arXiv subjects

Joshua T. Damron

Publications and source records attributed to Joshua T. Damron.

8 recordsLinked to original sources

arXiv2026

Concerted Electron-Ion Transport by Polyacrylonitrile Elucidated with Reactive Deep Learning Potentials

Charge transport in polymers, such as polyacrylonitrile (PAN), is crucial for electronics and energy storage. For instance, PAN can transport cations e.g., Li+, by facilitating dynamic cation-nitrile coordination in batteries. However, little is known regarding the underlying role of complex reactive polymer configurations. Herein, we develop a deep-learning potential, trained on ab initio energies and forces of nonequilibrium reactive PAN configurations, to unravel the kinetics of PAN cyclization initiated by a nucleophile (OH- dissociated from LiOH) attacking the terminal nitrile carbon. We find, based on the reaction free-energetics, rates, and charge analysis, that the nucleophile attack producing the first ring is the rate-limiting step, which subsequently triggers Li+-coupled electron transfer along the PAN backbone, causing ~10,000 times faster sequential ring-formation of the remaining nitriles. PAN's extended configurations, where dipolar and H-bonding interactions are minimal, enable such rapid kinetics. By validating our computational findings with IR and NMR experiments, we establish a pathway for designing reactive polymers with enhanced charge transport for energy applications.

cond-mat.mtrl-sci↗

arXiv2026

Probing Boron Vacancy Defects in hBN via Single Spin Relaxometry

Spin defects in solids offer promising platforms for quantum sensing and memory due to their long coherence times and optical addressability. Here, we integrate a single nitrogen-vacancy (NV) center in diamond with scanning probe microscopy to discover, read out, and spatially map arbitrary spin-based quantum sensors at the nanoscale. Using the boron vacancy ($\mathrm{V}_\mathrm{B}^-$) center in hexagonal boron nitride$\unicode{x2013}$an emerging two-dimensional spin system$\unicode{x2013}$as a model, we detect its electron spin resonance indirectly via changes in the spin relaxation time ($T_1$) of a nearby NV center, eliminating the need for optical excitation or fluorescence detection of the $\mathrm{V}_\mathrm{B}^-$. Cross-relaxation between NV and $\mathrm{V}_\mathrm{B}^-$ ensembles significantly reduces NV $T_1$, enabling quantitative nanoscale mapping of defect densities beyond the optical diffraction limit and clear resolution of hyperfine splitting in isotopically enriched h$^{10}$B$^{15}$N. Our method demonstrates interactions between 3D and 2D spin sensors, establishing NV centers as versatile probes for characterizing otherwise inaccessible spin defects.

cond-mat.mtrl-sci↗

arXiv2026

Detecting half-quantum superconducting vortices by spin-qubit relaxometry

Half-quantum vortices in spin-triplet superconductors are predicted to harbor Majorana zero modes and may provide a viable avenue to topological quantum computation. Here, we introduce a novel approach for directly measuring the half-integer-quantized magnetic fluxes, $Φ= h / (4e)$, carried by such half-quantum vortices via spin-qubit relaxometry. We consider a superconducting strip with a narrow pinch point at which vortices cross quasi-periodically below a spin qubit as a result of a bias current. We demonstrate that the relaxation rate of the spin qubit exhibits a pronounced peak if the vortex-crossing frequency matches the transition frequency of the spin qubit and conclude that the magnetic flux $Φ$ of a single vortex can be obtained by dividing the corresponding voltage along the strip with the transition frequency. We discuss experimental constraints on implementing our proposed setup in spin-triplet candidate materials like UTe$_2$, UPt$_3$, and URhGe.

cond-mat.supr-con↗

arXiv2025

Cross-scale Modeling of Polymer Topology Impact on Extrudability through Molecular Dynamics and Computational Fluid Dynamics

Understanding how polymer topology influences melt extrudability is critical for advancing material design in extrusion-based additive manufacturing. In this work, we develop a bottom-up, cross-scale modeling framework that integrates coarse-grained molecular dynamics (CGMD) and continuum-scale computational fluid dynamics (CFD) to quantitatively assess the effects of polymer architecture on extrudability A range of branched polydimethylsiloxane (PDMS) polymers are systematically designed by varying backbone length, sidechain length, grafting density, grafted block ratio, and periodicity of grafted-ungrafted segments. CGMD simulations are used to compute zero-shear viscosity and relaxation times, which are then incorporated into the Phan-Thien-Tanner (PTT) model within a computational fluid dynamics (CFD) model to predict pressure drop of PDMS during extrusion through printer nozzle. Qualitative analysis reveals that polymers with concentrated grafted blocks exhibit significantly higher zero-shear viscosity than stochastically branched analogs, while sidechain inertia drives longer relaxation time. However, for untangled and weakly entangled PDMS, relaxation time remains in the nanosecond range, making shear-thinning and elastic effects negligible. Consequently, zero-shear viscosity emerges as the primary determinant of extrudability. This cross-scale modeling strategy provides a predictive framework for guiding the rational design of extrudable polymer materials with tailored topologies.

