arXiv subjects

Anmol Singh

Publications and source records attributed to Anmol Singh.

5 recordsLinked to original sources

arXiv2025

Digital Twin Simulations Toolbox of the Nitrogen-Vacancy Center in Diamond

The nitrogen-vacancy (NV) center in diamond is a crucial platform for quantum technologies, where its precise numerical modeling is indispensable for the continued advancement of the field. We present here a Python library for simulating the NV spin dynamics under general experimental conditions, i.e. a digital twin. Our library accounts for electromagnetic pulses and other environmental inputs, which are used to solve the system's time evolution, resulting in a physical output in the form of a quantum observable given by fluorescence. The simulation framework is based on a non-perturbative time-dependent Hamiltonian model, where the states initialization and readout are postulated from the interaction with optical fields. By eliminating oversimplifications such as the adoption of rotating frames for the microwave and radio frequency fields, our simulations reveal subtle dynamics emerging from realistic pulse constraints. The software is illustrated with three examples and validated by comparing the simulations with experimental reports, relevant to the fields of quantum computing (conditional logic gates), sensing (dynamical decoupling sequences with coupled spins) and networks (state teleportation). Overall, this digital twin delivers a robust numerical modeling of the NV spin dynamics, with simple and accessible usability, which can be used for a wide range of applications.

quant-ph↗

arXiv2025

Heat and Hostility: How Substrate Temperature Shapes Bacterial Deposition Patterns and Pathogenesis in Evaporating Droplets

Hypothesis Droplets ejected from the host can directly settle on a substrate as fomite. In industrial environments, especially the food processing industries, the components maintained at specific temperatures can act as a substrate, leading to the fomite mode of infection. We hypothesize that substrate temperature influences the desiccation dynamics, bacterial deposition patterns, and bacterial viability and infectivity. Experiments We conducted a novel study on the desiccation behaviour of bacteria-laden droplets on hydrophilic substrates at different temperatures, an area rarely explored. Such studies have been rarely attempted. We analysed bacterial deposition patterns, mass transport dynamics, and viability across various base fluids used in food industry, such as Milli-Q water, LB media, and meat extract. Thermal imaging, confocal microscopy, scanning electron microscopy, atomic force microscopy, and optical profilometry characterized pattern formations, while bacterial viability and infectivity were assessed post-desiccation Findings Our results indicate that substrate temperature significantly affects bacterial deposition and viability. With Milli-Q water, lower temperatures resulted in ring-like deposits, while higher temperatures led to thinner rings with inner deposits due to Marangoni convection. Radial velocities at 50{\deg}C were an order of magnitude higher than 25{\deg}C. For LB media, dendritic patterns varied with temperature, whereas meat extract patterns remained unchanged. At 60{\deg}C, bacterial surface area was significantly reduced compared to 25{\deg}C while maintaining a constant aspect ratio. Higher temperatures reduced bacterial viability in precipitates, but bacterial infectivity remained nearly unchanged across all base fluids. These findings highlight potential fomite-based infection risks from heated surfaces, particularly in industrial settings.

physics.bio-ph↗

arXiv2024

Ambiguous Resonances in Multipulse Quantum Sensing with Nitrogen Vacancy Centers

Dynamical decoupling multipulse sequences can be applied to solid state spins for sensing weak oscillating fields from nearby single nuclear spins. By periodically reversing the probing system's evolution, other noises are counteracted and filtered out over the total evolution. However, the technique is subject to intricate interactions resulting in additional resonant responses, which can be misinterpreted with the actual signal intended to be measured. We experimentally characterized three of these effects present in single nitrogen vacancy centers in diamond, where we also developed a numerical simulations model without rotating wave approximation, showing robust correlation to the experimental data. Regarding centers with the $^{15}$N nitrogen isotope, we observed that a small misalignment in the bias magnetic field causes the precession of the nitrogen nuclear spin to be sensed by the electronic spin of the center. Another studied case of ambiguous resonances comes from the coupling with lattice $^{13}$C nuclei, where we used the echo modulation frequencies to obtain the interaction Hamiltonian and then utilized the latter to simulate multipulse sequences. Finally, we also measured and simulated the effects from the free evolution of the quantum system during finite pulse durations. Due to the large data volume and the strong dependency of these ambiguous resonances with specific experimental parameters, we provide a simulations dataset with a user-friendly graphical interface, where users can compare simulations with their own experimental data for spectral disambiguation. Although focused with nitrogen vacancy centers and dynamical decoupling sequences, these results and the developed model can potentially be applied to other solid state spins and quantum sensing techniques.

quant-ph↗

arXiv2024

Insights into the mechanics of pure and bacteria-laden sessile whole blood droplet evaporation

We study the mechanics of evaporation and precipitate formation in pure and bacteria-laden sessile whole blood droplets in the context of disease diagnostics. Using experimental and theoretical analysis, we show evaporation process has three stages based on evaporation rate. In the first stage, edge evaporation results in a gelated contact line along the periphery through sol-gel phase transition. The intermediate stage consists of gelated front propagating radially inwards due to capillary flow and droplet height regression in pinned mode, forming a wet-gel phase. We unearthed that the gelation of the entire droplet occurs in the second stage, and the wet-gel formed contains trace amount of water. In the final slowest stage, wet-gel transforms into dry-gel, leading to desiccation-induced stress forming diverse crack patterns in the precipitate. Slow evaporation in the final stage is quantitatively measured using evaporation of trace water and associated transient delamination of the precipitate. Using axisymmetric lubrication approximation, we compute the transient droplet height profile and the erythrocytes concentration for the first two stages of evaporation. We show that the precipitate thickness profile computed from the theoretical analysis conforms to the optical profilometry measurements. We show that the drop evaporation rate and final dried residue pattern do not change appreciably within the parameter variation of the bacterial concentration typically found in bacterial infection of living organisms. However, at exceedingly high bacterial concentrations, the cracks formed in the coronal region deviate from the typical radial cracks found in lower concentrations.

physics.flu-dyn↗

arXiv2021

Data Driven Prediction of Battery Cycle Life Before Capacity Degradation

Ubiquitous use of lithium-ion batteries across multiple industries presents an opportunity to explore cost saving initiatives as the price to performance ratio continually decreases in a competitive environment. Manufacturers using lithium-ion batteries ranging in applications from mobile phones to electric vehicles need to know how long batteries will last for a given service life. To understand this, expensive testing is required. This paper utilizes the data and methods implemented by Kristen A. Severson, et al, to explore the methodologies that the research team used and presents another method to compare predicted results vs. actual test data for battery capacity fade. The fundamental effort is to find out if machine learning techniques may be trained to use early life cycle data in order to accurately predict battery capacity over the battery life cycle. Results show comparison of methods between Gaussian Process Regression (GPR) and Elastic Net Regression (ENR) and highlight key data features used from the extensive dataset found in the work of Severson, et al.

eess.SP↗