arXiv subjects

Jianguo Wen

Publications and source records attributed to Jianguo Wen.

At least 19 recordsLinked to original sources

arXiv2026

From Stacking Disorder to Cubic Order: Ice Crystallization from Deeply Supercooled Water

Crystallization far from equilibrium can generate morphologies that defy classical crystal habits, yet the microscopic mechanisms linking atomic-scale disorder to emergent macroscopic order remain elusive. Here we use in situ cryogenic transmission electron microscopy with a membrane-encapsulated microdroplet platform to directly visualize the freezing of deeply supercooled water at molecular resolution. We show that homogeneous nucleation produces stacking-disordered ice composed of mixed hexagonal and cubic sequences, in which cubic ice initially exists only as isolated monolayers. The gradual thickening of these cubic layers constitutes the key kinetic mechanism that governs the entire crystallization pathway. As thickening proceeds, nanoscale, defect-free cubic ice germs nucleate on the basal planes of the disordered lattice. These faceted cubic germs act as facet-registered kinetic seeds that enforce cubic twinning and sequentially multiply growth branches. This kinetic pathway reproducibly generates robust eight-branched dendrites with global cubic (octahedral) symmetry, even though each branch remains highly stacking-disordered. At later stages, latent heat release drives a crossover to the thermodynamically favored hexagonal phase; remarkably, the pre-established global cubic symmetry is retained. These results reveal how strong kinetic driving forces convert microscopic disorder into emergent macroscopic symmetry, providing a general framework for understanding and controlling rapid crystallization far from equilibrium.

cond-mat.mtrl-sci↗

arXiv2026

Imaging nanoscale photocarrier traps in solar water-splitting catalysts

Defects trap photocarriers and hinder solar water splitting. The nanoscale photocarrier transport, trapping, and recombination mechanisms are usually inferred from ensemble-averaged measurements and remain elusive. Because an individual high-performing nanoparticle photocatalyst may outperform the ensemble average, design rules that would otherwise enhance catalytic efficiency remain unclear. Here, we introduce photomodulated electron energy-loss spectroscopy (EELS) in an optically coupled scanning transmission electron microscope (STEM) to map photocarrier localization. Using rhodium-doped strontium titanate (SrTiO3:Rh) solar water-splitting nanoparticles, we directly image the carrier densities concentrated at oxygen-vacancy surface trap states. This is achieved by separating photothermal heating from photocarrier populations through experimental and computational analyses of low-loss spectra. Photomodulated STEM-EELS enables angstrom-scale imaging of defect-induced photocarrier traps and their impact on photocatalytic efficiency.

physics.chem-ph↗

arXiv2026

The Role of Defect Geometry in Localized Emission from Monolayer Tungsten Dichalcogenides

Understanding the mechanism of single photon emission (SPE) in two-dimensional (2D) material is an unsolved problem important for quantum optical materials and the development of quantum information applications. In 2D transition metal dichalcogenides (TMDs) such as tungsten diselenide (WSe2), quantum emission has been broadly attributed to exciton localization from atomic point defects, yet the precise microscopic origins are not fully understood. This work introduces an empirically grounded computational framework that explains both the origins of facile SPE in WSe2 and its relative scarcity in related TMD, tungsten disulfide. High resolution microscopy identifies native defect geometries existing in monolayer WSe2 lattices providing the ingredients necessary to build a realistic model. The qualitative effects of chalcogen type, defect geometry, and mechanical strain on the electronic structure are then individually assessed using density functional theory, from which a specific divacancy configuration emerges as the candidate for localized single-electron transitions that match observed spectral energies. Spectroscopy and photon correlation measurements further validate this model, establishing a self-consistent link between defect geometry, electronic structure, and quantum emission. By isolating the distinct roles of chalcogen type, defect configuration, and mechanical strain, this work provides a thorough investigation of exciton localization and optical behavior, contributing to a clearer picture of the physical drivers of single photon emission in tungsten-based TMDs.

