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At least 55 records · Page 3

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗

Investigating the role of edge neutrals in exciting tearing mode activity and achieving flat temperature profiles in LTX-β

Abstract We present observations, numerical simulations, and analysis from experiments in the Lithium Tokamak Experiment-Beta (LTX- β ) in which the electron temperature profile ( T e ( r )) shifts from flat to peaked and a tearing mode is also destabilized when the average density ( n e ave ) exceeds ∼10 19 m −3 . Flat T e ( r ) is obtained routinely in LTX- β , with a lithium coated, low-recycling first wall, once the external fueling is stopped and density decays [Boyle et al 2023 Nucl. Fusion 63 056020]. In the present experiment, flat T e profiles can be sustained while maintaining constant n e ave below a line averaged density threshold ( n e ave th ) of ∼10 19 m −3 . Above n e ave th , T e ( r ) shifts from flat to peaked and a tearing mode is destabilized. Due to low recycling, the achieved n e ave can be controlled precisely by external fueling and hence, a certain threshold of the edge neutral inventory from the external fueling is experimentally manifested through n e ave th . The goal of the present work is to investigate the role of edge neutrals in determining T e ( r ) and MHD stability in the unique low-recycling regime of LTX- β . Our hypothesis is that the peaking of T e ( r ) beyond n e ave th is due ultimately to the edge cooling by the cold neutrals beyond a critical fueling flux. At lower fueling flux, flat T e ( r ) results in broader pressure profile and lower resistivity, which in turn stabilizes the tearing mode. This hypothesis is supported by edge neutral density estimation by DEGAS 2 code. Mode analysis by singular value decomposition confirms the tearing mode structure to be m / n = 2/1 ( m and n being the poloidal and toroidal mode numbers). Linear tearing stability analysis with M3D-C1 predicts that plasmas with n e ave > 10 19 are highly susceptible to a n = 1 tearing mode. ORBIT simulations, however, confirmed that the tearing modes do not contribute to the loss of fast ions from neutral beam injection. This study shows for the first time that the neutral inventory at the edge could be one of the deciding factors for the achievability of the unique operation regime of flat T e ( r ) and the excitation of tearing activity that could be disruptive for the plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Crystal structure of N-terminally hexahistidine-tagged Onchocerca volvulus macrophage migration inhibitory factor-1

Onchocerca volvulus causes blindness, onchocerciasis, skin infections and devastating neurological diseases such as nodding syndrome. New treatments are needed because the currently used drug, ivermectin, is contraindicated in pregnant women and those co-infected with Loa loa . The Seattle Structural Genomics Center for Infectious Disease (SSGCID) produced, crystallized and determined the apo structure of N-terminally hexahistidine-tagged O. volvulus macrophage migration inhibitory factor-1 (His- Ov MIF-1). Ov MIF-1 is a possible drug target. His- Ov MIF-1 has a unique jellyfish-like structure with a prototypical macrophage migration inhibitory factor (MIF) trimer as the `head' and a unique C-terminal `tail'. Deleting the N-terminal tag reveals an Ov MIF-1 structure with a larger cavity than that observed in human MIF that can be targeted for drug repurposing and discovery. Removal of the tag will be necessary to determine the actual biological oligomer of Ov MIF-1 because size-exclusion chomatographic analysis of His- Ov MIF-1 suggests a monomer, while PISA analysis suggests a hexamer stabilized by the unique C-terminal tails.

