method of intentionally introducing controllable surface interactions onto catalyst surfaces. In this study, Pd/Al2O 3 catalysts were modified with either thiol or phosphonic acid (PA) ligands and tested in the hydrogenation of furanic species. The thiol modifiers were found to inhibit ring hydrogenation (RH) activity, with the degree of inhibition trending with the thiol surface coverage. This suggests that thiols do not strongly interact with the reactants and simply serve to block active sites on the Pd surface. PAs, on the other hand, were found to enhance RH when furfuryl alcohol (FA) was used as the reactant. Density functional theory calculations suggested that this enhancement was due to hydrogen-bonding interactions between FA-derived surface intermediates and PA modifiers. Here, installation of hydrogen-bonding groups on the Pd surface served to preferentially stabilize RH product states. Furthermore, the promotional effect on the RH of FA was observed to be greater when a higher-coverage PA was used, providing a rate more than twice that of the unmodified Pd/Al2O 3 . The results of this work suggest that organic ligands can be designed to impart tunable surface interactions on heterogeneous catalysts, providing an additional method of controlling catalytic performance.

09 BIOMASS FUELS↗

DOE OSTI2020

The influence of Au substitution and hydrostatic pressure on the phase transitions and magnetocaloric properties of MnCoGe alloys

In this work, the phase transitions and magnetocaloric properties of Mn 1--x Au x CoGe (0≤x≤0.025) alloys were studied as a function of concentration x and applied hydrostatic pressure. The increasing substitution of Au for Mn results in the decrease of the first-order martensitic transition temperature, and this first-order martensitic transition was ultimately converted to a second-order magnetic transition when the Au substitution (x) reached 0.025. The magnitudes of the maximum magnetic entropy changes increased when the magnetic and structural transitions were coupled, which occurred for 0.005≤x≤0.020. The largest maximum magnetic entropy change for a field change of μ 0 ΔH=7 T was 33.1J/kgK for the sample with x=0.020. Similar to the effect of Au substitution, the first-order martensitic transition temperature initially decreased, and then converted to second order, when the applied hydrostatic pressure reached a large enough value. Interestingly, both Au substitution and pressure application cause a volume reduction and, in both cases, the first-order martensitic transition temperature initially reduced and then converted to second-order. These results suggest two different methods of tuning the transition temperatures in these magnetocaloric materials. One can either apply hydrostatic pressure and temporarily adjust the transition temperatures or modify the composition chemically and permanently change the transition temperatures.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Tuning hydrogenation chemistry of Pd-based heterogeneous catalysts by introducing homogeneous-like ligands

Abstract Noble metals have been extensively employed in a variety of hydrotreating catalyst systems for their featured functionality of hydrogen activation but may also bring side reactions such as undesired deep hydrogenation. It is crucial to develop a viable approach to selectively inhibit side reactions while preserving beneficial functionalities. Herein, we present modifying Pd with alkenyl-type ligands that forms homogeneous-like Pd-alkene metallacycle structure on the heterogeneous Pd catalyst to achieve the selective hydrogenolysis and hydrogenation. Particularly, a doped alkenyl-type carbon ligand on Pd-Fe catalyst is demonstrated to donate electrons to Pd, creating an electron-rich environment that elongates the distance and weakens the electronic interaction between Pd and unsaturated C of the reactants/products to control the hydrogenation chemistry. Moreover, high H 2 activation capability is maintained over Pd and the activated H is transferred to Fe to facilitate C-O bond cleavage or directly participate in the reaction on Pd. The modified Pd-Fe catalyst displays comparable C-O bond cleavage rate but much higher selectivity (>90%) than the bare Pd-Fe (<50%) in hydrotreating of diphenyl ether (DPE, modelling the strongest C-O linkage in lignin) and enhanced ethene selectivity (>90%) in acetylene hydrogenation. This work sheds light on the controlled synthesis of selective hydrotreating catalysts via mimicking homogeneous analogues.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Ionic Tuning of Cobaltites at the Nanoscale

