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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

TRIZ-Based Design Improvement for Facilitating Transmission Control Unit Remanufacturing

Design for Remanufacturing (DfRem) centers on enhancing product design and operations to facilitate remanufacturing while increasing both economic and environmental sustainability. DfRem requires a comprehensive understanding of product characteristics, production conditions, and operational constraints. Thus, DfRem requires a systematic methodology to identify and implement alternative designs. Among many critical steps in remanufacturing, disassembly is essential because it directly supports remanufacturing by separation of product components for cleaning, inspection, and other subsequent process steps. This study proposes a TRIZ-based framework to improve product design for disassembly in support of remanufacturing. The framework is applied to a transmission control unit (TCU) case study. The current TCU design prevents remanufacturing due to the sealant-based component joinery, which complicates disassembly and risks damaging the printed circuit board (PCB). After defining technical contradictions for the TCU product design and reviewing the suggested TRIZ principles to solve the conflicts, a cantilever snap-fit design alternative is developed. The economic feasibility of the snap-fit design is assessed by comparing the current and snap-fit design costs for three life cycles. The cost analysis demonstrates that the cost of the snap-fit design remains the same as the current design. Additionally, the snap-fit design offers substantial cost savings for three life cycles compared to the current design. We demonstrate how snap-fit design supports both environmental and economic sustainability.

42 ENGINEERING↗
DOE OSTI2020

Highly selective Si 3 N 4 /SiO 2 etching using an NF 3 /N 2 /O 2 /H 2 remote plasma. I. Plasma source and critical fluxes

Highly selective plasma etching of silicon nitride (Si 3 N 4 ), while not etching silicon dioxide (SiO 2 ), is a critical step in the fabrication of microelectronics devices. In many applications, this etching must be damage-free and isotropic, which then motivates the use of remote plasmas where the reactants interacting with the substrate are dominantly neutral species. In this paper and Paper II, mechanisms for highly selective Si 3 N 4 etching in remote plasmas are discussed based on results from experiments and simulations. It has been shown experimentally that high Si 3 N 4 /SiO 2 etch selectivity (≈380) can be achieved in the downstream effluent of an NF 3 /N 2 /O 2 /H 2 plasma. The authors found that H2 plays a principal role in the reaction mechanism as Si 3 N 4 /SiO 2 selectivity shows a sharp maximum as a function of the H2 flow rate. Based on this observation, and measured densities of F-atoms and H2 in the process chamber, a mechanism of selective Si 3 N 4 /SiO 2 etching is proposed in which HF molecules in vibrationally excited states accelerate etching reactions. A reaction mechanism for NF 3 /N 2 /O 2 /H 2 plasmas and its afterglow was developed to computationally determine the species densities and fluxes on the wafer level, validated by comparing with experimentally measured F-atom and H 2 densities. The calculated species densities and fluxes were used as input to an analytical model of Si 3 N 4 and SiO 2 etching based on the results of quantum chemistry simulations. This paper presents experimental results (etching data and species densities), the reaction mechanism for NF 3 /N 2 /O 2 /H 2 plasmas, and the results of simulations of gas phase chemistry. Quantum chemistry simulations of elementary etching reactions, description of the analytical model of Si 3 N 4 and SiO 2 etching, calculations of the etch rates, and Si 3 N 4 and SiO 2 selectivity with this model are presented in Paper II.

42 ENGINEERING↗
DOE OSTI2021

Self-assembled vertically aligned nanocomposite systems integrated on silicon substrate: Progress and future perspectives

