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Results for “cell imaging”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Improved defect analysis of Gallium Arsenide solar cells using image enhancement

A new technique has been developed to capture, digitize, and enhance the image of light emission from a forward biased direct bandgap solar cell. Since the forward biased light emission from a direct bandgap solar cell has been shown to display both qualitative and quantitative information about the solar cell's performance and its defects, signal processing techniques can be applied to the light emission images to identify and analyze shunt diodes. Shunt diodes are of particular importance because they have been found to be the type of defect which is likely to cause failure in a GaAs solar cell. The presence of a shunt diode can be detected from the light emission by using a photodetector to measure the quantity of light emitted at various current densities. However, to analyze how the shunt diodes affect the quality of the solar cell the pattern of the light emission must be studied. With the use of image enhancement routines, the light emission can be studied at low light emission levels where shunt diode effects are dominant.

Kilmer, Louis C.↗
NASA NTRS2008

Dependence of Adaptive Cross-correlation Algorithm Performance on the Extended Scene Image Quality

Recently, we reported an adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels between two extended-scene sub-images captured by a Shack-Hartmann wavefront sensor. It determines the positions of all extended-scene image cells relative to a reference cell in the same frame using an FFT-based iterative image-shifting algorithm. It works with both point-source spot images as well as extended scene images. We have demonstrated previously based on some measured images that the ACC algorithm can determine image shifts with as high an accuracy as 0.01 pixel for shifts as large 3 pixels, and yield similar results for both point source spot images and extended scene images. The shift estimate accuracy of the ACC algorithm depends on illumination level, background, and scene content in addition to the amount of the shift between two image cells. In this paper we investigate how the performance of the ACC algorithm depends on the quality and the frequency content of extended scene images captured by a Shack-Hatmann camera. We also compare the performance of the ACC algorithm with those of several other approaches, and introduce a failsafe criterion for the ACC algorithm-based extended scene Shack-Hatmann sensors.

wave-front sensing and control↗
NASA NTRS1999

Model system for plant cell biology: GFP imaging in living onion epidermal cells

The ability to visualize organelle localization and dynamics is very useful in studying cellular physiological events. Until recently, this has been accomplished using a variety of staining methods. However, staining can give inaccurate information due to nonspecific staining, diffusion of the stain or through toxic effects. The ability to target green fluorescent protein (GFP) to various organelles allows for specific labeling of organelles in vivo. The disadvantages of GFP thus far have been the time and money involved in developing stable transformants or maintaining cell cultures for transient expression. In this paper, we present a rapid transient expression system using onion epidermal peels. We have localized GFP to various cellular compartments (including the cell wall) to illustrate the utility of this method and to visualize dynamics of these compartments. The onion epidermis has large, living, transparent cells in a monolayer, making them ideal for visualizing GFP. This method is easy and inexpensive, and it allows for testing of new GFP fusion proteins in a living tissue to determine deleterious effects and the ability to express before stable transformants are attempted.

NASA Discipline Plant Biology↗
DOE OSTI2023

Single-cell multimodal imaging uncovers energy conversion pathways in biohybrids

Microbe–semiconductor biohybrids, which integrate microbial enzymatic synthesis with the light-harvesting capabilities of inorganic semiconductors, have emerged as promising solar-to-chemical conversion systems. Improving the electron transport at the nano–bio interface and inside cells is important for boosting conversion efficiencies, yet the underlying mechanism is challenging to study by bulk measurements owing to the heterogeneities of both constituents. Here we develop a generalizable, quantitative multimodal microscopy platform that combines multi-channel optical imaging and photocurrent mapping to probe such biohybrids down to single- to sub-cell/particle levels. We uncover and differentiate the critical roles of different hydrogenases in the lithoautotrophic bacterium Ralstonia eutropha for bioplastic formation, discover this bacterium’s surprisingly large nanoampere-level electron-uptake capability, and dissect the cross-membrane electron-transport pathways. Furthermore, this imaging platform, and the associated analytical framework, can uncover electron-transport mechanisms in various types of biohybrid, and potentially offers a means to use and engineer R. eutropha for efficient chemical production coupled with photocatalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Multimodal single-Cell/ Particle imaging and engineering for energy conversion in bacteria (Final Technical Report)

Hybrid inorganic-microbial systems have emerged as a potentially transformative approach to combine the light-harvesting capability of inorganic semiconductors and the ability of microbes to orchestrate complex chemical transformations. The objective of this collaborative research is to combine quantum materials synthesis, bacterial synthetic biology, and multimodal single-entity imaging to quantitatively study how hybrid QD-bacteria systems convert light to value chemicals at the single-to-sub cell level, with the ultimate goal of gaining insights to guide the engineering of QDs and bacterial genetics for more efficient bioenergy conversion. The final technical report summarizes our achievements toward this objective.

