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Anshuman Nayak

Publications and source records attributed to Anshuman Nayak.

4 recordsLinked to original sources

Microwave-Free $^{13}$C Hyperpolarization of Diamond Particles Enabled by Magic Angle Spinning and NV Centers

Nuclear hyperpolarization from optically pumped color centers in solids offers an alternative to conventional microwave-driven dynamic nuclear polarization (DNP). Diamond can host the nitrogen vacancy (NV) center, whose ground spin state can be readily polarized by light at room temperature, making diamond a candidate platform for nuclear hyperpolarization. We report $^{13}{\rm C}$ nuclear hyperpolarization in randomly oriented diamond particles with sizes ranging from 0.2 to 2 $μ$m, both at natural $^{13}{\rm C}$ abundance (1.1 %) and at 20 % isotopic enrichment, at magnetic fields of 7.1 T and 9.4 T. The protocol combines optical illumination with magic angle spinning (MAS) and does not require microwave irradiation. By investigating the nuclear polarization as a function of the MAS frequency between 0 and 6 kHz at the magnetic field of 7.1 T, we find maximum light-induced polarization enhancements of $280$-fold for the isotopically enriched sample and $411$-fold for the natural abundance sample. Under continuous illumination, steady-state absolute $^{13}{\rm C}$ polarization levels above 0.1 % are reached. A model involving optical pumping of NV centers and spin dynamics near level anticrossings (LACs) in three-spin clusters formed by NV, a substitutional nitrogen (P1) and $^{13}{\rm C}$ is used to describe these findings. The protocol strongly mitigates the effect of the anisotropy of the NV spin Hamiltonian, allowing more than $99.9\%$ of NV orientations to participate in the polarization transfer process. These results represent a first step toward transferring nuclear polarization from diamond particles to external nuclei, with potential applications in sensitive and high-resolution NMR at room temperature.

quant-ph↗

Spin-force from a Nitrogen-Vacancy ensemble drives a 100 mg levitated resonator

The force experienced by a spin in a magnetic field gradient underlies many proposals for hybrid quantum systems. These include schemes for mechanically mediated quantum gates, spin squeezing, searches for exotic forces, and motional superpositions for probing the interface between quantum and gravity. Yet, experimentally observing this spin-force for anything larger than atomic scales has proved challenging. In our work, we demonstrate controllable Center-of-Mass motion of a $128 \rm\: mg$ diamagnetically levitated oscillator due to force from an ensemble of Nitrogen-Vacancy (NV) defects in diamond. We induce coherent motion in the oscillator by periodic optical initialisation of the NV spin states, achieving motional amplitudes exceeding $100 \rm\:nm$. Our results mark a key milestone towards spin-based engineering of motional states deep in the high-mass regime.

quant-ph↗

Spin-mechanical thermal machines

We introduce a method to construct a quantum battery and a quantum Otto heat engine using a Nitrogen-Vacancy (NV) center spin coupled to a mechanical oscillator in a highly detuned regime. By precisely controlling the NV spin, we enable efficient energy exchange despite significant detuning, challenging conventional assumptions. This leads to a robust mechanical quantum battery and a cooling scheme driving the oscillator toward its ground state. Leveraging this, we implement a quantum Otto engine that produces mechanical work at maximum efficiency without decoupling from the hot bath, paving the way for practical quantum thermal machines.

quant-ph↗

Prediction of soil fertility parameters using USB-microscope imagery and portable X-ray fluorescence spectrometry

This study investigated the use of portable X-ray fluorescence (PXRF) spectrometry and soil image analysis for rapid soil fertility assessment, with a focus on key indicators such as available boron (B), organic carbon (OC), available manganese (Mn), available sulfur (S), and the sulfur availability index (SAI). A total of 1,133 soil samples from diverse agro-climatic zones in Eastern India were analyzed. The research integrated color and texture features from microscopic soil images, PXRF data, and auxiliary soil variables (AVs) using a Random Forest model. Results showed that combining image features (IFs) with AVs significantly improved prediction accuracy for available B (R2 = 0.80) and OC (R2 = 0.88). A data fusion approach, incorporating IFs, AVs, and PXRF data, further enhanced predictions for available Mn and SAI, with R2 values of 0.72 and 0.70, respectively. The study highlights the potential of integrating these technologies to offer rapid, cost-effective soil testing methods, paving the way for more advanced predictive models and a deeper understanding of soil fertility. Future work should explore the application of deep learning models on a larger dataset, incorporating soils from a wider range of agro-climatic zones under field conditions.

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