cond-mat.mtrl-sci↗

arXiv2025

A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent

We envision the Full-Body AI Agent as a comprehensive AI system designed to simulate, analyze, and optimize the dynamic processes of the human body across multiple biological levels. By integrating computational models, machine learning tools, and experimental platforms, this system aims to replicate and predict both physiological and pathological processes, ranging from molecules and cells to tissues, organs, and entire body systems. Central to the Full-Body AI Agent is its emphasis on integration and coordination across these biological levels, enabling analysis of how molecular changes influence cellular behaviors, tissue responses, organ function, and systemic outcomes. With a focus on biological functionality, the system is designed to advance the understanding of disease mechanisms, support the development of therapeutic interventions, and enhance personalized medicine. We propose two specialized implementations to demonstrate the utility of this framework: (1) the metastasis AI Agent, a multi-scale metastasis scoring system that characterizes tumor progression across the initiation, dissemination, and colonization phases by integrating molecular, cellular, and systemic signals; and (2) the drug AI Agent, a system-level drug development paradigm in which a drug AI-Agent dynamically guides preclinical evaluations, including organoids and chip-based models, by providing full-body physiological constraints. This approach enables the predictive modeling of long-term efficacy and toxicity beyond what localized models alone can achieve. These two agents illustrate the potential of Full-Body AI Agent to address complex biomedical challenges through multi-level integration and cross-scale reasoning.

q-bio.TO↗

arXiv2025

Uniaxial spin texture in a superconducting electron gas revealed by exchange interactions

Two-dimensional superconductors with spin-textured Fermi surfaces can be a platform for realizing unconventional pairing states and are of substantial interest in the context of quantum information science, and superconducting spintronics/orbitronics. We observed an unusual in-plane uniaxial anisotropy in the superconducting 2D electron gas (2DEG) formed at EuOx/KTaO3 (110) interfaces, where the EuOx is magnetic. This anisotropy is not evident in AlOx/KTaO3 (110) where the overlayer is non-magnetic. Our results are consistent with a highly anisotropic 'half-Rashba' spin-textured Fermi surface in 2DEGs formed at the KTaO3 (110) interface that is hidden from external magnetic fields due to a near cancellation between orbital and spin moments but revealed by exchange interactions of the electrons in the 2DEG with Eu moments near the EuOx/KTaO3 (110) interface. The interactions between the uniaxial spin texture and the magnetic overlayer offer new ways to explore the interplay between magnetism and 2D superconductivity.

cond-mat.supr-con↗

arXiv2024

Revealing subterahertz atomic vibrations in quantum paraelectrics by surface-sensitive spintronic terahertz spectroscopy

Understanding surface collective dynamics in quantum materials is crucial for advancing quantum technologies. For example, surface phonon modes in quantum paraelectrics are thought to play an essential role in facilitating interfacial superconductivity. However, detecting these modes, especially below 1 terahertz (THz), is challenging due to limited sampling volumes and the need for high spectroscopic resolution. Here, we report surface soft transverse optical (TO1) phonon dynamics in KTaO3 and SrTiO3 by developing surface-sensitive spintronic THz spectroscopy that can sense the collective modes only a few nanometers deep from the surface. In KTaO3, the TO1 mode softens and sharpens with decreasing temperature, leveling off at 0.7 THz. In contrast, this mode in SrTiO3 broadens significantly below the quantum paraelectric crossover and coincides with the hardening of a sub-meV phonon mode related to the antiferrodistortive transition. These observations that deviate from their bulk properties may have implications for interfacial superconductivity and ferroelectricity. The developed technique opens opportunities for sensing low-energy surface excitations.

cond-mat.mtrl-sci↗

arXiv2024

Deterministic Creation of Identical Monochromatic Quantum Emitters in Hexagonal Boron Nitride

Deterministic creation of quantum emitters with high single-photon-purity and excellent indistinguishability is essential for practical applications in quantum information science. Many successful attempts have been carried out in hexagonal boron nitride showing its capability of hosting room temperature quantum emitters. However, most of the existing methods produce emitters with heterogeneous optical properties and unclear creation mechanisms. Here, the authors report a deterministic creation of identical room temperature quantum emitters using masked-carbon-ion implantation on freestanding hBN flakes. Quantum emitters fabricated by our approach showed thermally limited monochromaticity with an emission center wavelength distribution of 590.7 +- 2.7 nm, a narrow full width half maximum of 7.1 +- 1.7 nm, excellent brightness (1MHz emission rate), and extraordinary stability. Our method provides a reliable platform for characterization and fabrication research on hBN based quantum emitters, helping to reveal the origins of the single-photon-emission behavior in hBN and favoring practical applications, especially the industrial-scale production of quantum technology.

cond-mat.mtrl-sci↗

arXiv2023

Optical and microstructural characterization of Er$^{3+}$ doped epitaxial cerium oxide on silicon