Control of materials through custom design of ionic distributions is one of the most promising approaches for developing future technologies ranging from spintronic logic and memory devices to energy storage. Perovskites have shown particular promise for ionic-devices due to their high ion mobility and sensitivity to chemical stoichiometry. In this work, we demonstrate control of ion distributions in (La,Sr)CoO3. Commensurate with control of the oxygen stoichiometry, the Co valence and saturation magnetization show a smooth continuous variation. In contrast, magnetoresistance measurements show a sharp, step-wise transition in the magnetic anisotropy and resistivity over the same range of oxygen stoichiometry. These results suggest significant phase separation, with conductive magnetic regions and oxygen-deficient, insulating, non-magnetic regions, forming percolated networks. Indeed, X-ray diffraction identifies oxygen vacancy ordering, including the emergence of Brownmillerite ordering. The unusual appearance of the Brownmillerite phase under ambient temperature is further confirmed by high resolution scanning transmission electron microscopy. This work demonstrates room-temperature ionic control of magnetism, electrical conductivity and crystalline structure, in a 40 nm film, presenting new opportunities for ionic devices that leverage multiple material properties.

thin films, magnetism, xmcd, pnr, STEM, defects↗

DOE OSTI2025

Facet-dependent structure and dissociation of water at pristine IrO 2 /water interfaces

Understanding the microscopic structure of water at metal oxide interfaces is crucial for advancing electrocatalysis. IrO 2 , specifically, has shown exceptional activity for electrochemical water oxidation, but we currently lack a fundamental understanding of how the surface structure of IrO 2 impacts water reactivity. In this work, we developed a machine learning potential trained to first-principles accuracy for modeling IrO 2 /water interfaces across different facets: (110), (100), (101), and (001). Using extensive machine learning molecular dynamics simulations, we investigated the spontaneous dissociation of water molecules at these interfaces. Our results reveal a distinct dissociation probability trend: (110) > (100) ≈ (101) > (001), which we attribute primarily to the reaction thermodynamics of surface water dissociation. A strong correlation is observed between the surface Ir–O bond distances and the dissociation probabilities, highlighting the role of surface geometry in modulating reactivity. As a consequence, the interfacial solvation structures and hydrogen bonding environments are dynamically tuned by the varying water dissociation capabilities across facets. This work elucidates how water dissociation energetics depend on surface orientation and interfacial structure, offering atomistic insights into manipulating reaction chemistry at electrocatalytic interfaces.

organic↗

DOE OSTI2019

Local structure of potassium doped nickel oxide: A combined experimental-theoretical study

The electronic structure of Mott and charge-transfer insulators can be tuned through charge doping to achieve a variety of fascinating physical properties, e.g., superconductivity, colossal magnetoresistance, and metal-to-insulator transitions. Strong correlations between d electrons give rise to these properties but they are also the reason why they are inherently difficult to model. This holds true especially for the evolution of properties upon charge doping. Here, we hole-dope nickel oxide with potassium and elucidate the resulting structure by using a range of experimental and theoretical tools; potassium is twice as big as nickel and is expected to lead to distortions in its vicinity. Our measurements of the x-ray absorption fine structure (XAFS) show a significant distortion around the dopant and that the dopant is fully incorporated in the nickel oxide matrix. In parallel, the theoretical investigations include developing a Gaussian process for quantum Monte Carlo calculations to determine the lowest energy local structure around the potassium dopant. While the optimal structures determined from density functional theory and quantum Monte Carlo calculations agree very well, we find a large discrepancy between the experimentally determined structures and the theoretical doped structures. Further modeling indicates that the discrepancy is likely due to vacancy defects. Furthermore, our work shows that potassium doping is a possible avenue to doping NiO, in spite of the size of the potassium dopant. In addition, the Gaussian process opens up a new route towards obtaining structure predictions outside of density functional theory.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Copper Silver Thin Films with Metastable Miscibility for Oxygen Reduction Electrocatalysis in Alkaline Electrolytes

