Discriminative Addressing of Versatile Nanodiamonds via Physically-Enabled Classifier in Complex Bio-Systems
- URL: http://arxiv.org/abs/2408.01164v1
- Date: Fri, 2 Aug 2024 10:29:12 GMT
- Title: Discriminative Addressing of Versatile Nanodiamonds via Physically-Enabled Classifier in Complex Bio-Systems
- Authors: Yayin Tan, Xiaolu Wang, Feng Xu, Xinhao Hu, Yuan Lin, Bo Gao, Zhiqin Chu,
- Abstract summary: Nitrogen-vacancy (NV) centers show great potentials for nanoscale bio-sensing and bio-imaging.
Their envisioned bio-applications suffer from intrinsic background noise due to unavoidable light scattering and autofluorescence in cells and tissues.
- Score: 8.014042040860724
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nitrogen-vacancy (NV) centers show great potentials for nanoscale bio-sensing and bio-imaging. Nevertheless, their envisioned bio-applications suffer from intrinsic background noise due to unavoidable light scattering and autofluorescence in cells and tissues. Herein, we develop a novel all-optical modulated imaging method via physically-enabled classifier, for on-demand and direct access to NV fluorescence at pixel resolution while effectively filtering out background noise. Specifically, NV fluorescence can be modulated optically to exhibit sinusoid-like variations, providing basis for classification. We validate our method in various complex biological scenarios with fluorescence interference, ranging from cells to organisms. Notably, our classification-based approach achieves almost 10^6 times enhancement of signal-to-background ratio (SBR) for fluorescent nanodiamonds (FNDs) in neural protein imaging. We also demonstrate 4-fold contrast improvement in optically-detected magnetic resonance measurements (ODMR) of FNDs inside stained cells. Our technique offers a generic, explainable and robust solution, applicable for realistic high-fidelity imaging and sensing in challenging noise-laden scenarios.
Related papers
- Giant Purcell broadening and Lamb shift for DNA-assembled near-infrared quantum emitters [0.0]
Engineering of plasmonic modes enables cavity-mediated fluorescence far detuned from the zero-phonon-line.
In the future, this approach may also allow to design efficient quantum emitters at infrared wavelengths.
arXiv Detail & Related papers (2024-07-28T15:35:04Z) - Spatial super-resolution in nanosensing with blinking emitters [79.16635054977068]
We propose a method of spatial resolution enhancement in metrology with blinking fluorescent nanosensors.
We believe that blinking fluorescent sensing agents being complemented with the developed image analysis technique could be utilized routinely in the life science sector.
arXiv Detail & Related papers (2024-02-27T10:38:05Z) - FiND: Few-shot three-dimensional image-free confocal focusing on
point-like emitters [0.2094057281590807]
We introduce FiND, an imaging-free, non-trained 3D focusing framework for confocal microscopy.
FiND achieves focusing for signal-to-noise ratios down to 1, with a few-shot operation for signal-to-noise ratios above 5.
Our results show that FiND is a useful focusing framework for the scalable analysis of point-like emitters in biology, material science, and quantum optics.
arXiv Detail & Related papers (2023-11-11T04:41:26Z) - On-chip quantum information processing with distinguishable photons [55.41644538483948]
Multi-photon interference is at the heart of photonic quantum technologies.
Here, we experimentally demonstrate that detection can be implemented with a temporal resolution sufficient to interfere photons detuned on the scales necessary for cavity-based integrated photon sources.
We show how time-resolved detection of non-ideal photons can be used to improve the fidelity of an entangling operation and to mitigate the reduction of computational complexity in boson sampling experiments.
arXiv Detail & Related papers (2022-10-14T18:16:49Z) - Toward deep-learning-assisted spectrally-resolved imaging of magnetic
noise [52.77024349608834]
We implement a deep neural network to efficiently reconstruct the spectral density of the underlying fluctuating magnetic field.
These results create opportunities for the application of machine-learning methods to color-center-based nanoscale sensing and imaging.
arXiv Detail & Related papers (2022-08-01T19:18:26Z) - OADAT: Experimental and Synthetic Clinical Optoacoustic Data for
Standardized Image Processing [62.993663757843464]
Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion.
OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues.
No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
arXiv Detail & Related papers (2022-06-17T08:11:26Z) - Laser threshold magnetometry using green light absorption by diamond
nitrogen vacancies in an external cavity laser [52.77024349608834]
Nitrogen vacancy (NV) centers in diamond have attracted considerable recent interest for use in quantum sensing.
We show theoretical sensitivity to magnetic field on the pT/sqrt(Hz) level is possible using a diamond with an optimal density of NV centers.
arXiv Detail & Related papers (2021-01-22T18:58:05Z) - Resonant Excitation and Purcell Enhancement of Coherent Nitrogen-Vacancy
Centers Coupled to a Fabry-P\'{e}rot Micro-Cavity [0.0]
nitrogen-vacancy (NV) center in diamond has been established as a prime building block for quantum networks.
Poor optical coherence of near-surface NV centers has so far prevented their resonant optical control, as would be required for entanglement generation.
We demonstrate resonant addressing of individual, fiber-cavity-coupled NV centers, and collection of their Purcell-enhanced coherent photon emission.
arXiv Detail & Related papers (2020-09-17T10:48:16Z) - Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis.
We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors.
Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
arXiv Detail & Related papers (2020-05-12T08:01:16Z) - Deep Learning Improves Contrast in Low-Fluence Photoacoustic Imaging [0.7046417074932257]
Low fluence illumination sources can facilitate clinical transition of photoacoustic imaging because they are rugged, portable, affordable, and safe.
Here, we propose a denoising method using a multi-level wavelet-convolutional neural network to map low fluence illumination source images to its corresponding high fluence excitation map.
arXiv Detail & Related papers (2020-04-19T07:06:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.