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At least 199 records · Page 11

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon sparsity can be addressed by collecting approximately 200 photons per pixel; this, however, requires long acquisitions and, as such, suboptimal imaging rates. Here, we introduce near-zero photon bioimaging, a method that operates at kHz rates and 10,000-fold lower irradiance than standard microscopy. To achieve this level of performance, we uniquely combined a judiciously designed epifluorescence microscope enabling ultralow background levels and AI that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon bioimaging captures the structure of multicellular and subcellular features with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.

AI↗

Physics-assisted generative adversarial network for X-ray tomography

X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori , deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.

47 OTHER INSTRUMENTATION↗

Fast quantum ghost imaging with a single-photon-sensitive time-stamping camera

Quantum ghost imaging (QGI) leverages correlations between entangled photon pairs to reconstruct an image using light that has never physically interacted with an object. Despite extensive research interest, this technique has long been hindered by slow acquisition speeds, due to the use of raster-scanned detectors or the slow response of intensified cameras. Here, we utilize a single-photon-sensitive time-stamping camera to perform QGI at ultra-low-light levels with rapid data acquisition and processing times, achieving high-resolution and high-contrast images in under 1 min. Our work addresses the trade-off between image quality, optical power, data acquisition time, and data processing time in QGI, paving the way for practical applications in biomedical and quantum-secured imaging.

Mavian, Alex (ORCID:0000000279448830)↗

Fabrication and testing of high-performance all-metal neutron guides and axisymmetric mirrors by electrochemical replication

Neutron scattering is one of the most useful methods of studying the structure of matter, with applications to biomedical, structural, magnetic and energy-related materials. Neutron-scattering instruments are installed around research reactors or accelerator-based neutron sources, and neutron guides are critical components of these facilities. They are neutron-transport optical devices consisting of state-of-the-art mirrors often tens of meters long. Here we demonstrate a novel fabrication method of all-metallic neutron guides and axisymmetric mirrors by electroplating from precision mandrels. The process allows for the fabrication of single-piece all-metal guides of prismatic and axisymmetric shapes. We also demonstrate supermirror guides and axisymmetric focusing supermirrors produced with the same technology. We present the fabrication and tests of the multilayer-coated replicated guides and optic and show that the mandrel is reproduced with high fidelity and reliability. Furthermore, such supermirror optics will provide game-changing improvements in neutron techniques.

36 MATERIALS SCIENCE↗

Non-Ionizing Millimeter Waves Non-Thermal Radiation of Saccharomyces cerevisiae—Insights and Interactions

Non-ionizing millimeter-waves (MMW) interact with cells in a variety of ways. Here the inhibited cell division effect was investigated using 85–105 GHz MMW irradiation within the International Commission on Non-Ionizing Radiation Protection (ICNIRP) non-thermal 20 mW/cm 2 safety standards. Irradiation using a power density of about 1.0 mW/cm 2 SAR over 5–6 h on 50 cells/μL samples of Saccharomyces cerevisiae model organism resulted in 62% growth rate reduction compared to the control (sham). The effect was specific for 85–105 GHz range and was energy- and cell density-dependent. Irradiation of wild type and Δrad52 (DNA damage repair gene) deleted cells presented no differences of colony growth profiles indicating non-thermal MMW treatment does not cause permanent genetic alterations. Dose versus response relations studied using a standard horn antenna (~1.0 mW/cm 2 ) and compared to that of a compact waveguide (17.17 mW/cm 2 ) for increased power delivery resulted in complete termination of cell division via non-thermal processes supported by temperature rise measurements. We have shown that non-thermal MMW radiation has potential for future use in treatment of yeast related diseases and other targeted biomedical outcomes.

59 BASIC BIOLOGICAL SCIENCES↗

Magnetoimpedance Biosensors and Real-Time Healthcare Monitors: Progress, Opportunities, and Challenges

A small DC magnetic field can induce an enormous response in the impedance of a soft magnetic conductor in various forms of wire, ribbon, and thin film. Also known as the giant magnetoimpedance (GMI) effect, this phenomenon forms the basis for the development of high-performance magnetic biosensors with magnetic field sensitivity down to the picoTesla regime at room temperature. Over the past decade, some state-of-the-art prototypes have become available for trial tests due to continuous efforts to improve the sensitivity of GMI biosensors for the ultrasensitive detection of biological entities and biomagnetic field detection of human activities through the use of magnetic nanoparticles as biomarkers. In this review, we highlight recent advances in the development of GMI biosensors and review medical devices for applications in biomedical diagnostics and healthcare monitoring, including real-time monitoring of respiratory motion in COVID-19 patients at various stages. We also discuss exciting research opportunities and existing challenges that will stimulate further study into ultrasensitive magnetic biosensors and healthcare monitors based on the GMI effect.