Silicon (Si) integration is a critical step for implementing functional oxides into Si-based electronic devices, considering the advantages of low-cost and scalability of Si substrates. In the past decade, self-assembled vertically aligned nanocomposites (VANs) have attracted enormous research interest owing to their unique multifunctionalities and highly tunable physical properties as well as their one-step self-assembly process. Most of the VAN thin films have been reported to grow epitaxially on single crystalline oxide substrates, however, with limited systems reported on Si substrates due to the very large lattice mismatch between oxides and Si lattices. In this review, the current progress for self-assembled VAN systems integrated on a Si substrate is summarized. Buffer layered enabled VAN growth has been proven to be an effective approach for improving the epitaxial quality of oxide-oxide and oxide-metal VAN systems, while direct growth is preferred in nitride-metal VAN systems. Furthermore, the material versatility enables the Si-integrated VAN thin films to exhibit distinct physical properties such as ferromagnetism, ferroelectricity, magnetoresistance, as well as unique optical properties. The review also summarizes the various parameters for tuning the growth morphologies and corresponding properties for the VAN systems, including phase molar ratio, deposition frequency, buffer layers, background pressure, etc. Finally, future perspectives are discussed including new VAN system exploration, physical properties tuning, as well as design and fabrication of Si-based nanoelectronics and nanophotonic devices applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Size-dependent influence of NO x on the growth rates of organic aerosol particles

Atmospheric new-particle formation (NPF) affects climate by contributing to a large fraction of the cloud condensation nuclei (CCN). Highly oxygenated organic molecules (HOMs) drive the early particle growth and therefore substantially influence the survival of newly formed particles to CCN. Nitrogen oxide (NO x ) is known to suppress the NPF driven by HOMs, but the underlying mechanism remains largely unclear. Here, we examine the response of particle growth to the changes of HOM formation caused by NO x . We show that NO x suppresses particle growth in general, but the suppression is rather nonuniform and size dependent, which can be quantitatively explained by the shifted HOM volatility after adding NO x . By illustrating how NO x affects the early growth of new particles, a critical step of CCN formation, our results help provide a refined assessment of the potential climatic effects caused by the diverse changes of NO x level in forest regions around the globe.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Emergence of SARS-CoV-2 through recombination and strong purifying selection

COVID-19 has become a global pandemic caused by the novel coronavirus SARS-CoV-2. Understanding the origins of SARS-CoV-2 is critical for deterring future zoonosis, discovering new drugs, and developing a vaccine. We show evidence of strong purifying selection around the receptor binding motif (RBM) in the spike and other genes among bat, pangolin, and human coronaviruses, suggesting similar evolutionary constraints in different host species. We also demonstrate that SARS-CoV-2’s entire RBM was introduced through recombination with coronaviruses from pangolins, possibly a critical step in the evolution of SARS-CoV-2’s ability to infect humans. Similar purifying selection in different host species, together with frequent recombination among coronaviruses, suggests a common evolutionary mechanism that could lead to new emerging human coronaviruses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Verwey transition as evolution from electronic nematicity to trimerons via electron-phonon coupling

Understanding the driving mechanisms behind metal-insulator transitions (MITs) is a critical step toward controlling material’s properties. Since the proposal of charge order–induced MIT in magnetite Fe 3 O 4 in 1939 by Verwey, the nature of the charge order and its role in the transition have remained elusive. Recently, a trimeron order was found in the low-temperature structure of Fe 3 O 4 ; however, the expected transition entropy change in forming trimeron is greater than the observed value, which arises a reexamination of the ground state in the high-temperature phase. Here, we use electron diffraction to unveil that a nematic charge order on particular Fe sites emerges in the high-temperature structure of bulk Fe 3 O 4 and that, upon cooling, a competitive intertwining of charge and lattice orders arouses the Verwey transition. Our findings discover an unconventional type of electronic nematicity in correlated materials and offer innovative insights into the transition mechanism in Fe 3 O 4 via the electron-phonon coupling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

La- and Mn-doped cobalt spinel oxygen evolution catalyst for proton exchange membrane electrolysis

Discovery of earth-abundant electrocatalysts to replace iridium for the oxygen evolution reaction (OER) in a proton exchange membrane water electrolyzer (PEMWE) represents a critical step in reducing the cost for green hydrogen production. Here we report a nanofibrous cobalt spinel catalyst codoped with lanthanum (La) and manganese (Mn) prepared from a zeolitic imidazolate framework embedded in electrospun polymer fiber. The catalyst demonstrated a low overpotential of 353 millivolts at 10 milliamperes per square centimeter and a low degradation for OER over 360 hours in acidic electrolyte. A PEMWE containing this catalyst at the anode demonstrated a current density of 2000 milliamperes per square centimeter at 2.47 volts (Nafion 115 membrane) or 4000 milliamperes per square centimeter at 3.00 volts (Nafion 212 membrane) and low degradation in an accelerated stress test.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Soil microbiome interventions for carbon sequestration and climate mitigation