Kimble, Amber D. (ORCID:0000000167851596)↗

Exploring how urban form and demographics are linked with pedestrian and bicycle safety

With pedestrian and bicycle safety as the focus, this study investigates the role of urban form, burdened communities (BCs), and demographics at the national level. Urban form can contribute to segregation, limiting access to crucial resources such as safe infrastructure, essential services, and economic opportunities. Leveraging recent data, this research applies six key indicators to identify BCs based on various socioeconomic and environmental factors. Here, the study creates a unique database combining 10 years of pedestrian-bicycle-involved fatal crashes with data for the 71,729 census tracts with burden indicators and census data. The data are analyzed using descriptives and rigorous zero-hurdle negative binomial models, which account for excessive zeros observed in the data. The inference-based analysis results reveal a positive correlation between burden indicators and pedestrian-bicycle-involved fatal crash occurrences, alongside a heightened risk in areas with high-intensity development. Higher Black, American Indian, or Alaska Native populations are associated with more fatal crashes. The study offers novel insights into safety dynamics across different contexts characterized by urban forms, BCs, and demographics. The study underscores the importance of targeted interventions to enhance pedestrian and bicycle safety.

bicycle crashes↗

On the Effectiveness of Neural Operators at Zero-Shot Weather Downscaling [SWR-25-20]

Code repository for the experiments performed in the paper: On the Effectiveness of Neural Operators at Zero-Shot Weather Downscaling (https://doi.org/10.1017/eds.2025.11) Overall, our work investigates the zero-shot downscaling potential of neural operators. To summarize, our contributions are: 1. We provide a comparative analysis based on two challenging weather downscaling problems, between various neural operator and non-neural-operator methods with large upsampling factors (e.g., 8x and 15x) and fine grid resolutions (e.g., 2 km × 2 km wind speed). 2. We examine whether neural operator layers provide unique advantages when testing downscaling models on upsampling factors higher than those seen during training, i.e., zero-shot downscaling. Our results instead show the surprising success of an approach that combines a powerful transformer-based model with a parameter-free interpolation step at zero-shot weather downscaling. 3. We find that this Swin-Transformer-based approach mostly outperforms all neural operator models in terms of average error metrics, whereas an enhanced super-resolution generative adversarial network (ESRGAN)-based approach is better than most models in capturing the physics of the system, and suggests their use in future work as strong baselines. However, these approaches still do not capture variations at smaller spatial scales well, including the physical characteristics of turbulence in the HR data. This suggests a potential for improvement in transformer or GAN-based methods and neural-operator-based methods for zero-shot weather downscaling.

Sinha, Saumya [National Renewable Energy Laborator↗

Conus-Wide Mapping of Water Availability and Cost

Background: New demands for water can be satisfied through a variety of source options. In some basins surface and/or groundwater may be available through permitting with the state water management agency (termed unappropriated water), alternatively water might be purchased and transferred out of its current use to another (termed appropriated water), or non-traditional water sources can be captured and treated (e.g., wastewater). The relative availability and cost of each source are key factors in the development decision. Unfortunately, these measures are location dependent with no consistent or comparable set of data available for evaluating competing water sources. With the help of water managers, water availability was mapped for over 2500 watersheds throughout CONUS. These values are unique in that they account for institutional factors (i.e., water rights, compacts, treaties, instream flows) that locally limit water use. Five water sources were individually examined, including unappropriated surface water, unappropriated groundwater, appropriated water, municipal wastewater and brackish groundwater. Associated costs to acquire, convey and treat the water, as necessary, for each of the five sources were estimated. These metrics were developed to support regional water planning and policy analysis with initial application to electric transmission planning in the western US. Problem: These data were originally developed in 20141 and updated in 20182. New datasets on which these data depend have been published. As such, there is need to update the water availability and cost data. There is also need to map this same data to a county resolution and to publish the data in a more accessible place. Organization of the dataset: Data are organized according to five primary water sources: • Unappropriated fresh surface water • Unappropriated fresh groundwater • Appropriated water • Recycled wastewater, and • Brackish water

Tidwell, Vincent [Pacific Northwest National Labor↗

Multiparticle Factorization and the Rigidity of String Theory

Is string theory uniquely determined by self-consistency? Causality and unitarity seemingly permit a multitude of putative deformations, at least at the level of two-to-two scattering. Motivated by this question, we initiate a systematic exploration of the constraints on scattering from higher-point factorization, which imposes extraordinarily restrictive sum rules on the residues and spectra defined by a given amplitude. These bounds handily exclude several proposed deformations of the string: the simplest “bespoke” amplitudes with tunable masses and a family of modified string integrands from “binary geometry.” While the string itself passes all tests, our formalism directly extracts the three-point amplitudes for the low-lying string modes without the aid of worldsheet vertex operators. Published by the American Physical Society 2024