60 APPLIED LIFE SCIENCES↗

The Saga of Endocrine FGFs

Fibroblast growth factors (FGFs) are cell-signaling proteins with diverse functions in cell development, repair, and metabolism. The human FGF family consists of 22 structurally related members, which can be classified into three separate groups based on their action of mechanisms, namely: intracrine, paracrine/autocrine, and endocrine FGF subfamilies. FGF19, FGF21, and FGF23 belong to the hormone-like/endocrine FGF subfamily. These endocrine FGFs are mainly associated with the regulation of cell metabolic activities such as homeostasis of lipids, glucose, energy, bile acids, and minerals (phosphate/active vitamin D). Endocrine FGFs function through a unique protein family called klotho. Two members of this family, α-klotho, or β-klotho, act as main cofactors which can scaffold to tether FGF19/21/23 to their receptor(s) (FGFRs) to form an active complex. There are ongoing studies pertaining to the structure and mechanism of these individual ternary complexes. These studies aim to provide potential insights into the physiological and pathophysiological roles and therapeutic strategies for metabolic diseases. Herein, we provide a comprehensive review of the history, structure–function relationship(s), downstream signaling, physiological roles, and future perspectives on endocrine FGFs.

59 BASIC BIOLOGICAL SCIENCES↗

Iron Oxide Nanorings and Nanotubes for Magnetic Hyperthermia: The Problem of Intraparticle Interactions

Magnetic interactions can play an important role in the heating efficiency of magnetic nanoparticles. Although most of the time interparticle magnetic interactions are a dominant source, in specific cases such as multigranular nanostructures intraparticle interactions are also relevant and their effect is significant. In this work, we have prepared two different multigranular magnetic nanostructures of iron oxide, nanorings (NRs) and nanotubes (NTs), with a similar thickness but different lengths (55 nm for NRs and 470 nm for NTs). In this way, we find that the NTs present stronger intraparticle interactions than the NRs. Magnetometry and transverse susceptibility measurements show that the NTs possess a higher effective anisotropy and saturation magnetization. Despite this, the AC hysteresis loops obtained for the NRs (0–400 Oe, 300 kHz) are more squared, therefore giving rise to a higher heating efficiency (maximum specific absorption rate, SARmax = 110 W/g for the NRs and 80 W/g for the NTs at 400 Oe and 300 kHz). These results indicate that the weaker intraparticle interactions in the case of the NRs are in favor of magnetic hyperthermia in comparison with the NTs.

magnetic hyperthermia↗

Nonclassical Crystallization Pathway in Biomolecular Self-assembly

Biomolecular self-assembly plays a vital role in synthetic and biological material systems and has therefore attracted tremendous interest due to its great potential for applications in biomedical and tissue engineering, biosensing, materials science, and nanotechnology. Although there have been numerous studies focusing on these systems’ designs, structures, and functions, little attention has been given to the mechanisms by which they nucleate. In this chapter, we review some recent advances in understanding the nucleation pathways of self-assembling biomolecular systems. We will mainly focus on crystallization via nonclassical nucleation mechanisms, which involve the addition of oligomers and more complex species or passage through transient metastable states, including dense liquids, amorphous clusters, and crystalline polymorphs. However, some cases that go through classical pathways are also introduced and discussed.

Chen, Jiajun↗

Modulating the Pseudoelastic Response of Nitinol Using Ion Implantation

This work explores whether ion beam modification can be used to modulate the austenite to martensite phase transformation in Nickel-Titanium (NiTi), thereby achieving novel or localized transformation properties in near-surface regions. We report this could provide alternatives to laser shot peening or other surface treatment methods and possibly expand applications in biomedical, aerospace, and other fields. Irradiation induces defects and internal stress that can serve as nucleation and/or pinning sites for the phase transformation. Thus, it can augment more convention- al approaches, including alloying, severe mechanical work, grain size reduction, and precipitation of coherent precipitates. A range of outcomes is possible in principle, including a shift of the critical stress or temperature for onset of the transformation, linearization, reduction of hysteresis, stabilization, and extent of transformation strain.