Mitigating climate change in soil ecosystems involves complex plant and microbial processes regulating carbon pools and flows. Here, we advocate for the use of soil microbiome interventions to help increase soil carbon stocks and curb greenhouse gas emissions from managed soils. Direct interventions include the introduction of microbial strains, consortia, phage, and soil transplants, whereas indirect interventions include managing soil conditions or additives to modulate community composition or its activities. Approaches to increase soil carbon stocks using microbially catalyzed processes include increasing carbon inputs from plants, promoting soil organic matter (SOM) formation, and reducing SOM turnover and production of diverse greenhouse gases. Marginal or degraded soils may provide the greatest opportunities for enhancing global soil carbon stocks. Among the many knowledge gaps in this field, crucial gaps include the processes influencing the transformation of plant-derived soil carbon inputs into SOM and the identity of the microbes and microbial activities impacting this transformation. As a critical step forward, we encourage broadening the current widespread screening of potentially beneficial soil microorganisms to encompass functions relevant to stimulating soil carbon stocks. Moreover, in developing these interventions, we must consider the potential ecological ramifications and uncertainties, such as incurred by the widespread introduction of homogenous inoculants and consortia, and the need for site-specificity given the extreme variation among soil habitats. Incentivization and implementation at large spatial scales could effectively harness increases in soil carbon stocks, helping to mitigate the impacts of climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Density Functional Theory Study of the Initial Stages of Cl-Induced Degradation of α -Cr 2 O 3 Passive Film

The ion exchange and point defect models are two prominent models describing the role of anions, such as chlorides, in the degradation of passive oxide films. Here the thermodynamic feasibility of critical steps of Cl-induced degradation of a hydroxylated α-Cr 2 O 3 (0001) surface, as proposed by these two models, are studied. Both models begin with Cl substitution of surface OH and H 2 O, which becomes less favorable with increasing Cl coverage. The initial stages of Cl-induced breakdown of the α-Cr 2 O 3 depend on Cl coverage and the presence of O vacancy near the surface as follows: (1) neither Cl insertion (supporting the ion exchange model) nor Cr vacancy formation (supporting the point defect model) is feasible at low Cl coverages except in the presence of O vacancies near the surface, where Cl insertion is thermodynamically feasible even at low coverages, (2) in the absence of O vacancies, Cr vacancy formation becomes feasible from 10/12 ML onwards whereas Cl insertion by exchange with subsurface OH only becomes feasible at full coverage. This implies that at higher coverages Cl-induced degradation first initiates through a vacancy formation mechanism, but both insertion and vacancy formation would be feasible at full coverage.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Arasteh, Kourosh [Lawrence Livermore National Labo↗
DOE OSTI2023

Strictly Enforcing Invertibility and Conservation in CNN-Based Super Resolution for Scientific Datasets

Abstract Recently, deep convolutional neural networks (CNNs) have revolutionized image “super resolution” (SR), dramatically outperforming past methods for enhancing image resolution. They could be a boon for the many scientific fields that involve imaging or any regularly gridded datasets: satellite remote sensing, radar meteorology, medical imaging, numerical modeling, and so on. Unfortunately, while SR-CNNs produce visually compelling results, they do not necessarily conserve physical quantities between their low-resolution inputs and high-resolution outputs when applied to scientific datasets. Here, a method for “downsampling enforcement” in SR-CNNs is proposed. A differentiable operator is derived that, when applied as the final transfer function of a CNN, ensures the high-resolution outputs exactly reproduce the low-resolution inputs under 2D-average downsampling while improving performance of the SR schemes. The method is demonstrated across seven modern CNN-based SR schemes on several benchmark image datasets, and applications to weather radar, satellite imager, and climate model data are shown. The approach improves training time and performance while ensuring physical consistency between the super-resolved and low-resolution data. Significance Statement Recent advancements in using deep learning to increase the resolution of images have substantial potential across the many scientific fields that use images and image-like data. Most image super-resolution research has focused on the visual quality of outputs, however, and is not necessarily well suited for use with scientific data where known physics constraints may need to be enforced. Here, we introduce a method to modify existing deep neural network architectures so that they strictly conserve physical quantities in the input field when “super resolving” scientific data and find that the method can improve performance across a wide range of datasets and neural networks. Integration of known physics and adherence to established physical constraints into deep neural networks will be a critical step before their potential can be fully realized in the physical sciences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Asc-Seurat: analytical single-cell Seurat-based web application