Increasing the activity of Ag-based catalysts for the oxygen reduction reaction (ORR) is important for improving the performance and economic outlook of alkaline-based fuel cell and metal-air battery technologies. In this work, we prepare CuAg thin films with controllable compositions using e-beam physical vapour deposition. X-ray diffraction analysis indicates that this fabrication route yields metastable miscibility between these two thermodynamically immiscible metals, with the thin films consisting of a Ag-rich and a Cu-rich phase. Electrochemical testing in 0.1 M potassium hydroxide showed significant ORR activity improvements for the CuAg films. On a geometric basis, the most active thin film (Cu 70 Ag 30 ) demonstrated a 4-fold activity improvement versus pure Ag at 0.8 V vs the reversible hydrogen electrode. Furthermore, enhanced ORR kinetics for Cu-rich (> 50at % Cu) thin films was demonstrated by a decrease in Tafel slope from 90 mV/dec, a commonly observed value for Ag catalysts, to 45 mV/dec. Surface enrichment of the Ag-rich phase after ORR testing was indicated by x-ray photoelectron spectroscopy and grazing incidence synchrotron x-ray diffraction measurements. Here, by correlating density functional theory with experimental measurements, we postulate that the activity enhancement of the Cu-rich CuAg thin films arises due to the non-equilibrium miscibility of Cu atoms in the Ag-rich phase, which favourably tunes the surface electronic structure and binding energies of reaction species.

25 ENERGY STORAGE↗

DOE OSTI2023

Accurate prediction of global-density-dependent range-separation parameters based on machine learning

In this work, we develop an accurate and efficient XGBoost machine learning model for predicting the global-density-dependent range-separation parameter, ωGDD, for long-range corrected functional (LRC)-ωPBE. This ωGDDML model has been built using a wide range of systems (11 466 complexes, ten different elements, and up to 139 heavy atoms) with fingerprints for the local atomic environment and histograms of distances for the long-range atomic correlation for mapping the quantum mechanical range-separation values. The promising performance on the testing set with 7046 complexes shows a mean absolute error of 0.001 117 a0−1 and only five systems (0.07%) with an absolute error larger than 0.01 a0−1, which indicates the good transferability of our ωGDDML model. In addition, the only required input to obtain ωGDDML is the Cartesian coordinates without electronic structure calculations, thereby enabling rapid predictions. LRC-ωPBE(ωGDDML) is used to predict polarizabilities for a series of oligomers, where polarizabilities are sensitive to the asymptotic density decay and are crucial in a variety of applications, including the calculations of dispersion corrections and refractive index, and surpasses the performance of all other popular density functionals except for the non-tuned LRC-ωPBE. Finally, LRC-ωPBE (ωGDDML) combined with (extended) symmetry-adapted perturbation theory is used in calculating noncovalent interactions to further show that the traditional ab initio system-specific tuning procedure can be bypassed. The present study not only provides an accurate and efficient way to determine the range-separation parameter for LRC-ωPBE but also shows the synergistic benefits of fusing the power of physically inspired density functional LRC-ωPBE and the data-driven ωGDDML model.

Chemistry↗

DOE OSTI2022

Release of Contaminants from Burning Liquids and Solids

This report details model development, theory, and a literature review focusing on the emission of contaminants on solid substrates in fires. This is the final report from a 2-year Nuclear Safety Research and Development (NSRD) project. The work represents progress towards a goal of having modeling and simulation capabilities that are sufficiently mature and accurate that they can be utilized in place of physical tests for determining safe handling practices. At present, the guidelines for safety are largely empirically based, derived from a survey of existing datasets. This particular report details the development, verification and calibration of a number of code improvements that have been implemented in the SIERRA suite of codes, and the application of those codes to three different experimental scenarios that have been subject of prior tests. The first scenario involves a contaminated PMMA slab, which is exposed to heat. The modeling involved a novel method for simulating the viscous diffusion of the particles in the slab. The second scenario involved a small pool fire of contaminated combustible liquid mimicking historical tests and finds that the release of contaminants has a high functionality with the height of the liquid in the container. The third scenario involves the burning of a contaminated tray of shredded cellulose. A novel release mechanism was formulated based on predicted progress of the decomposition of the cellulose, and while the model was found to result in release that can be tuned to match the experiments, some modifications to the model are desirable to achieve quantitative accuracy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Neural Network Analysis of Nuclear Magnetic Resonance and Infrared Spectra

Nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy are powerful chemical characterization techniques with broad general usage. However, the manual evaluation of the resulting spectra is time-consuming and requires significant expertise, preventing insights from being used in real-time applications. With recent advances in computation and artificial intelligence (AI), new tools are available for automating spectral interpretation. In this work, machine learning (ML) algorithms using 1-dimensional convolutional neural networks (CNNs) were applied to identify common functional groups from spectral information. Raw spectra were collected virtually from the Human Metabolome Database (HMDB) and National Institute of Standards and Technology (NIST) Chemistry WebBook and processed into a suitable standard. Algorithm design was tailored to best fit the nature of the problem, with built-in flexibility to accommodate relevant parameters beyond the raw spectral input, specifically solvent identity and magnetic frequency for NMR. The predictive capability of the algorithm in identifying functional groups is displayed in several examples. This methodology has been compiled into a code repository and could easily be modified to adapt alternative data sources, including other spectrum types. To mitigate overfitting, a common problem in mathematical modeling where overfamiliarity with training data produces trends that are not representative of the general data, a novel metric was developed, referred to as Accufit. Accufit includes a parameter that penalizes substantial differences in the training accuracy and the accuracy of an independent validation set. Examples are presented showing the effectiveness of Accufit in maintaining the model’s predictive capability while controlling the overfitting when used as a custom metric for hyperparameter tuning.

Sturgill, James↗

DOE OSTI2018

Tuning Electronic Structure of Single Layer MoS 2 through Defect and Interface Engineering

Transition-metal dichalcogenides (TMDs) have emerged in recent years as a special group of two-dimensional materials and have attracted tremendous attention. Among these TMD materials, molybdenum disulfide (MoS 2 ) has shown promising applications in electronics, photonics, energy, and electrochemistry. In particular, the defects in MoS 2 play an essential role in altering the electronic, magnetic, optical, and catalytic properties of MoS 2 , presenting a useful way to engineer the performance of MoS 2 . The mechanisms by which lattice defects affect the MoS 2 properties are unsettled. In this work, we reveal systematically how lattice defects and substrate interface affect MoS 2 electronic structure. We fabricated single-layer MoS 2 by chemical vapor deposition and then transferred onto Au, single-layer graphene, hexagonal boron nitride, and CeO 2 as substrates and created defects in MoS 2 by ion irradiation. Here, we assessed how these defects and substrates affect the electronic structure of MoS 2 by performing X-ray photoelectron spectroscopy, Raman and photoluminescence spectroscopies, and scanning tunneling microscopy/spectroscopy measurements. Molecular dynamics and first-principles based simulations allowed us to conclude the predominant lattice defects upon ion irradiation and associate those with the experimentally obtained electronic structure. We found that the substrates can tune the electronic energy levels in MoS 2 due to charge transfer at the interface. Furthermore, the reduction state of CeO 2 as an oxide substrate affects the interface charge transfer with MoS 2 . The irradiated MoS 2 had a faster hydrogen evolution kinetics compared to the as-prepared MoS 2 , demonstrating the concept of defect controlled reactivity in this phase. Our findings provide effective probes for energy band and defects in MoS 2 and show the importance of defect engineering in tuning the functionalities of MoS 2 and other TMDs in electronics, optoelectronics, and electrochemistry.

42 ENGINEERING↗

DOE OSTI2020

Tunable Magnetic Exchange between Rare-Earth Metal 5d and Iron 3d States: A Case Study of the Multiple Magnetic Transitions in Gd 6 FeBi 2 and the Solid Solutions Dy 6– x Gd x FeBi 2 (1 ≤ x ≤ 5) with Curie Temperatures in the Range 120–350 K

In this paper, the room-temperature magnet Gd 6 FeBi 2 was comprehensively characterized by means of temperature-dependent single-crystal X-ray diffraction, magnetization measurements, and experimental electron density and electronic structure computations. This work explores the electron-spin effects of Fe on this structure and suggests that Gd6FeBi2 shows structural features and exchange interactions, which are clearly distinguishable from other analogues, including the solid solutions Dy 6–x Gd x FeBi 2 (0 ≤ x ≤ 5). The unique traits of Gd 6 FeBi 2 and its derivatives encompass abnormal variations of lattice parameters with the temperature, as well as the presence of multiple magnetic transitions of complex nature. Based on the comprehensive analyses, it can be suggested that the unique magnetic response in this material is the result from tunable Fe spin states, which are coupled with strong Gd 5d–Fe 3d interactions, which differ from other members of this large, structural family. In the ground state, according to density functional theory calculations, the Fe atom carries two net electrons, whose spins are coupled in an antiparallel fashion to the spins of the Gd electrons. Near the Curie temperature, the Fe net moment is reduced intermittently, coupled with a multiple of magnetic transitions. The magnetic correlations are manifested in unexpected variations of the lattice parameters as a function of temperature, suggesting spin–lattice interactions. Furthermore, this study emphasizes the important role of Gd 5d electrons in tuning Fe 3d-based magnetic contribution, which enables better understanding on the spin coupling in other related compounds and sheds light on the development of new magnetic and spintronic compounds.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Development of a hybrid neural network and transfer learning model for optimized ICP-MS/MS operation