Rare-earth ion dopants in solid-state hosts are ideal candidates for quantum communication technologies such as quantum memory, due to the intrinsic spin-photon interface of the rare-earth ion combined with the integration methods available in the solid-state. Erbium-doped cerium oxide (Er:CeO$_2$) is a particularly promising platform for such a quantum memory, as it combines the telecom-wavelength (~1.5 $μ$m) 4f-4f transition of erbium, a predicted long electron spin coherence time supported by CeO$_2$, and is also near lattice-matched to silicon for heteroepitaxial growth. In this work, we report on the epitaxial growth of Er:CeO$_2$ thin films on silicon using molecular beam epitaxy (MBE), with controlled erbium concentration down to 2 parts per million (ppm). We carry out a detailed microstructural study to verify the CeO$_2$ host structure, and characterize the spin and optical properties of the embedded Er$^{3+}$ ions. In the 2-3 ppm Er regime, we identify EPR linewidths of 245(1) MHz, optical inhomogeneous linewidths of 9.5(2) GHz, optical excited state lifetimes of 3.5(1) ms, and spectral diffusion-limited homogenoeus linewidths as narrow as 4.8(3) MHz in the as-grown material. We test annealing of the Er:CeO$_2$ films up to 900 deg C, which yields modest narrowing of the inhomogeneous linewidth by 20% and extension of the excited state lifetime by 40%. We have also studied the variation of the optical properties as a function of Er doping and find that the results are consistent with the trends expected from inter-dopant charge interactions.

cond-mat.mtrl-sci↗

arXiv2023

Nanocavity-mediated Purcell enhancement of Er in TiO$_2$ thin films grown via atomic layer deposition

The use of trivalent erbium (Er$^{3+}$), typically embedded as an atomic defect in the solid-state, has widespread adoption as a dopant in telecommunications devices and shows promise as a spin-based quantum memory for quantum communication. In particular, its natural telecom C-band optical transition and spin-photon interface makes it an ideal candidate for integration into existing optical fiber networks without the need for quantum frequency conversion. However, successful scaling requires a host material with few intrinsic nuclear spins, compatibility with semiconductor foundry processes, and straightforward integration with silicon photonics. Here, we present Er-doped titanium dioxide (TiO$_2$) thin film growth on silicon substrates using a foundry-scalable atomic layer deposition process with a wide range of doping control over the Er concentration. Even though the as-grown films are amorphous, after oxygen annealing they exhibit relatively large crystalline grains, and the embedded Er ions exhibit the characteristic optical emission spectrum from anatase TiO$_2$. Critically, this growth and annealing process maintains the low surface roughness required for nanophotonic integration. Finally, we interface Er ensembles with high quality factor Si nanophotonic cavities via evanescent coupling and demonstrate a large Purcell enhancement (300) of their optical lifetime. Our findings demonstrate a low-temperature, non-destructive, and substrate-independent process for integrating Er-doped materials with silicon photonics. At high doping densities this platform can enable integrated photonic components such as on-chip amplifiers and lasers, while dilute concentrations can realize single ion quantum memories.

cond-mat.mtrl-sci↗

arXiv2022

Ensemble of Pre-Trained Neural Networks for Segmentation and Quality Detection of Transmission Electron Microscopy Images

Automated analysis of electron microscopy datasets poses multiple challenges, such as limitation in the size of the training dataset, variation in data distribution induced by variation in sample quality and experiment conditions, etc. It is crucial for the trained model to continue to provide acceptable segmentation/classification performance on new data, and quantify the uncertainty associated with its predictions. Among the broad applications of machine learning, various approaches have been adopted to quantify uncertainty, such as Bayesian modeling, Monte Carlo dropout, ensembles, etc. With the aim of addressing the challenges specific to the data domain of electron microscopy, two different types of ensembles of pre-trained neural networks were implemented in this work. The ensembles performed semantic segmentation of ice crystal within a two-phase mixture, thereby tracking its phase transformation to water. The first ensemble (EA) is composed of U-net style networks having different underlying architectures, whereas the second series of ensembles (ER-i) are composed of randomly initialized U-net style networks, wherein each base learner has the same underlying architecture 'i'. The encoders of the base learners were pre-trained on the Imagenet dataset. The performance of EA and ER were evaluated on three different metrics: accuracy, calibration, and uncertainty. It is seen that EA exhibits a greater classification accuracy and is better calibrated, as compared to ER. While the uncertainty quantification of these two types of ensembles are comparable, the uncertainty scores exhibited by ER were found to be dependent on the specific architecture of its base member ('i') and not consistently better than EA. Thus, the challenges posed for the analysis of electron microscopy datasets appear to be better addressed by an ensemble design like EA, as compared to an ensemble design like ER.