Physics↗

Foundations of Molecular 'Isotomics'

The naturally occurring rare isotopes are versions of common elements, such as hydrogen, carbon and oxygen, that contain a larger than usual number of neutrons in their atomic nuclei and therefore are higher in mass than the common atoms of that element. Isotopes exist for most elements and are found in most natural and synthetic materials, but are uneven in their distribution because chemical and physical processes are isotope-selective (e.g., a chemical reaction may proceed more rapidly for one isotope than for another). For this reason, abundances of isotopes in a material of interest can provide a record, or ‘signature’ of various features of that material’s origin and history. These signatures have been used in the geo, life, chemical and physical sciences in a wide variety of ways over close to 8 decades. However, many such applications struggle to reach unique interpretations of isotopic data because multiple factors combine to control a given sample’s overall isotopic content. That is, the factors controlling isotopic content are too numerous and complex to fully constrain from a simple measurement of a material’s isotope abundances. However, the distribution of isotopes within materials, at molecular scales potentially provides a vastly larger number and diversity of constraints on the chemical and physical processes that comprise a material’s history. The rare isotopes may be concentrated into one atomic position in a molecule relative to another, some proportion of molecules in a sample may contain two or more rare isotopes, and those multiply-isotope-substituted forms of molecules may also have uneven distributions of those isotopes across individual atomic sites. For these reasons, even small, seemingly simple molecules, such as sugars, amino acids or drug compounds, actually exist in a vast number of isotopically unique forms (often millions or more), and each one of those forms is in some sense an independent ‘vote’ on that sample’s history. This project has focused on opening this rich archive of information by enabling the creation of routinely and widely applicable ways of measuring and interpreting isotopic structures of molecules. This work has included the development of core technologies and analytical methods, advancing fundamental understanding of the physical and chemical properties of isotopic versions of molecules, and conducting proof of concept studies of illustrative geochemical, cosmochemical and forensic problems in order to show how these technologies, methods and principles come together to solve problems in new ways. A key to the success of this project was the adaptation of ‘Fourier transform mass spectrometry’ (FTMS) to the task of precisely measuring proportions of the rare, naturally occurring isotopic forms of molecules. FTMS is a highly specialized form of mass spectrometry that traps ions within magnetic or electrostatic cavities and, effectively, ‘listens’ (through registering of subtle electrical signals) to the harmonic signals they make while rapidly orbiting within those cavities. These signals have periods that are a function of their mass and strength (or ‘loudness’) that is proportional to their abundances. Thus, these signals constrain relative amounts of molecules that differ in their mass due to various isotopic substitutions. This technology has been essential to the identification of organic molecules in the life, chemical and environmental sciences for over 4 decades, but generally has lacked the control, stability and precision to meaningfully measure rare isotope forms of molecules. This project’s most fundamental contribution has been to modify FTMS, both in terms of hardware and methods, to enable such measurements. The raw data of molecular isotopic structure is tremendously voluminous and complex, so another important activity of this project has been developing the theoretical and data-science tools needed to interpret the data generated by this new form of isotopic measurement. A particularly challenging part of this task has been predicting molecular isotopic structure, as only through the comparison of measurements with predictions can we make progress on hypothesis driven research questions. We have attacked this this prediction task through a combination of first-principles chemical-physics models of the effects of isotope substitution on molecule properties and data-science models that permit us to generalize that chemical physics to cases that have not yet been studied by detailed chemical physics theory. The proof of concept applications we have pursued over the course of this study include biological reactions of amino acids and other biomolecules, non-biological synthesis of organic molecules in extra-terrestrial settings such as meteorites, petroleum geoscience questions concerning the origin and evolution of natural gas, oil and kerogen compounds, and forensic questions such as the sourcing of chemical weapons. The successes of these applications have laid the groundwork for the next phase of this field’s development, which will include larger scale and more ambitious studies of molecular isotopic structure as a means of diagnosing human diseases, such as cancer, and reconstructing detailed interpretations of the origin and evolution of organic molecules in modern and geological environments.

