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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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8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning models for segmentation and classification of cyanobacterial cells

Abstract Timelapse microscopy has recently been employed to study the metabolism and physiology of cyanobacteria at the single-cell level. However, the identification of individual cells in brightfield images remains a significant challenge. Traditional intensity-based segmentation algorithms perform poorly when identifying individual cells in dense colonies due to a lack of contrast between neighboring cells. Here, we describe a newly developed software package called Cypose which uses machine learning (ML) models to solve two specific tasks: segmentation of individual cyanobacterial cells, and classification of cellular phenotypes. The segmentation models are based on the Cellpose framework, while classification is performed using a convolutional neural network named Cyclass. To our knowledge, these are the first developed ML-based models for cyanobacteria segmentation and classification. When compared to other methods, our segmentation models showed improved performance and were able to segment cells with varied morphological phenotypes, as well as differentiate between live and lysed cells. We also found that our models were robust to imaging artifacts, such as dust and cell debris. Additionally, the classification model was able to identify different cellular phenotypes using only images as input. Together, these models improve cell segmentation accuracy and enable high-throughput analysis of dense cyanobacterial colonies and filamentous cyanobacteria.

Huffine, Clair A.↗
DOE OSTI2023

Cellulose Nanocrystals’ Role in Critical Mineral Beneficiation: Dual Aggregate-Dispersant Behavior Supports Environmentally Benign Nickel Processing

Environmentally unsustainable and toxic chemical flocculants and dispersants from nickel (Ni) processing contribute to industrial effluents that greatly impact biodiversity and aquatic life. Despite the industry’s efforts to reduce its ecological footprint—primarily due to the lack of commercially available biodegradable, environmentally benign, and non-toxic reagents—these reagents continue to harm natural ecosystems. Chemicals used during the processing operations often target a specific unit operation that negatively affects downstream operations. In this study, we discover the concentration-dependent behavior of cellulose nanocrystals (CNCs) as a dispersant and flocculant, avoiding the use of various harmful chemicals in the nickel processing stages. Electrophoretic, optical brightfield microscopy, and quartz crystal microbalance-dissipation studies detected charge neutralization behavior by renewable and biodegradable CNCs, which can greatly benefit Ni processing operations. Microflotation experiments demonstrated that CNCs enhanced Ni recovery from 62 to 77 wt.% and concentrate grade from 15 to 20 wt.%. Settling and turbidity studies demonstrated the dual flocculant-dispersant behavior of CNCs resulting from the alignment of CNC fibers along the octahedral brucite basal and amphoteric edge plane of serpentine. CNCs can be used as dispersants during froth flotation to improve Ni beneficiation and as post-processing flocculants for tailings management and dewatering, which is one of the major environmental and social concerns facing the mining and mineral processing industry. This study paves the way for CNCs to be used as flocculants and dispersants in a range of industries from healthcare to pharmaceuticals to semiconductor devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A bi-channel aided stitching of atomic force microscopy images

Microscopy is an essential tool in scientific research, enabling the visualization of structures at micro- and nanoscale resolutions. However, the field of microscopy often encounters limitations in field-of-view (FOV), restricting the amount of sample that can be imaged in a single capture. To overcome this limitation, image stitching techniques have been developed to seamlessly merge multiple overlapping images into a single, high-resolution composite. The images collected from microscope need to be optimally stitched before accurate physical information can be extracted from post analysis. However, the existing stitching tools either struggle to stitch images together when the microscopy images are feature sparse or cannot address all the transformations of images when performing image stitching. To address these issues, we propose a bi-channel aided feature-based image stitching method and demonstrate its use on Atomic Force Microscopy (AFM) generated Pantoea sp. YR343 biofilm and PTO thin film sample images as experimental data. The topographical channel image of AFM data captures the morphological details of the sample, and a stitched topographical image is desired for researchers. We utilize the amplitude and phase channels of AFM data to maximize the matching features and to estimate the position of the original topographical images and show that the proposed bi-channel aided stitching method outperforms the traditional direct stitching approach in AFM topographical image stitching task. Here, we demonstrated the application on AFM, but similar approaches could be employed of optical microscopy with brightfield and fluorescence channels. We believe this proposed workflow can serve as a valuable augmentation strategy for microscopy image stitching tasks and will benefit the experimentalist to avoid erroneous analysis and discovery due to incorrect stitching.

Atomic force microscopy↗
DOE OSTI2026

Ray-tracing image simulations of transparent objects with complex shape and inhomogeneous refractive index

Optical images of transparent three-dimensional objects can be different from a replica of the object’s cross section in the image plane, due to refraction at the surface or in the body of the object. Simulations of the object’s image are thus needed for the visualization and validation of physical models. We report ray tracing image simulations that achieved high physical fidelity, reproducing optical behaviors and image features not rendered in previous studies. We replicated brightfield microscopy images of drops with complex shapes, and images of pressure and shock waves traveling inside them. For high physical fidelity, the simulations must replicate the spatial and angular distribution of illumination rays, and both the experiment and the simulation must be designed for accurate optical modeling. The simulations are highly sensitive to the properties of the drops and can be used to diagnose and refine fluid dynamics models. The simulated images can also be optimized to extract multiple 3D properties from experimental images. Compared to specialized single-shot 3D imaging methods, this approach has the advantage that it preserves the experimental simplicity, the high resolution, and the visual interpretability characteristic to basic optical imaging. The techniques introduced here are directly applicable to optical microscopy, so they can be used in other fields, such as microfluidics and biology, to expand the type and the accuracy of three-dimensional information that can be extracted from basic optical images.

Cavitation↗
DOE OSTI2025

Multi-contrast machine learning improves schistosomiasis diagnostic performance

Schistosomiasis currently affects over 250 million people and remains a public health burden despite ongoing global control efforts. Conventional microscopy is a practical tool for diagnosis and screening ofSchistosoma haematobium, but identification of eggs requires a skilled microscopist. Here we present a machine learning (ML)-based strategy for automated detection ofS. haematobiumthat combines two imaging contrasts, brightfield (BF) and darkfield (DF), to improve diagnostic performance. We collected BF and DF images of urine samples, many of them containingS. haematobiumeggs, during two different field studies in Côte d’Ivoire using a mobile phone-based microscope, the SchistoScope. We then trained separate egg-detection ML models and compared the patient-level performance of BF and DF models alone to combinations of BF and DF models, using annotations from trained microscopists as the gold standard. We found that models trained on DF images, and almost all BF and DF combinations, performed significantly better than models trained on BF images only. When models were trained on images from the first field study (n = 349 patients, 748 images of each contrast), patient-level classification performance on patient images from the second study (n = 375 patients, 752 images of each contrast) met the WHO Diagnostic Target Product Profile (TPP) sensitivity and specificity for the monitoring and evaluation use case (sensitivity for all models and combinations was >75% when evaluated at a confidence score threshold that resulted in specificity >96.5%). When we used images from both field studies for the training set, performance of the models was improved. Overall, this work shows that the use of DF and BF increases the performance of ML models on images from devices with low-cost optics, while retaining the portability, power, and time-to-results of the WHO’s diagnostic TPP. DF requires no additional sample preparation and does not increase the complexity of the imaging system. It thus offers a practical means to improve performance of automated diagnostics forS. haematobiumas well as other microscopy-based diagnostics.

Infectious Diseases↗
DOE OSTI2021

In Vivo Entombment of Bacteria and Fungi during Calcium Oxalate, Brushite, and Struvite Urolithiasis

Human kidney stones form via repeated events of mineral precipitation, partial dissolution, and reprecipitation, which are directly analogous to similar processes in other natural and manmade environments, where resident microbiomes strongly influence biomineralization. High-resolution microscopy and high-fidelity metagenomic (microscopy-to-omics) analyses, applicable to all forms of biomineralization, have been applied to assemble definitive evidence of in vivo microbiome entombment during urolithiasis. Stone fragments were collected from a randomly chosen cohort of 20 patients using standard percutaneous nephrolithotomy (PCNL). Fourier transform infrared (FTIR) spectroscopy indicated that 18 of these patients were calcium oxalate (CaOx) stone formers, whereas one patient formed each formed brushite and struvite stones. This apportionment is consistent with global stone mineralogy distributions. Stone fragments from seven of these 20 patients (five CaOx, one brushite, and one struvite) were thin sectioned and analyzed using brightfield (BF), polarization (POL), confocal, super-resolution autofluorescence (SRAF), and Raman techniques. DNA from remaining fragments, grouped according to each of the 20 patients, were analyzed with amplicon sequencing of 16S rRNA gene sequences (V1–V3, V3–V5) and internal transcribed spacer (ITS1, ITS2) regions. Bulk-entombed DNA was sequenced from stone fragments in 11 of the 18 patients who formed CaOx stones, and the patients who formed brushite and struvite stones. These analyses confirmed the presence of an entombed low-diversity community of bacteria and fungi, including Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria, and Aspergillus niger. Bacterial cells approximately 1 μm in diameter were also optically observed to be entombed and well preserved in amorphous hydroxyapatite spherules and fans of needle-like crystals of brushite and struvite. In conclusion, these results indicate a microbiome is entombed during in vivo CaOx stone formation. Similar processes are implied for brushite and struvite stones. This evidence lays the groundwork for future in vitro and in vivo experimentation to determine how the microbiome may actively and/or passively influence kidney stone biomineralization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Minnesota Solar Pathways: Illuminating Pathways to 10% Solar (Final Technical Report)

The Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, was a three-year project designed to explore least-risk, best-value strategies for meeting the State of Minnesota’s solar goals. As part of this aim, the Pathways Team modeled renewable generation costs, examined ways to streamline interconnection, and evaluated technologies that can increase solar hosting capacity on the distribution grid. Midway through the project period, the Pathways Team expanded the scope of study using the same modeling framework from the Minnesota Solar Potential Analysis to estimate and compare generation costs for high renewables penetration scenarios across the Midcontinent Independent System Operator (MISO) region. The analysis concludes that it can be cheaper to overbuild solar + wind and curtail surplus than to store all generation. In addition, the analysis of individual MISO subregions compared with the whole region highlights the opportunities of enabling market potential compared with impact of ongoing transmission constraints. Beyond the analysis of future scenarios, the partnerships initiated and supported through this project underscored the ongoing need for work with stakeholders to address key issues, particularly around siting, and leverage symbiotic strategies to meet state goals.

14 SOLAR ENERGY↗