Abstract Background Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of transcriptomes, arising as a powerful tool for discovering and characterizing cell types and their developmental trajectories. However, scRNA-seq analysis is complex, requiring a continuous, iterative process to refine the data and uncover relevant biological information. A diversity of tools has been developed to address the multiple aspects of scRNA-seq data analysis. However, an easy-to-use web application capable of conducting all critical steps of scRNA-seq data analysis is still lacking. Summary We present Asc-Seurat, a feature-rich workbench, providing an user-friendly and easy-to-install web application encapsulating tools for an all-encompassing and fluid scRNA-seq data analysis. Asc-Seurat implements functions from the Seurat package for quality control, clustering, and genes differential expression. In addition, Asc-Seurat provides a pseudotime module containing dozens of models for the trajectory inference and a functional annotation module that allows recovering gene annotation and detecting gene ontology enriched terms. We showcase Asc-Seurat’s capabilities by analyzing a peripheral blood mononuclear cell dataset. Conclusions Asc-Seurat is a comprehensive workbench providing an accessible graphical interface for scRNA-seq analysis by biologists. Asc-Seurat significantly reduces the time and effort required to analyze and interpret the information in scRNA-seq datasets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Hydrothermal conditioning of oleaginous yeast cells to enable recovery of lipids as potential drop-in fuel precursors

Abstract Background Lipids produced using oleaginous yeast cells are an emerging feedstock to manufacture commercially valuable oleochemicals ranging from pharmaceuticals to lipid-derived biofuels. Production of biofuels using oleaginous yeast is a multistep procedure that requires yeast cultivation and harvesting, lipid recovery, and conversion of the lipids to biofuels. The quantitative recovery of the total intracellular lipid from the yeast cells is a critical step during the development of a bioprocess. Their rigid cell walls often make them resistant to lysis. The existing methods include mechanical, chemical, biological and thermochemical lysis of yeast cell walls followed by solvent extraction. In this study, an aqueous thermal pretreatment was explored as a method for lysing the cell wall of the oleaginous yeast Rhodotorula toruloides for lipid recovery. Results Hydrothermal pretreatment for 60 min at 121 °C with a dry cell weight of 7% (w/v) in the yeast slurry led to a recovery of 84.6 ± 3.2% (w/w) of the total lipids when extracted with organic solvents. The conventional sonication and acid-assisted thermal cell lysis led to a lipid recovery yield of 99.8 ± 0.03% (w/w) and 109.5 ± 1.9% (w/w), respectively. The fatty acid profiles of the hydrothermally pretreated cells and freeze-dried control were similar, suggesting that the thermal lysis of the cells did not degrade the lipids. Conclusion This work demonstrates that hydrothermal pretreatment of yeast cell slurry at 121 °C for 60 min is a robust and sustainable method for cell conditioning to extract intracellular microbial lipids for biofuel production and provides a baseline for further scale-up and process integration. Graphical abstract

09 BIOMASS FUELS↗
DOE OSTI2025

Data for "Viral-mediated Delivery of Morphogenic Regulators Enables Leaf Transformation in Sorghum bicolor (L.)"