Cesar, Jaime↗

Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials

Optical metamaterials manipulate light through various confinement and scattering processes, offering unique advantages like high performance, small form factor and easy integration with semiconductor devices. However, designing metasurfaces with suitable optical responses for complex metamaterial systems remains challenging due to the exponentially growing computation cost and the ill‐posed nature of inverse problems. To expedite the computation for the inverse design of metasurfaces, a physics‐informed deep learning (DL) framework is used. A tandem DL architecture with physics‐based learning is used to select designs that are scientifically consistent, have low error in design prediction, and accurate reconstruction of optical responses. The authors focus on the inverse design of a representative plasmonic device and consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. The physics‐based constraint is derived from solving the electromagnetic wave equations for a simplified homogenized model. The model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input, and up to 96% for optical response of single wavelength of light as input, with optical response reconstruction accuracy of 99%.

Sarkar, Sulagna↗

Genomic adaptations of the green alga Dunaliella salina to life under high salinity

Life in high salinity environments poses challenges to cells in a variety of ways: maintenance of ion homeostasis and nutrient acquisition, often while concomitantly enduring saturating irradiances. Dunaliella salina has an exceptional ability to thrive even in saturated brine solutions. This ability has made it a model organism for studying responses to abiotic stress factors. Here we describe the occurrence of unique gene families, expansion of gene families, or gene losses that might be linked to osmoadaptive strategies. We discovered multiple unique genes coding for several of the homologous superfamily of the Ser-Thr-rich glycosyl-phosphatidyl-inositol-anchored membrane family and of the glycolipid 2-alpha-mannosyltransferase family, suggesting that such components on the cell surface are essential to life in high salt. Gene expansion was found in families that participate in sensing of abiotic stress and signal transduction in plants. One example is the patched family of the Sonic Hedgehog receptor proteins, supporting a previous hypothesis that plasma membrane sterols are important for sensing changes in salinities in D. salina. We also investigated genome-based capabilities regarding glycerol metabolism and present an extensive map for core carbon metabolism. We postulate that a second broader glycerol cycle exists that also connects to photorespiration, thus extending the previously described glycerol cycle. Further genome-based analysis of isoprenoid and carotenoid metabolism revealed duplications of genes for 1-deoxy-D-xylulose-5-phosphate synthase (DXS) and phytoene synthase (PSY), with the second gene copy of each enzyme being clustered together. Moreover, we identified two genes predicted to code for a prokaryotic-type phytoene desaturase (CRTI), indicating that D. salina may have eukaryotic and prokaryotic elements comprising its carotenoid biosynthesis pathways. In brief, our genomic data provide the basis for further gene discoveries regarding sensing abiotic stress, the metabolism of this halophilic alga, and its potential in biotechnological applications.

59 BASIC BIOLOGICAL SCIENCES↗

Investigating the weak charge of 48 Ca using a dispersive optical model

A new nonlocal dispersive-optical-model analysis has been carried out for neutrons and protons in 48 Ca that reproduces the weak-form-factor measurement of CREX. In addition to elastic-scattering angular distributions, total and reaction cross sections, single-particle energies, the neutron and proton numbers, and the charge distribution, the CREX-measured weak form factor has been fit to extract the neutron and proton self-energies both above and below the Fermi energy. The resulting single-particle propagators yield a weak form factor of F w = 0.125 ± 0.05 and a neutron skin of R skin = 0.152 ± 0.05 fm, in good agreement with CREX. The rearrangement of the neutron distribution to accommodate such a thin neutron skin results in the high-momentum content of the neutrons exceeding that of the protons, in contrast to what is expected from high-energy two-nucleon knockout measurements by the CLAS collaboration and ab initio asymmetric matter calculations. The present analysis also emphasizes the importance of neutron experimental data in constraining weak charge observables necessary for a precise description of neutron densities. Notably, the neutron reaction cross section and further parity-violating experiments weak form factor measurements are essential to generate a unique way to determine the 48 Ca neutron distribution in this framework.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Predicted Ferromagnetism in Discovered Co–Bi Binary Phases