Correct function and calibration of instrumentation is a crucial assumption for any scientific experiment. One such instrument, tandem inductively coupled plasma mass spectrometer (ICP-MS/MS), has in-depth calibration settings that range across 30+ different parameters, making it difficult to determine optimal conditions without expertise and some degree of trial and error. Often, these settings are hand-tuned, a time-intensive process prone to local maxima and human error. While some automation is available, the automation also may favor local optimizations over a global optimum. In addition to these difficulties, day to day instrument variability can further complicate the calibration process. We propose a solution to this problem as a machine learning (ML) algorithm that learns how each parameter helps determine the calibration sensitivity across several elements, and re-weights parameters over time as instrument variability changes (e.g., a global neural network (NN) with a time-dependent transfer learning (TL) component). This model would be able to generate a surface of predicted calibration sensitivities and their respective parameters, and a simple multivariate algorithm would be able to pull out the optimum results with the settings associated with them. Here-in, we describe our initial findings in working towards this goal, including data extraction from historical files, exploratory data analysis, and some initial model building to better describe the data and the feasibility of our goal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2019

Energetics at the Surface: Direct Optical Mapping of Core and Surface Electronic Structure in CdSe Quantum Dots Using Broadband Electronic Sum Frequency Generation Microspectroscopy

Understanding and controlling the electronic structure of nanomaterials is the key to tailoring their use in a wide range of practical applications. Despite this need, many important electronic states are invisible to conventional optical measurements and are typically identified indirectly based on their inferred impact on luminescence properties. This is especially common and important in the study of nanomaterial surfaces and their associated defects. Surface trap states play a crucial role in photophysical processes yet remain remarkably poorly understood. In this work we demonstrate for the first time that broadband electronic sum frequency generation (eSFG) microspectroscopy can directly map the optically bright and dark states of nanoparticles, including the elusive below gap states. This new approach is applied to model cadmium selenide (CdSe) quantum dots (QDs), where the energies of surface trap states have eluded direct optical characterization for decades. Our eSFG measurements show clear signatures of electronic transitions both above the band gap, which we assign to previously reported one- and two-photon transitions associated with the CdSe core, as well as broad spectral signatures below the band gap that are attributed to surface states. In addition to the core states, this analysis reveals two distinct distributions of below gap states, providing the first direct optical measurement of both shallow and deep surface states on this system. Lastly, chemical modification of the surfaces via oxidation results in the relative increase in the signals originating from the surface states. Overall, our eSFG experiments provide an avenue to directly map the entirety of the QD core and surface electronic structure, which is expected to open up opportunities to study how these materials are grown in situ and how surface states can be controlled to tune functionality.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Phase Transformations among TiO2 Polymorphs

Polymorphs widely exist in nature and synthetic systems and are well known to determine material properties. Understanding phase transformation mechanisms among polymorphs enables the design of structures and tuning of phases to tailor material properties. However, current understanding is limited due to the lack of direct observations that track the processes and the structural evolution is not known at atomic scale. Here, integrating (semi) in situ transmission electron microscopy and density functional theory, we report atomic structural evolutions of phase transformation from anatase (A) to rutile (R), brookite (B), R-phase, and TiO. Besides the consistent paths with previous reports, we discover several unreported paths, including a [001] direction and (020) plane of anatase to [100]A and (011)R of rutile, respectively, ( , ) and , . Density functional theory analysis elucidates atomic structural evolution during the processes and over 16% of Ti-O bonds break and reform during the processes with energy barriers of ~0.7-1.0 eV per TiO2 formula unit. Under electron-beam irradiation, anatase particles transform into TiO2-R phase or TiO at high or room temperature, respectively. We also reveal the anisotropic nature of electron-beam effect, which is seldom discussed: dependence of crystallographic orientation with respect to electron-beam irradiation direction. Understanding the atomic structural evolution sheds light on interpreting and controlling TiO2 polymorphs and interface structures for various applications. The revealed electron-beam effects in our work provide guidance for in situ transmission electron microscopy studies.

Song, Miao↗