cond-mat.mtrl-sci↗

arXiv2022

Purcell enhancement of erbium ions in TiO$_{2}$ on silicon nanocavities

Isolated solid-state atomic defects with telecom optical transitions are ideal quantum photon emitters and spin qubits for applications in long-distance quantum communication networks. Prototypical telecom defects such as erbium suffer from poor photon emission rates, requiring photonic enhancement using resonant optical cavities. Many of the traditional hosts for erbium ions are not amenable to direct incorporation with existing integrated photonics platforms, limiting scalable fabrication of qubit-based devices. Here we present a scalable approach towards CMOS-compatible telecom qubits by using erbium-doped titanium dioxide thin films grown atop silicon-on-insulator substrates. From this heterostructure, we have fabricated one-dimensional photonic crystal cavities demonstrating quality factors in excess of $5\times10^{4}$ and corresponding Purcell-enhanced optical emission rates of the erbium ensembles in excess of 200. This easily fabricated materials platform represents an important step towards realizing telecom quantum memories in a scalable qubit architecture compatible with mature silicon technologies.

cond-mat.mtrl-sci↗

arXiv2022

Development of a Scalable Quantum Memory Platform -- Materials Science of Erbium-Doped TiO$_2$ Thin Films on Silicon

Rare-earth ions (REI) have emerged as an attractive candidate for solid-state qubits, particularly as a quantum memory. Their 4f-4f transitions are shielded by filled 5s and 5p orbitals, offering a degree of protection from external electric fields. Embedded within a thin film oxide host, REIs could enable a qubit platform with significant memory capabilities. Furthermore, a silicon-compatible thin film form factor would enable the use of standard semiconductor fabrication processes to achieve chip-based integrability and scalability for functional quantum networks. Towards this goal, we have carried out optical and microstructural studies of erbium-doped polycrystalline and epitaxial TiO$_2$ thin films on Si (100), r-sapphire, and SrTiO$_3$ (100). We observe that the inhomogeneous optical linewidth of the Er photoluminescence is comparable or better for polycrystalline Er:TiO$_2$(grown on Si) in comparison to single crystal epitaxial films on sapphire or SrTiO$_3$, implying a relative insensitivity to extended defects. We investigated the effect of the film/substrate and film/air interface and found that the inhomogeneous linewidth and spectral diffusion can be significantly improved via bottom buffer and top capping layers of undoped TiO$_2$. Using such approaches, we obtain inhomogeneous linewidths of 5.2 GHz and spectral diffusion of 180 MHz in Er:TiO$_2$ /Si(100) films and have demonstrated the engineerability of quantum-relevant properties in these thin films.

cond-mat.mtrl-sci↗

arXiv2021

Ultrafast formation of transient 2D diamond-like structure in twisted bilayer graphene

Due to the absence of matching carbon atoms at honeycomb centers with carbon atoms in adjacent graphene sheets, theorists predicted that a sliding process is needed to form AA, AB, or ABC stacking when directly converting graphite into sp3 bonded diamond. Here, using twisted bilayer graphene, which naturally provides AA and AB stacking configurations, we report the ultrafast formation of a transient 2D diamond-like structure (which is not observed in aligned graphene) under femtosecond laser irradiation. This photo-induced phase transition is evidenced by the appearance of new bond lengths of 1.94A and 3.14A in the time-dependent differential pair distribution function using MeV ultrafast electron diffraction. Molecular dynamics and first principles calculation indicate that sp3 bonds nucleate at AA and AB stacked areas in moire pattern. This work sheds light on the direct graphite-to-diamond transformation mechanism, which has not been fully understood for more than 60 years.

cond-mat.mtrl-sci↗

arXiv2021

Machine Learning the Metastable Phase Diagram of Materials

Phase diagrams are an invaluable tool for material synthesis and provide information on the phases of the material at any given thermodynamic condition. Conventional phase diagram generation involves experimentation to provide an initial estimate of thermodynamically accessible phases, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to inaccessible regions. Such an approach, combined with first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases albeit at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local free energy minimum, that may exhibit desirable properties. Mapping these metastable phases and their thermodynamic behavior is highly desirable but currently lacking. Here, we introduce an automated workflow that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases of a given elemental composition. Using a representative material, carbon, with a vast number of metastable phases without parent in equilibrium, we demonstrate automatic mapping of hundreds of metastable states ranging from near equilibrium to those far-from-equilibrium. Moreover, we incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for construction of metastable phase diagrams. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy are used to validate our metastable phase predictions. Our introduced approach is general and broadly applicable to single and multi-component systems.