Binary solid-state materials offer unique insight into how the interplay of factors such as stoichiometry and bonding interactions affects magnetism and electronic properties. In this work, we considered systems where a transition metal provides the spin moment and a heavy main group element bolsters strong spin–orbit coupling. Within this context, cobalt, a known component of permanent magnets, and bismuth, functionally the heaviest element stable to radioactive decay, form a compelling combination. The Co–Bi system has been previously shown to exhibit superconductivity in a phase recovered from high pressure. We expected the Co−Bi system could also be ferromagnetic, resulting in two sets of compounds within one chemical system, one superconducting and one ferromagnetic. Subsequently, we investigated the Co–Bi system through both experimental and theoretical approaches to discover new candidates for permanent magnets. Ab initio random structure searching calculations identified five new compounds with diverse structural motifs that may form at higher pressures than previously reported. Experimental high-pressure synthesis yielded four compounds: α-CoBi, α-CoBi 2 , β-CoBi, and β-CoBi 2 . Three of these phases, α-CoBi 2 , β-CoBi, and β-CoBi 2 , were consistent with the calculated structures, corresponding to a 60% success rate for our structure search and underscoring the strength of combining computation with experiment. Theory predicts β-CoBi and β-CoBi2 are ferromagnetic, with β-CoBi possessing larger magnetocrystalline anisotropy energy than familiar permanent magnets such as CoPt and Nd–Fe–B. These results suggest the Co–Bi system could be a platform for understanding the factors that underpin magnetism and, to an extent, superconductivity in a chemically simple binary system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Measurement of femtosecond dynamics of ultrafast electron beams through terahertz compression and time-stamping

Visualizing ultrafast dynamics at the atomic scale requires time-resolved characterization with femtosecond temporal resolution. For fully relativistic electron bunch probes, existing techniques for single-shot ultrafast electron diffraction (UED) are limited by the achievable electron probe bunch length, charge, and timing jitter. We present the first experimental demonstration of dual-fed THz-driven compression and time-stamping that enables electron probes with improved temporal resolution. This technique utilizes two counter-propagating quasi-single-cycle THz pulses generated from two OH-1 organic crystals coupled into an optimized THz compressor structure. We demonstrate electron bunch compression and time-of-arrival jitter suppression by a factor of 3 paving the way toward unique opportunities for UED time-resolved measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Economic losses from extreme weather in the U.S. Gulf Coast region: spatially differential contributions of climate hazard and socioeconomic exposure and vulnerability

Worldwide economic losses from extreme weather events (EWE) have increased over recent decades, with significant geographic heterogeneity in damages. The IPCC defines the risk from EWE as a function of the climate hazard, socioeconomic exposure, and vulnerability. Although these three drivers vary at fine spatial scales, spatial variability largely has been overlooked in assessments of the drivers of economic loss from EWE. Using cluster analysis, we developed a novel socio-climate hazard typology (SCT) that integrates locally defined climate hazard and socioeconomic exposure and social vulnerability typologies. The results identified 838 unique SCT types impacted by EWE across the Gulf Coastal United States during 1981–2010. We regressed the SCT types and their constituent hazard and socioeconomic components against the cumulative economic loss (1981–2010) from EWE for each SCT type. Across the landscape, economic damages of SCT types were determined by unique, spatially explicit combinations of different risk factors, even in explaining the same level of economic loss. For example, multi-billion-dollar damages in the central Gulf Coast and peninsular Florida were explained by different drivers of risk, with damages in the former explained by additive interaction between climate hazard and multiplicative interaction between climate hazard and socioeconomic exposure and vulnerability, and in the latter explained by socioeconomic exposure and vulnerability. These results highlight the need to diagnose additive and multiplicative interactions among drivers of EWE risk in a spatially explicit context.