36 MATERIALS SCIENCE↗

Nitinol Electroslag Remelting: Initial Slag Study

Nitinol’s unique shape memory and super-elastic properties make it attractive for many applications in biomedical, automotive, aerospace, industrial refrigeration, and waste heat reclamation industries. The fatigue performance is limited however, by non-metallic inclusions (NMI) formed during the traditional VIM/VAR or multi-VAR process. ESR is a potential alternative melt process and is commonly used to refine ingot chemistry and enhance the metallurgical structure of many superalloys and specialty steels, but no study on ESR of Nitinol has been reported to date. The key to establishing a successful ESR process is selecting a compatible slag. In this study, several different slag chemistries are evaluated for their compatibility with Nitinol. One slag chemistry is chosen, along with a control, to produce four Nitinol ESR ingots. Pursuit of a novel slag chemistry for adequate Nitinol refinement is discussed.

Fezi, Kyle↗

Cost and Time Effective Lithography of Reusable Millimeter Size Bone Tissue Replicas With Sub‐15 nm Feature Size on A Biocompatible Polymer

Abstract The ability to replicate the microenvironment of biological tissues creates unique biomedical possibilities for stem cell applications. Current fabrication methods are limited by either the control on feature size and shape, or by the throughput and size of the replicas. Here, a novel platform is reported that combines thermal scanning probe lithography (tSPL) with innovative methodologies for the low‐cost and high‐throughput nanofabrication of large area quasi‐3D bone tissue replicas with high fidelity, sub‐15 nm lateral precision, and sub‐2 nm vertical resolution. This bio‐tSPL platform features a biocompatible polymer resist that withstands multiple cell culture cycles, allowing the reuse of the replicas, further decreasing costs and fabrication times. The as‐fabricated replicas support the culture and proliferation of human induced mesenchymal stem cells, which display broad therapeutic and biomedical potential. Furthermore, it is demonstrated that bio‐tSPL can be used to nanopattern the bone tissue replicas with amine groups, for subsequent tissue‐mimetic biofunctionalization. The achieved level of time and cost‐effectiveness, as well as the cell compatibility of the replicas, make bio‐tSPL a promising platform for the production of tissue‐mimetic replicas to study stem cell‐tissue microenvironment interactions, test drugs, and ultimately harness the regenerative capacity of stem cells and tissues for biomedical applications.

Liu, Xiangyu↗

Stereochemically‐Controlled Fluorinated Copolymers for Selectively Permeable Barrier Applications

Selective oxygen permeability coupled with low water vapor transmission is essential for biomedical and packaging applications requiring controlled oxygen flux under humid conditions. However, most high‐performance barrier polymers depend on perfluoroalkyl substances (PFAS), whose persistence and regulatory restrictions limit their long‐term applicability. We designed a series of stereocontrolled thiol‐yne‐based polyesters, including both fluorinated and non‐fluorinated variants, for selective oxygen permeability with considerable water barrier performance. Tailoring polymer crystallinity and morphology tuned both oxygen transport and mechanical properties. Fluorinated polymers demonstrated enhanced hydrophobicity and water resistance while maintaining oxygen diffusivity within a range relevant to oxygen‐sensing applications. Structure–property relationships were elucidated through small‐ and wide‐angle X‐ray scattering, revealing semi‐crystalline domains influenced by fluorine content and dithiol chain length. Barrier performance was rigorously evaluated via water vapor transmission rate and dynamic vapor sorption, showing reduced water uptake with increasing dithiol monomer length and crystallinity. In conclusion, this work introduces a PFAS‐free alternative to conventional barrier materials and establishes a tunable materials platform with potential relevance for biomedical devices and packaging systems requiring controlled oxygen permeability.

36 MATERIALS SCIENCE↗

Collaborative Research Proposal: Time Resolved Optical Emissions Spectroscopy and Laser Induced Fluorescence Spectroscopy of Nanosecond Pulsed Discharges in a Gas-Liquid Water Film Reactor