cond-mat.mtrl-sci↗

arXiv2021

Atomistic Evidence of Nucleation Mechanism for the Direct Graphite-to-Diamond Transformation

The direct graphite-to-diamond transformation mechanism has been a subject of intense study and remains debated concerning the initial stages of the conversion, the intermediate phases, and their transformation pathways. Here, we successfully recover samples at early conversion stage by tuning high-pressure/high-temperature conditions and reveal direct evidence supporting the nucleation-growth mechanism. Atomistic observations show that intermediate orthorhombic graphite phase mediates the growth of diamond nuclei. Furthermore, we observe that quenchable orthorhombic and rhombohedra graphite are stabilized in buckled graphite at lower temperatures. These intermediate phases are further converted into hexagonal and cubic diamond at higher temperatures following energetically favorable pathways in the order: graphite -> orthorhombic graphite -> hexagonal diamond, graphite -> orthorhombic graphite -> cubic diamond, graphite -> rhombohedra graphite -> cubic diamond. These results significantly improve our understanding of the transformation mechanism, enabling the synthesis of different high-quality forms of diamond from graphite.

cond-mat.mtrl-sci↗

arXiv2021

Lonsdaleite: The diamond with optimized bond lengths and enhanced hardness

Diamond is known as the hardest substance due to its ultra-strong tetrahedral sp3 carbon bonding framework. The only weak link is its cubic cleavage planes between (111) buckled honeycomb layers. Compressing graphite single crystals and heating to moderate temperatures, we synthesized a bulk, pure, hexagonal diamond (lonsdaleite) with distorted carbon tetrahedrons that shorten the bond between its hexagonal (001) buckled honeycomb layers, thus strengthening their linkage. We observed direct transformation of graphite (100) to lonsdaleite (002) and graphite (002) to lonsdaleite (100). We find the bulk lonsdaleite has superior mechanical properties of Vicker hardnesses HV = 164+/-11 GPa and 124+/-13 GPa, measured on the surface corresponding to the original graphite (001) and (100) surfaces, respectively. Properties of lonsdaleite as the supreme material can be further enhanced by purifying the starting material graphite carbon and fine-tuning the high pressure-temperature synthesis conditions.

cond-mat.mtrl-sci↗

arXiv2020

Discovery of two-dimensional anisotropic superconductivity at KTaO$_3$ (111) interfaces

The unique electronic structure found at interfaces between materials can allow unconventional quantum states to emerge. Here we observe superconductivity in electron gases formed at interfaces between (111) oriented KTaO$_3$ and insulating overlayers of either EuO or LaAlO$_3$. The superconducting transition temperature, approaching 2.2 K, is about one order of magnitude higher than that of the LaAlO$_3$/SrTiO$_3$ system. Strikingly, similar electron gases at (001) KTaO$_3$ interfaces remain normal down to 25 mK. The critical field and current-voltage measurements indicate that the superconductivity is two dimensional. Higher mobility EuO/KTaO$_3$ (111) samples show a large in-plane anisotropy in transport properties at low temperatures prior to onset of superconductivity, suggesting the emergence of a stripe like phase where the superconductivity is nearly homogeneous in one direction, but strongly modulated in the other.

cond-mat.supr-con↗

arXiv2019

Atomistic manipulation of reversible oxidation and reduction in Ag by electron beam

Employing electrons for direct control of nanoscale reaction is highly desirable since it provides fabrication of nanostructures with different properties at atomic resolution and with flexibility of dimension and location. Here, applying in situ transmission electron microscopy, we show the reversible oxidation and reduction kinetics in Ag, well controlled by changing the dose rate of electron beam. Aberration-corrected high-resolution transmission electron microscopy observation reveals that O atoms are preferably inserted and extracted along the {111} close-packed planes of Ag, leading to the nucleation and decomposition of nanoscale Ag2O islands on the Ag substrate. By controlling electron beam size and dose rate, we demonstrated fabrication of an array of 3 nm Ag2O nanodots in an Ag matrix. Our results open up a new pathway to manipulate atomistic reaction with electron beam towards the precise fabrication of nanostructures for device applications.

cond-mat.mtrl-sci↗