Recent advancements in monocot transformation, using leaf tissue as explant material, have expanded the number of grass species capable of transgenesis. However, the complexity of vectors and reliance on inducible excision of essential morphogenic regulators have so far limited widespread application. Plant RNA viruses, such as Foxtail Mosaic Virus (FoMV), present a unique opportunity to express morphogenic regulator genes, such as Babyboom ( Bbm ), Wuschel2 ( Wus2 ), Wuschel-like homeobox protein 2a ( Wox2a ) and the GROWTH-REGULATING FACTOR 4 (GRF4) GRF-INTERACTING FACTOR 1 (GIF1) fusion protein transiently in leaf explant tissues. Furthermore, altruistic delivery of conventional and viral vectors could provide opportunities to simplify vectors used for leaf transformation—facilitating vector optimization and reducing reliance on morphogenic regulator gene integration. In this study, both viral and conventional T-DNA vectors were tested for their ability to promote the formation of embryonic calli, a critical step in leaf transformation protocols, using Sorghum bicolor leaf explants. Although conventional leaf transformation vectors yielded viable embryonic calli (43.2 ± 2.9%: GRF4-GIF1, 50.2 ± 3%: Bbm/Wus2), altruistic conventional vectors employing the GRF4-GIF1 morphogenic regulator resulted in improved efficiencies (61.3 ± 4.7%). Altruistic delivery was further enhanced with the use of viral vectors employing both GRF4-GIF1 and Bbm/Wus2 regulators, resulting in 75.1 ± 2.3% and 79.2 ± 2.5% embryonic calli formation, respectively. Embryonic calli generated from both conventional and viral vectors produced shoots expressing fluorescent reporters, which were confirmed using molecular analysis. This work provides an important proof-of-concept for the use of both altruistic vectors and viral-expressed morphogenic regulators for improving plant transformation.

gene editing↗
DOE OSTI2024

Data for Hydrothermal Conditioning of Oleaginous Yeast Cells to Enable Recovery of Lipids as Potential Drop-in Fuel Precursors

Lipids produced using oleaginous yeast cells are an emerging feedstock to manufacture commercially valuable oleochemicals ranging from pharmaceuticals to lipid-derived biofuels. Production of biofuels using oleaginous yeast is a multistep procedure that requires yeast cultivation and harvesting, lipid recovery, and conversion of the lipids to biofuels. The quantitative recovery of the total intracellular lipid from the yeast cells is a critical step during the development of a bioprocess. Their rigid cell walls often make them resistant to lysis. The existing methods include mechanical, chemical, biological and thermochemical lysis of yeast cell walls followed by solvent extraction. In this study, an aqueous thermal pretreatment was explored as a method for lysing the cell wall of the oleaginous yeast Rhodotorula toruloides for lipid recovery. Hydrothermal pretreatment for 60 min at 121 °C with a dry cell weight of 7% (w/v) in the yeast slurry led to a recovery of 84.6 ± 3.2% (w/w) of the total lipids when extracted with organic solvents. The conventional sonication and acid-assisted thermal cell lysis led to a lipid recovery yield of 99.8 ± 0.03% (w/w) and 109.5 ± 1.9% (w/w), respectively. The fatty acid profiles of the hydrothermally pretreated cells and freeze-dried control were similar, suggesting that the thermal lysis of the cells did not degrade the lipids. This work demonstrates that hydrothermal pretreatment of yeast cell slurry at 121 °C for 60 min is a robust and sustainable method for cell conditioning to extract intracellular microbial lipids for biofuel production and provides a baseline for further scale-up and process integration.

Conversion↗
DOE OSTI2023

Quantifying high-dimensional spatial entanglement with a single-photon-sensitive time-stamping camera

High-dimensional entanglement is a promising resource for quantum technologies. Being able to certify it for any quantum state is essential. However, to date, experimental entanglement certification methods are imperfect and leave some loopholes open. Using a single-photon-sensitive time-stamping camera, we quantify high-dimensional spatial entanglement by collecting all output modes and without background subtraction, two critical steps on the route toward assumptions-free entanglement certification. We show position-momentum Einstein–Podolsky–Rosen (EPR) correlations and quantify the entanglement of formation of our source to be larger than 2.8 along both transverse spatial axes, indicating a dimension higher than 14. Our work overcomes important challenges in photonic entanglement quantification and paves the way toward the development of practical quantum information processing protocols based on high-dimensional entanglement.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Novel perspective on a conventional technique: Impact of ultra-low temperature on bacterial viability and protein extraction

