Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration
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36 MATERIALS SCIENCE↗
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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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Interlaboratory Study for Characterizing Monoclonal Antibodies by Top-Down and Middle-Down Mass Spectrometry
The Consortium for Top-Down Proteomics (www.topdownproteomics.org) launched the present study to assess the state of top-down mass spectrometry (TD MS) and middle-down mass spectrometry (MD MS) for characterizing monoclonal antibody (mAb) primary structures, including their modifications. To meet the needs of the rapidly growing therapeutic antibody market, it is important to develop analytical strategies to characterize the heterogeneity of a therapeutic product’s primary structure reliably and reproducibly. The major objective of the present study was to determine whether current TD/MD MS technologies and protocols can add value to the more commonly employed bottom-up approaches, such as confirming protein integrity, sequencing variable domains, ruling out artefacts, and revealing modifications and their locations. A panel of three mAbs was selected and centrally provided to twenty laboratories worldwide for the analysis: Sigma mAb standard (SiLuLite), NIST mAb standard, and the therapeutic mAb Herceptin (trastuzumab). A variety of different MS instrument platforms and ion dissociation techniques were employed. Overall, the present study confirms that MD/TD MS strategies are valuable and readily available tools in laboratories worldwide. They provide complementary information to the bottom-up approach and can add unique information, which may be crucial for comprehensive mAb characterization. The current limitations, as well as possible solutions to overcome them, are also outlined. One of the limitations revealed by the results of the present study is that the expert knowledge in both experiment and data analysis is indispensable to practice TD/MD MS, but the number of TD/MD MS experts is still very low.
59 BASIC BIOLOGICAL SCIENCES↗
Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images
Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ~80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. Further, we show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.
60 APPLIED LIFE SCIENCES↗
Application of a long short-term memory for deconvoluting conductance contributions at charged ferroelectric domain walls
Ferroelectric domain walls are promising quasi-2D structures that can be leveraged for miniaturization of electronics components and new mechanisms to control electronic signals at the nanoscale. Despite the significant progress in experiment and theory, however, most investigations on ferroelectric domain walls are still on a fundamental level, and reliable characterization of emergent transport phenomena remains a challenging task. Here, we apply a neural-network-based approach to regularize local I ( V )-spectroscopy measurements and improve the information extraction, using data recorded at charged domain walls in hexagonal (Er 0.99 ,Zr 0.01 )MnO 3 as an instructive example. Using a sparse long short-term memory autoencoder, we disentangle competing conductivity signals both spatially and as a function of voltage, facilitating a less biased, unconstrained and more accurate analysis compared to a standard evaluation of conductance maps. The neural-network-based analysis allows us to isolate extrinsic signals that relate to the tip-sample contact and separating them from the intrinsic transport behavior associated with the ferroelectric domain walls in (Er 0.99 ,Zr 0.01 )MnO 3 . Our work expands machine-learning-assisted scanning probe microscopy studies into the realm of local conductance measurements, improving the extraction of physical conduction mechanisms and separation of interfering current signals.
36 MATERIALS SCIENCE↗
Machine learning for automated experimentation in scanning transmission electron microscopy
Abstract Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.
36 MATERIALS SCIENCE↗
Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction
Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significance, however, is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms, which often results in a low signal-to-noise ratio and the superposition of multiple signals that are challenging to deconvolute. Here we apply scanning electron diffraction to explore local polar distortions in the uniaxial ferroelectric Er(Mn,Ti)O 3 . Using a custom-designed convolutional autoencoder with bespoke regularization, we demonstrate that subtle variations in the scattering signatures of ferroelectric domains, domain walls, and vortex textures can readily be disentangled with statistical significance and separated from extrinsic contributions due to, e.g., variations in specimen thickness or bending. The work demonstrates a pathway to quantitatively measure symmetry-breaking distortions across large areas, mapping structural changes at interfaces and topological structures with nanoscale spatial resolution.