54 ENVIRONMENTAL SCIENCES↗

Search for the Exclusive W Boson Hadronic Decays W ± → π ± γ, W ± → K ± γ and W ± → ρ ± γ with the ATLAS Detector

A search for the exclusive hadronic decays W ± → π ± γ, W ± → K ± γ, and W ± → ρ ± γ is performed using up to 140 fb -1 of proton-proton collisions recorded with the ATLAS detector at a center-of-mass energy of $\sqrt{s} =$ 13 TeV. If observed, these rare processes would provide a unique test bench for the quantum chromodynamics factorization formalism used to calculate cross sections at colliders. Additionally, at future colliders, these decays could offer a new way to measure the W boson mass through fully reconstructed decay products. The search results in the most stringent upper limits to date on the branching fractions B(W ± → π ± γ) < 1.9 × 10 -6 , B(W ± → K ± γ) < 1.7 × 10 -6 , B(W ± → ρ ± γ) < 5.2 × 10 -6 at 95% confidence level.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Variations to the z-Expansion of the Form Factor Describing the Decay of B Mesons

We examine the decay rate of the particle decay B 0 → D -ℓ +νℓ using data collected from the Belle Collaboration. We studied three parameterizations of the form factor which describe the differential decay rate, the Caprini, Lellouch, and Neubert (CLN) parametrization, the Boyd, Grinstein, and Lebed (BGL) parametrization, and the Bourrely, Caprini, and Lellouch (BCL) parameterization. The form factor is a function of the hadronic recoil variable w, and each parameterization contains unique free parameters which are the focus of this work. We test the extrapolations of the form factor by fitting many different subsets of the low w data and then compare the prediction of the fit to the high w data using a χ 2 -metric. By only fitting the low w data we are able to examine the stability of extrapolations which will be informative for lattice simulations.

Simons, Daniel↗

Performance-limiting formation dynamics in mixed-halide perovskites

Wide-bandgap (WBG) mixed-halide perovskites as the front cell absorber are accomplishing perovskite-based tandem solar cells with over 29% power conversion efficiency. However, their large voltage deficits limit their ultimate performance. Only a handful of studies probe the fundamental mechanisms underlying the voltage deficits, which remain an unsolved challenge in the field. In this study, we investigate the formation dynamics and defect physics of WBG mixed-halide perovskites in contrast with their corresponding triiodide-based perovskites. Our results show that the inclusion of bromide introduced a halide homogenization process that occurs during the perovskite growth stage from an initial bromide-rich phase toward the final target stoichiometry. We further elucidated a physical model that correlates the role of bromide with the formation dynamics, defect physics, and eventual optoelectronic properties of the film. This work provides a fundamental and unique perspective toward understanding the performance-limiting factors affecting WBG mixed-halide perovskites.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Room-temperature high-purity single-photon emission from carbon-doped boron nitride thin films

Hexagonal boron nitride (h-BN) has emerged as a promising platform for generating room temperature single photons exhibiting high brightness and spin-photon entanglement. However, improving emitter purity, stability, and scalability remains a challenge for quantum technologies. Here, we demonstrate highly pure and stable single-photon emitters (SPEs) in h-BN by directly growing carbon-doped, centimeter-scale h-BN thin films using the pulsed laser deposition (PLD) method. These SPEs exhibit room temperature operation with polarized emission, achieving a g (2) (0) value of 0.015, which is among the lowest reported for room temperature SPEs and the lowest achieved for h-BN SPEs. It also exhibits high brightness (~0.5 million counts per second), remarkable stability during continuous operation (>15 min), and a Debye-Waller factor of 45%. First-principles calculations reveal unique carbon defects responsible for these properties, enabled by PLD’s low-temperature synthesis and in situ doping. Our results demonstrate an effective method for large-scale production of high-purity, stable SPEs in h-BN, enabling robust quantum optical sources for various quantum applications.

36 MATERIALS SCIENCE↗