09 BIOMASS FUELS↗
DOE OSTI2025

Automated segmentation of soft X-ray tomography: Native cellular structure with submicron resolution at high-throughput for whole-cell quantitative imaging in yeast

Soft X-ray tomography (SXT) is an invaluable tool for quantitatively analyzing cellular structures at suboptical isotropic resolution. However, it has traditionally depended on manual segmentation, limiting its scalability for large datasets. Here, we leverage a deep learning-based autosegmentation pipeline to segment and label cellular structures in hundreds of cells across three Saccharomyces cerevisiae strains. This task-based pipeline uses manual iterative refinement to improve segmentation accuracy for key structures, including the cell body, nucleus, vacuole, and lipid droplets, enabling high-throughput and precise phenotypic analysis. Using this approach, we quantitatively compared the three-dimensional (3D) whole-cell morphometric characteristics of wild-type, VPH1-GFP, and vac14 strains, uncovering detailed strain-specific cell and organelle size and shape variations. We show the utility of SXT data for precise 3D curvature analysis of entire organelles and cells and detection of fine morphological features using surface meshes. Our approach facilitates comparative analyses with high spatial precision and statistical throughput, uncovering subtle morphological features at the single-cell and population level. This workflow significantly enhances our ability to characterize cell anatomy and supports scalable studies on the mesoscale, with applications in investigating cellular architecture, organelle biology, and genetic research across diverse biological contexts.

Chen, Jianhua [Lawrence Berkeley National Laborato↗
DOE OSTI2024

Harnessing quantum light for microscopic biomechanical imaging of cells and tissues

The biomechanical properties of cells and tissues play an important role in our fundamental understanding of the structures and functions of biological systems at both the cellular and subcellular levels. Recently, Brillouin microscopy, which offers a label-free spectroscopic means of assessing viscoelastic properties in vivo, has emerged as a powerful way to interrogate those properties on a microscopic level in living tissues. However, susceptibility to photodamage and photobleaching, particularly when high-intensity laser beams are used to induce Brillouin scattering, poses a significant challenge. This article introduces a transformative approach designed to mitigate photodamage in biological and biomedical studies, enabling nondestructive, label-free assessments of mechanical properties in live biological samples. By leveraging quantum-light-enhanced stimulated Brillouin scattering (SBS) imaging contrast, the signal-to-noise ratio is significantly elevated, thereby increasing sample viability and extending interrogation times without compromising the integrity of living samples. The tangible impact of this methodology is evidenced by a notable three-fold increase in sample viability observed after subjecting the samples to three hours of continuous squeezed-light illumination, surpassing the traditional coherent light-based approaches. The quantum-enhanced SBS imaging holds promise across diverse fields, such as cancer biology and neuroscience where preserving sample vitality is of paramount significance. By mitigating concerns regarding photodamage and photobleaching associated with high-intensity lasers, this technological breakthrough expands our horizons for exploring the mechanical properties of live biological systems, paving the way for an era of research and clinical applications.

Li, Tian↗
DOE OSTI2026

A unified large language model–based framework for heterogeneous PV image diagnosis

With advances in imaging technologies, modern photovoltaic (PV) systems generate large volumes of heterogeneous image data, including visible, electroluminescence (EL), and infrared (IR) images. Existing PV image analysis models, particularly deep learning approaches, are typically task-specific and lack cross-modality generalization. To address this limitation, this paper proposes an open-source large language model (LLM)–based unified framework for heterogeneous PV image diagnostics. Through task-aware diagnostic prompting, the framework enables analysis of visible, EL, and IR images within a single pipeline, supporting both zero-shot and few-shot inference and binary and multiclass classification. It is compatible with state-of-the-art multimodal LLMs, including ChatGPT, Gemini, Claude, Qwen, and CLIP. The framework is evaluated on PV module condition classification (clean, soiling, snow, hail, and bird droppings) using visible images, cell crack detection using EL images, and hotspot detection using IR images. GPT-5.1 in few-shot mode achieves the best performance, with classification accuracy exceeding 97.3%. Open-source models such as Qwen and CLIP also deliver competitive results on visible images (around 90% accuracy), though their performance is more limited on EL and IR modalities. On the full ELPV dataset, the framework achieves 83.5% zero-shot accuracy, within 2.8% of the supervised CNN baseline, confirming scalability to larger benchmarks. Practical aspects such as reproducibility, response latency, and confidence estimation are systematically analyzed. The framework operates across PV image modalities without modality- or task-specific training, making it well suited as a rapid pre-screening tool to support downstream detailed diagnostics. A benchmark dataset of diverse labeled PV images is also released.