36 MATERIALS SCIENCE↗
Top-down mass spectrometry of native proteoforms and their complexes: a community study
The combination of native electrospray ionization with top-down fragmentation in mass spectrometry (MS) allows simultaneous determination of the stoichiometry of noncovalent complexes and identification of their component proteoforms and cofactors. Although this approach is powerful, both native MS and top-down MS are not yet well standardized, and only a limited number of laboratories regularly carry out this type of research. Further, to address this challenge, the Consortium for Top-Down Proteomics initiated a study to develop and test protocols for native MS combined with top-down fragmentation of proteins and protein complexes across 11 instruments in nine laboratories. Here we report the summary of the outcomes to provide robust benchmarks and a valuable entry point for the scientific community.
47 OTHER INSTRUMENTATION↗
Top-down proteomics
Proteoforms arising from posttranslational modifications, genetic polymorphisms, and RNA splice variants, play a pivotal role as the key drivers in biology. Thus, a comprehensive understanding of proteoforms is essential for unraveling the intricacies of biological systems and bridging the gap between genotype and phenotype. By analyzing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and presents a next-generation approach for deciphering protein function, uncovering disease mechanisms, and advancing precision medicine. This Primer embarks on a journey into the world of TDP by encapsulating its historical context, underlying principles, recent advances, and an outlook on the future of TDP. The experimental section navigates instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques, and data collection. Results decipher raw data, visualize intact protein spectra, unravel data analysis, and explain proteoform identification, characterization, and quantitation, as well as statistical analysis. Various applications of TDP spanning the human proteoform project, biomedical, biopharmaceutical, and clinical applications are described. These are complemented by discussions on measurement reproducibility, limitations, and a forward-looking perspective outlining uncharted waters where the field can advance, and potential exciting future applications of TDP.
Roberts, David S.↗
Designing high energy density flow batteries by tuning active-material thermodynamics
With the cost of renewable energy near parity with fossil fuels, energy storage is paramount. We report a breakthrough on a bioinspired NRFB active-material, with greatly improved solubility, and place it in a predictive theoretical framework.
25 ENERGY STORAGE↗
Optimization framework for redox flow battery electrodes with improved microstructural characteristics
This research aims to advance the field of vanadium redox flow batteries (VRFBs) by introducing a pioneering approach to optimize the microstructural characteristics of carbon cloth electrodes.
25 ENERGY STORAGE↗
Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak
Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. Here, in this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Reliable edge machine learning hardware for scientific applications
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for performance validation in experimental software frameworks, verifying those ML models are robust under extreme quantization and pruning, and enabling ultra-fine-grained model inspection for efficient fault tolerance. We discuss approaches to developing and validating reliable algorithms at the scientific edge under such strict latency, resource, power, and area requirements in extreme experimental environments. We study metrics for developing robust algorithms, present preliminary results and mitigation strategies, and conclude with an outlook of these and future directions of research towards the longer-term goal of developing autonomous scientific experimentation methods for accelerated scientific discovery.
Baldi, Tommaso↗
The Human Proteoform Project: Defining the human proteome
Proteins are the primary effectors of function in biology, and thus, complete knowledge of their structure and properties is fundamental to deciphering function in basic and translational research. The chemical diversity of proteins is expressed in their many proteoforms, which result from combinations of genetic polymorphisms, RNA splice variants, and posttranslational modifications. This knowledge is foundational for the biological complexes and networks that control biology yet remains largely unknown. We propose here an ambitious initiative to define the human proteome, that is, to generate a definitive reference set of the proteoforms produced from the genome. Several examples of the power and importance of proteoform-level knowledge in disease-based research are presented along with a call for improved technologies in a two-pronged strategy to the Human Proteoform Project.