Electrical discharge plasma formed in contact with liquid water is of interest for a wide range of applications in chemical, biomedical, agricultural, electrical, and materials science and engineering. Such plasma reactors are of very timely importance since they also have significant disinfection capability by inactivating bacteria, viruses and other pathogens. Many types of plasma sources including those driven by AC, DC, RF, microwave, and pulsed electrical power supplies coupled to a wide range of different electrode configurations and reactor designs have been developed and explored which contact the plasma with liquid water. Recent roadmaps have recommended that further work is needed to develop our understanding of the fundamental chemical and physical process which occur at the interface of non-thermal plasma with liquid water solutions in order to advance this large diversity of applications which ultimately depend upon efficient production of key reactive chemical species. There is a wide range of interacting factors that affect the chemical reactions that occur in the plasma, in the liquid phase, and at the interface. These factors ultimately govern the key reactive chemical species formed and used in the various applications and they include a) the reactor design and input parameters, b) the discharge and transport processes, c) the plasma properties, and d) the resulting chemical reactions. For pulsed discharges, the power supply design and output parameters control the applied voltage, frequency, rise time, and width (duration) of the applied pulses. The reactor design involves specification of the gas-liquid contacting methods, electrode gap distance, reactor volume and shape, and gas and liquid flow rates achievable. The gas and liquid compositions as well as the liquid properties such as pH and conductivity are also of key importance in determination of the resulting chemical reactions. In addition, the important plasma properties include plasma gas temperature, electron density, electron energy (distribution), and size of the plasma channels which are all affected by the discharge and transport properties. Many studies have focused on specific aspects of these various processes. Of particular important and relevance to the proposed work is the utilization of nanosecond pulses to generate plasma in gas-liquid systems, liquid bubbles, underwater, and in gases. Recent advances in nanosecond pulses provide significant advantages in utilization with liquid water. For example, fast rise time and short pulses are less sensitive to water conductivity, such short pulses may provide advantages in fast temporal quenching of the plasma, and fundamental analysis of pulse properties, including pulse shape and width, may be facilitated by investigation of single filamentary fast pulses. Many studies have also dealt with the role of the gas composition on formation of reactive oxygen species (ROS) (i.e., hydroxyl radicals – ·OH, hydrogen peroxide – H 2 O 2 , various atomic oxygen species, ozone-O 3 , hydroperoxyl radicals HO 2 ·) and reactive nitrogen species (RNS) (i.e., nitrogen oxides – NO, NO 2 , N 2 O - collectively termed NO X , nitrite-NO 2 - , nitrate-NO 3 - , peroxynitrite-ONOO - ). The hydroxyl radical is the critical species in many chemical oxidation reactions for chemical degradation of toxic compounds in water and gases and for synthesis of some compounds. The mixture of various nitrogen oxide species is important for many biochemical and biological processes involved in biomedical and agricultural applications including disinfection and fertilizer production. The present proposal focuses on determination of the effects of time resolved electron density and hydroxyl radicals on plasma chemical reactions through collaboration with the Princeton Collaborative Low Temperature Plasma Research Facility (PCRF) at the Princeton Plasma Physics Laboratory (PPPL). In order to further investigate the role of the plasma generated electrons and hydroxyl radicals on the overall formation of the key species including hydrogen peroxide, hydroxyl radicals, and nitrogen oxides, the proposed work, thus seeks to determine high resolution time resolved electron density by optical emissions spectroscopy and time resolved hydroxyl radicals using laser induced fluorescence in the nanosecond discharge reactor. The combination of data on electron density and hydroxyl radical concentration will be utilized in the present work to more fully characterize the chemical reaction processes in this system and to advance the design, development, and operation of such chemical reactors for a wide range of applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗

Evaluation of AddUp Precision L-PBF Technology for Tooling and Other Industrial Applications

ORNL (Contractor) and AddUp Inc. (Participant) collaborated to enable the adoption of laser powder bed fusion for tooling applications, critical for reshoring the manufacturing sector in the U.S. Most of the work on laser powder bed fusion has focused on high value materials such as Ti-6Al-4V and Inconel 718 for niche applications in aerospace, biomedical and energy sectors. In this collaboration, ORNL and AddUp worked laser powder bed fusion of maraging steels for tooling applications while leveraging the unique ability of the AddUp laser powder bed fusion system to deposit fine powders. We demonstrated that the AddUp technology is capable of depositing complex artifacts and features such as overhangs within desired tolerances. We also showed that direct aging of the deposited parts result in simultaneously higher strength and elongation compared to the conventional two step heat treatments, which could result in significant energy savings. Finally, we developed thermos-kinetic models to enable design of heat treatments for optimal material properties compared to the energy intensive empirical methods currently used. The report summarizes the detailed findings of this collaboration.

36 MATERIALS SCIENCE↗

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

dynamic analysis↗