Ultra-low temperature (ULT) storage of microbial biomass is routinely practiced in biological laboratories. However, there is very little insight regarding the effects of biomass storage at ULT and the structure of the cell envelope, on cell viability. Eventually, these aspects influence bacterial cell lysis which is one of the critical steps for biomolecular extraction, especially protein extraction. Therefore, we studied the effects of ULT-storage (-80°C) on three different bacterial platforms: Escherichia coli , Bacillus subtilis and the cyanobacterium Synechocystis sp. PCC 6803. By using a propidium iodide assay and a modified MTT assay we determined the impact of ULT storage on cellular viability. Subsequently, the protein extraction efficiency was determined by analyzing the amount of protein released following the storage. The results successfully established that longer the ULT-storage time lower is the cell viability and larger is the protein extraction efficiency. Interestingly, E . coli and B . subtilis exhibited significant reduction in cell viability over Synechocystis 6803. This indicates that the cell membrane structure and composition may play a major role on cell viability in ULT storage. Interestingly, E . coli exhibited concomitant increase in cell lysis efficiency resulting in a 4.5-fold increase (from 109 μg/ml of protein on day 0 to 464 μg/ml of protein on day 2) in the extracted protein titer following ULT storage. Furthermore, our investigations confirmed that the protein function, tested through the extraction of fluorescent proteins from cells stored at ULT, remained unaltered. These results established the plausibility of using ULT storage to improve protein extraction efficiency. Towards this, the impact of shorter ULT storage time was investigated to make the strategy more time efficient to be adopted into protocols. Interestingly, E . coli transformants expressing mCherry yielded 2.7-fold increase (93 μg/mL to 254 μg/mL) after 10 mins, while 4-fold increase (380 μg/mL) after 120 mins of ULT storage in the extracted soluble protein. We thereby substantiate that: (1) the storage time of bacterial cells in -80°C affect cell viability and can alter protein extraction efficiency; and (2) exercising a simple ULT-storage prior to bacterial cell lysis can improve the desired protein yield without impacting its function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Quantum-enhanced detection of viral cDNA via luminescence resonance energy transfer using upconversion and gold nanoparticles

Abstract The COVID-19 pandemic has profoundly impacted global economies and healthcare systems, revealing critical vulnerabilities in both. In response, our study introduces a sensitive and highly specific detection method for cDNA, leveraging Luminescence Resonance Energy Transfer (LRET) between upconversion nanoparticles (UCNPs) and gold nanoparticles (AuNPs), and achieves a detection limit of 242 fM for SARS-CoV-2 cDNA. This innovative sensing platform utilizes UCNPs conjugated with one primer and AuNPs with another, targeting the 5′ and 3′ ends of the SARS-CoV-2 cDNA, respectively, enabling precise differentiation of mismatched cDNA sequences and significantly improving detection specificity. Through rigorous experimental analysis, we established a quenching efficiency range from 10.4 % to 73.6 %, with an optimal midpoint of 42 %, thereby demonstrating the superior sensitivity of our method. Our work uses SARS-CoV-2 cDNA as a model system to demonstrate the potential of our LRET-based detection method. This proof-of-concept study highlights the adaptability of our platform for future diagnostic applications. Instrumental validation confirms the synthesis and formation of AuNPs, addressing the need for experimental verification of the preparation of nanomaterial. Our comparative analysis with existing SARS-CoV-2 detection methods revealed that our approach provides a low detection limit and high specificity for target cDNA sequences, underscoring its potential for targeted COVID-19 diagnostics. This study demonstrates the superior sensitivity and adaptability of using UCNPs and AuNPs for cDNA detection, offering significant advances in rapid, accessible diagnostic technologies. Our method, characterized by its low detection limit and high precision, represents a critical step forward in developing next-generation biosensors for managing current and future viral outbreaks. By adjusting primer sequences, this platform can be tailored to detect other pathogens, contributing to the enhancement of global healthcare responsiveness and infectious disease control.

Esmaeili, Shahriar [Institute for Quantum Science ↗