59 BASIC BIOLOGICAL SCIENCES↗
Photo-response of the $N=Z$ nucleus $^{24}$Mg
The electric E1 and magnetic M1 dipole responses of the N = Z nucleus 24 Mg were investigated in an inelastic photon scattering experiment. The 13.0 MeV electrons, which were used to produce the unpolarised bremsstrahlung in the entrance channel of the 24 Mg(γ,γ') reaction, were delivered by the ELBE accelerator of the Helmholtz-Zentrum Dresden-Rossendorf. The collimated bremsstrahlung photons excited one J π = 1 – , four J π = 1 + , and six J π = 2 + states in 24 Mg. De-excitation γ rays were detected using the four high-purity germanium detectors of the γELBE setup, which is dedicated to nuclear resonance fluorescence experiments. In the energy region up to 13.0 MeV a total B(M1)↑ = 2.7(3) $μ$$^{2}_{N}$ is observed, but this N = Z nucleus exhibits only marginal E1 strength of less than ΣB(E1)↑≤ 0.61 × 10 –3 e 2 fm 2 . The B(Π1, $1$$^{π}_{i}$ → $2$$^{+}_{1}$)/B(Π1,$1$$^{π}_{i}$ → $0$$^{+}_{gs}$) branching ratios in combination with the expected results from the Alaga rules demonstrate that K is a good approximative quantum number for 24 Mg. The use of the known ρ 2 (E0, $0$$^{+}_{2}$ → $0$$^{+}_{gs}$) strength and the measured B(M1,1 + → $0$$^{+}_{2}$)/B(M1,1 + → $0$$^{+}_{gs}$) branching ratio of the 10.712 MeV 1 + level allows, in a two-state mixing model, an extraction of the difference Δ$β$$^{2}_{2}$ between the prolate ground-state structure and shape-coexisting superdeformed structure built upon the 6432-keV $0$$^{+}_{2}$ level.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Exploring the Effectiveness of Carbon Cloth Electrodes for All-Vanadium Redox Flow Batteries
Vanadium redox flow batteries (VRFBs) have shown to be a promising technology for integrating intermittent renewable energy sources into the existing electrical grid. Incorporation of carbon cloth electrodes into VRFB is an area of interest for their enhanced electrochemical performance, however, issues with performance degradation throughout the duration of the experiment persist. This study investigates the performance evolution of carbon cloth electrodes during VRFB cycling to build a hypothesis on possible reasons for the declining performance. Electrochemical impedance spectroscopy and polarization curve measurements are used in conjunction to monitor the electrode degradation and shed light on the effectiveness of carbon cloth electrodes during extended cycling experiments. A detailed investigation into the structure of the carbon cloth electrodes before and after cycling, via several material characterization tests, provides insight needed to determine an explanation for the increasing resistance. The structural integrity and surface morphology of the carbon cloth electrodes are evaluated to compare the electrode before and after cycling, displaying any changes to the electrode due to cycling. Durability of hydrophilicity during RFB cycling is found to be a key feature for future carbon cloth electrode design efforts.
25 ENERGY STORAGE↗
Binder-Coated Carbon Cloth Electrodes for All-Vanadium Redox Flow Batteries
Vanadium redox flow batteries (VRFBs) are a promising solution for integrating intermittent renewable energy sources into the existing power grid. However, enhancing the electrochemical performance of VRFBs is critical for their widespread adoption in grid-scale energy storage. This study investigates the impact of adding a porous binder to a carbon-cloth electrode, with a focus on optimizing thermal activation conditions. The electrochemical performance of the binder-coated electrodes compared to uncoated electrodes is evaluated through electrochemical impedance spectroscopy, polarization curve measurements, and charge-discharge cycling. The surface morphology and structural integrity of the binder-coated electrodes at each activation stage are examined using various material characterization techniques to assess the effects of thermal activation. The results are benchmarked against the experiments using non-coated electrodes to determine the performance improvements offered by the binder coating. Notably, the study reveals that binder-coated electrodes exhibit significantly lower resistance and improved efficiency compared to their uncoated counterparts, with optimal activation conditions enhancing performance metrics crucial for VRFB applications. These findings provide valuable insights for further optimizing electrode design and activation strategies, advancing the development of more efficient VRFB systems for large-scale energy storage.
Caiado, Ashley A.↗
Fungal mat growth and leaf colonization at the TRACE warming experiment, Mar - Aug 2024, Luquillo, Puerto Rico
This data package contains processed measurements on the growth of litter mat-forming fungi and the time to leaf colonization at the Tropical Responses to Altered Climate Experiment (TRACE). Located near the Sabana Field Research Station in Luquillo, