COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning.

During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.

Designing and development of agricultural rovers for vegetable harvesting and soil analysis.

To address the growing demand for sustainable agriculture practices, new technologies to boost crop productivity and soil health must be developed. In this research, we propose designing and building an agricultural rover capable of autonomous vegetable harvesting and soil analysis utilizing cutting-edge deep learning algorithms (YOLOv5). The precision and recall score of the model was 0.8518% and 0.7624% respectively. The rover uses robotics, computer vision, and soil sensing technology to perform accurate and efficient agricultural tasks. We go over the rover's hardware and software, as well as the soil analysis system and the tomato ripeness detection system using deep learning models. Field experiments indicate that this agricultural rover is effective and promising for improving crop management and soil monitoring in modern agriculture, hence achieving the UN's SDG 2 Zero Hunger goals.

Design optimization and validation of UV-C illumination chamber for filtering facepiece respirators.

In this study, we constructed an UV-C illumination chamber using commercially available germicidal lamps and other locally available low-cost components for general-purpose biological decontamination purposes. The illumination chamber provides uniform illumination of around 1 J/cm2 in under 5 min across the chamber. The control mechanism was developed to automate the on/off process and make it more secure minimizing health and other electrical safety. To validate the decontamination efficacy of the UV-C Illumination Chamber we performed the Geobacillus spore strip culture assay. Additionally, we performed the viral load measurement by identifying the COVID-19-specific N-gene and ORF1 gene on surgical masks. The gold standard RT-qPCR measurement was performed to detect and quantify the COVID-19-specific gene on the mask sample. The biochemical assay was conducted on the control and test group to identify the presence of different types of bacteria, and fungi before and after exposure under the illumination chamber. The findings of our study revealed satisfactory decontamination efficacy test results. Therefore, it could be an excellent device in healthcare settings as a disinfection tool for biological decontamination such as SAR-CoV-2 virus, personal protection equipment (PPE), (including n95, k95 respirators, and surgical masks), and other common pathogens.

Metasurface absorber based single junction thin film solar cell exceeding 30% efficiency.

In this article, we report, as per our knowledge, for the first time, a thin film single junction solar cell with a metasurface absorber layer directly incorporated. We have used an interconnected dual inverted split ring resonator pattern in the InAsP absorber layer. The structure eliminated patterns of conventional metals, such as silver, aluminum, and gold, from the active layer, a common drawback in conventional solar absorbers, hindering their direct integration into solar cells. Optical simulation results show a peak ideal short circuit current density of 76.23mA/cm2 for the meta-absorber structure under solar illumination. This current is the highest among previously reported absorbers based on Group IV materials and III-V compounds, overcoming the low solar absorption of such metasurfaces. The final proposed solar cell structure combines this meta-absorber layer with traditional efficiency enhancement methods namely anti-reflecting coating, textured back reflector, and transparent top electrode. This novel single junction structure shows a solar absorption efficiency of 97.86% and a power conversion efficiency of 30.87%, the highest for III-V solar cells. Our device proves the ability of metasurface absorber layers to produce high-efficiency solar cells and is expected to pave the way for integrating novel meta-devices into state-of-the-art photovoltaic devices, aiding the global transition towards clean energy sources.

Label-free quantification of gold nanoparticles at the single-cell level using a multi-column convolutional neural network (MC-CNN).

Gold nanoparticles (AuNPs) are extensively used in cellular imaging, single-particle tracking, disease diagnosis, studying membrane protein interaction, and drug delivery. Understanding the dynamics of AuNP uptake in live cells is crucial for optimizing their efficacy and safety. Traditional manual methods for quantifying AuNP uptake are time-consuming and subjective, limiting their scalability and accuracy. The available fluorescence-based techniques are limited to photobleaching and photoblinking. Optical microscopy techniques are limited by diffraction limits. Electron microscopy-based imaging techniques are destructive and unsuitable for live cell imaging. Furthermore, the resulting images may contain hundreds of particles with varied intensities, blurring, and substantial occlusion, making it difficult to manually quantify AuNP uptake. To overcome this issue and measure AuNP uptake by live cells, we annotated a dataset of dark-field images of 50 nanometer-radius AuNPs at different incubation durations. Then, to count the number of particles present in a cell, we created a customized multi-column convolutional neural network (MC-CNN). The customized MC-CNN outperformed typical particle counting architectures when compared to spectroscopy-based counting. This will allow researchers to gain a better understanding of AuNP behavior and interactions with cells, paving the way for advancements in nanomedicine, drug delivery, and biomedical research. The code for this paper is available at the following link: https://github.com/Namerlight/LabelFree_AuNP_Quantification.

Efficiency improvement of thin film solar cell using silver pyramids array and antireflective layer.

In recent years, plasmonics has been widely employed to improve light trapping in solar cells. Silver nanospheres have been used in several research works to improve the capability of solar absorption. In this paper, we use silver pyramid-shaped nanoparticles, a noble plasmonic nanoparticle, inside thin-film silicon and InP solar cells to increase light absorption compared to previously published topologies. The proposed structure consists of a TiO2 pyramid structure placed at the top of the surface working as an anti-reflective layer, silicon/indium phosphate as an absorption layer, silver pyramid-shaped nanoparticles incorporated inside the absorption layer, and an aluminum reflecting layer at the bottom. In this research, we used finite difference time domain (FDTD) simulation to model the thin-film solar cell (TFSC). Optimizing the shape and placement of the silver pyramids, we have achieved an efficiency of 17.08% and 18.58% using silicon and InP as the absorbing layers respectively, which is significantly better than previously reported studies. The open-circuit voltages are 0.58 V and 0.92 V respectively, which is the highest among other configurations. To conclude, the findings of this study laid the foundation to create an efficient thin-film solar cell utilizing the light-trapping mechanism of noble plasmonic nanoparticles.

Probing the intracellular refractive index and molecular interaction of gold nanoparticles in HeLa cells using single particle spectroscopy.

BACKGROUND: We have introduced a novel method to quantify the intracellular refractive index (RI) of living cells and determine the molecular interaction of two interacting molecules using single particle spectroscopy. The advantages of this proposed technique over fluorescence-based imaging techniques is that it does not require any contrasting agent and it does not blink and bleach. Instead, our technique provides a non-destructive, non-invasive, high-resolution imaging of live cells. METHODS: To verify our technique, we initially tested our approach for a dielectric medium where gold nanoparticles (AuNPs) were embedded in a polyvinyl alcohol (PVA) matrix, which was then extended to the cellular environment. In the dielectric medium, we identified the single particle and dimer and determined the interparticle distance of AuNPs using confocal laser scattering microscopy. We also determined the single particle RI from dark-field scattering microscopy images, which was confirmed with Mie theory and finite-difference time-domain (FDTD) simulated results. The single particle spectroscopy and microscopy technique was then extended to determine the intracellular RI and biomolecular interaction inside living cells using hyperspectral imaging and dark-field scattering microscopy. RESULTS: The novelty of the paper lies in the demonstration of a direct and accurate method to probe the intracellular RI and molecular interaction focused on single particle analysis whereas previous demonstrations were based on AuNP ensembles. Optically acquired single particle and dimer images was verified by correlated SEM images also optical spectrum with analytical models and FDTD simulations for both the dielectric and cellular environment. We reported the interparticle distance of AuNPs inside HeLa cells and intracellular refractive index, which was also confirmed with Mie Theory and extensive FDTD simulations. CONCLUSION: Moreover, we believe that our in-depth plasmonic NP-based alternate imaging technique will provide a new insight in monitoring cellular dynamics and tracking the targeted NPs within live cells, enabling us to use plasmonic NPs as an intracellular biosensor.

Refractive-Index Tuning of Highly Fluorescent Carbon Dots.

In this manuscript, we report the refractive-index (RI) modulation of various concentrations of nitrogen-doped carbon dots (N@C-dots) embedded in poly(vinyl alcohol) (PVA) polymer. The dispersion and size distribution of N@C-dots embedded within PVA have been investigated using electron microscopy. The RI of PVA-N@C-dots can be enhanced by increasing the doping concentration of highly fluorescent C-dots (quantum yield 44%). This is demonstrated using ultraviolet-visible (UV-visible), photoluminscence, Raman, and Fourier transform infrared (FTIR) spectroscopy measurements. The Mie scattering of light on N@C-dots was applied for developing the relationship between RI tuning and absorption cross section of N@C-dots. The extinction cross section of N@C-dot thin films can be rapidly enhanced by either tuning the RI or increasing the concentration of N@C-dots. The developed method can be used as effective RI contrast for various applications such as holography creation and bioimaging.

Electron-beam lithography of plasmonic nanorod arrays for multilayered optical storage.

In this paper we demonstrate multilayer fabrication of plasmonic gold nanorod arrays using electron-beam lithography (EBL), and show that this structure could be used for multilayered optical storage media capable of continuous-wave (cw) laser readout. The gold nanorods fabricated using the EBL method are aligned perfectly and homogeneous in size and shape, allowing the polarization response of surface plasmon resonance (SPR) to be observed through ensemble array. This property in turn permits polarization detuned SPR readout possible and other manipulations such as progressively twisted arrays through the multilayers to make cw readout possible through deeper layers without too much extinction loss. The layered gold nanorod arrays are separated by thick spacer layer to enable the optical resolving of individual layers. Using this method, we demonstrated four-fold reduction in extinction loss for cw readout in three-layer structure. The current technique of multilayer fabrication and readout can be useful in 3-dimensional fabrication of plasmonic circuits and structures.

Ultra-pure, water-dispersed Au nanoparticles produced by femtosecond laser ablation and fragmentation.

Aqueous solutions of ultra-pure gold nanoparticles have been prepared by methods of femtosecond laser ablation from a solid target and fragmentation from already formed colloids. Despite the absence of protecting ligands, the solutions could be (1) fairly stable and poly size-dispersed; or (2) very stable and monodispersed, for the two fabrication modalities, respectively. Fluorescence quenching behavior and its intricacies were revealed by fluorescence lifetime imaging microscopy in rhodamine 6G water solution. We show that surface-enhanced Raman scattering of rhodamine 6G on gold nanoparticles can be detected with high fidelity down to micromolar concentrations using the nanoparticles. Application potential of pure gold nanoparticles with polydispersed and nearly monodispersed size distributions are discussed.