Li, Baojie↗
DOE OSTI2025

Exploring Capability of Multimodal Foundation Model for Image-based Fault Detection of Photovoltaic Modules

Multimodal Foundation Model (MFM), like ChatGPT and Gemini, have emerged as powerful tools for their exceptional natural language processing capabilities and their emerging potential in image analysis. This paper investigates the application of MFMs for photovoltaic (PV) fault detection through image analysis, focusing on ChatGPT 4.0 and Gemini 1.5 Pro. Three types of PV images and the corresponding common PV faults are detected: bird droppings using visible images, cell cracks via electroluminescence (EL) images, and hotspots using infrared (IR) images. Among the two models, Gemini 1.5 Pro demonstrated superior performance, achieving near-perfect results with an average F1 score of 0.97, consistently outperforming ChatGPT 4.0 in accuracy and reliability. Unlike traditional machine learning (ML) models, MFMs can operate in a zero shot manner that does not require additional training by the user, and the input images are not limited by size, angle, scope, or PV technology. The strong adaptability and user-friendliness make MFM a promising tool for analyzing PV images and advancing health monitoring for PV modules.

Li, Baojie↗
NASA NTRS1981

Improved cell for water-vapor electrolysis

Continuous-flow electrolytic cells decompose water vapor in steam and room air into hydrogen and oxygen. Sintered iridium oxide catalytic anode coating yields dissociation rates hundredfold greater than those obtained using platinum black. Cell consists of two mirror-image cells, with dual cathode sandwiched between two anodes. Gas traverses serpentine channels within cell and is dissociated at anode. Oxygen mingles with gas stream, while hydrogen migrates through porous matrix and is liberated as gas at cathode.

Aylward, J. R.↗
NASA NTRS1987

Recombination imaging of III-V solar cells

An imaging technique based on the radiative recombination of minority carriers in forward-biased solar cells has been developed for characterization of III-V solar cells. When used in mapping whole wafers, it has helped identify three independent loss mechanisms (broken grid lines, shorting defects, and direct-to-indirect bandgap transitions), all of which resulted in lower efficiencies. The imaging has also led to improvements in processing techniques to reduce the occurrence of broken gridlines as well as surface defects. The ability to visualize current mechanisms in solar cells is an intuitive tool which is powerful in its simplicity.

Virshup, G. F.↗
NASA NTRS2002

Overexpression of neurofilament H disrupts normal cell structure and function

Studying exogenously expressed tagged proteins in live cells has become a standard technique for evaluating protein distribution and function. Typically, expression levels of experimentally introduced proteins are not regulated, and high levels are often preferred to facilitate detection. However, overexpression of many proteins leads to mislocalization and pathologies. Therefore, for normative studies, moderate levels of expression may be more suitable. To understand better the dynamics of intermediate filament formation, transport, and stability in a healthy, living cell, we inserted neurofilament heavy chain (NFH)-green fluorescent protein (GFP) fusion constructs in adenoviral vectors with tetracycline (tet)-regulated promoters. This system allows for turning on or off the synthesis of NFH-GFP at a selected time, for a defined period, in a dose-dependent manner. We used this inducible system for live cell imaging of changes in filament structure and cell shape, motility, and transport associated with increasing NFH-GFP expression. Cells with low to intermediate levels of NFH-GFP were structurally and functionally similar to neighboring, nonexpressing cells. In contrast, overexpression led to pathological alterations in both filament organization and cell function. Copyright 2002 Wiley-Liss, Inc.

Non-NASA Center↗
NASA NTRS2007

Adaptive Cross-correlation Algorithm and Experiment of Extended Scene Shack-Hartmann Wavefront Sensing

We have developed a new, adaptive cross-correlation (ACC) algorithm to estimate with high accuracy the shift as large as several pixels in two extended-scene images captured by a Shack-Hartmann wavefront sensor (SH-WFS). It determines the positions of all of the extended-scene image cells relative to a reference cell using an FFT-based iterative image shifting algorithm. It works with both point-source spot images as well as extended scene images. We have also set up a testbed for extended0scene SH-WFS, and tested the ACC algorithm with the measured data of both point-source and extended-scene images. In this paper we describe our algorithm and present out experimental results.

Shack - Hartmann sensor↗