cond-mat.soft↗

arXiv2025

Chemically resolved nuclear magnetic resonance spectroscopy by longitudinal magnetization detection with a diamond magnetometer

Non-inductive magnetometers based on solid-state spins offer a promising solution for small-volume nuclear magnetic resonance (NMR) detection. A remaining challenge is to operate at a sufficiently high magnetic field to resolve chemical shifts at the part-per-billion level. Here, we demonstrate a Ramsey-M_z protocol that uses Ramsey interferometry to convert an analyte's transverse spin precession into a longitudinal magnetization (M_z), which is subsequently modulated and detected with a diamond magnetometer. We record NMR spectra at B0=0.32 T with a fractional spectral resolution of ~350 ppb, limited by the stability of the electromagnet bias field. We perform NMR spectroscopy on a ~1 nL detection volume of ethanol and resolve the chemical shift structure with negligible distortion. Through simulation, we show that the protocol can be extended to fields up to B0=3 T, with minimal spectral distortion, using composite nuclear-spin inversion pulses. For sub-nanoliter analyte volumes, we estimate a resolution of ~1 ppb and concentration sensitivity of ~40 mM s^{1/2} is feasible with improvements to the sensor design. Our results establish diamond magnetometers as high-resolution NMR detectors in the moderate magnetic field regime, with potential applications in metabolomics and pharmaceutical research.

physics.ins-det↗

arXiv2024

Nanoscale magnetic ordering dynamics in a high Curie temperature ferromagnet

Thermally driven transitions between ferromagnetic and paramagnetic phases are characterized by critical behavior with divergent susceptibilities, long-range correlations, and spin dynamics that can span kHz to GHz scales as the material approaches the critical temperature $\mathrm{T_c}$, but it has proven technically challenging to probe the relevant length and time scales with most conventional measurement techniques. In this study, we employ scanning nitrogen-vacancy center based magnetometry and relaxometry to reveal the critical behavior of a high-$\mathrm{T_c}$ ferromagnetic oxide near its Curie temperature. Cluster analysis of the measured temperature-dependent nanoscale magnetic textures points to a 3D universality class with a correlation length that diverges near $\mathrm{T_c}$. Meanwhile, the temperature-dependent spin dynamics, measured through all optical relaxometry suggest that the phase transition is in the XY universality class. Our results capture both static and dynamic aspects of critical behavior, providing insights into universal properties that govern phase transitions in magnetic materials.

cond-mat.mtrl-sci↗

arXiv2024

The impact of microwave phase noise on diamond quantum sensing

Precision optical measurements of the electron-spin precession of nitrogen-vacancy (NV) centers in diamond form the basis of numerous applications. The most sensitivity-demanding applications, such as femtotesla magnetometry, require the ability to measure changes in GHz spin transition frequencies at the sub-millihertz level, corresponding to a fractional resolution of better than 10^{-12}. Here we study the impact of microwave (MW) phase noise on the response of an NV sensor. Fluctuations of the phase of the MW waveform cause undesired rotations of the NV spin state. These fluctuations are imprinted in the optical readout signal and, left unmitigated, are indistinguishable from magnetic field noise. We show that the phase noise of several common commercial MW generators results in an effective pT s^{1/2}-range noise floor that varies with the MW carrier frequency and the detection frequency of the pulse sequence. The data are described by a frequency domain model incorporating the MW phase noise spectrum and the filter-function response of the sensing protocol. For controlled injection of white and random-walk phase noise, the observed NV magnetic noise floor is described by simple analytic expressions that accurately capture the scaling with pulse sequence length and the number of pi pulses. We outline several strategies to suppress the impact of MW phase noise and implement a version, based on gradiometry, that realizes a >10-fold suppression. Our study highlights an important challenge in the pursuit of sensitive diamond quantum sensors and is applicable to other qubit systems with a large transition frequency.

quant-ph↗

arXiv2024

Deep Learning Interatomic Potential Connects Molecular Structural Ordering to Macroscale Properties of Polyacrylonitrile (PAN) Polymer

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units are indispensable to advance the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers such as dipole-dipole interactions and hydrogen bonding, and analyzing their influence on molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIP) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures/properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen bonding and dipolar correlations, and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties such as density and elastic modulus are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in Hermans orientation factor. This study enables the ability to predict the structure-property relations for PAN and analogs with sustainable ab initio accuracy across scales.

cond-mat.mtrl-sci↗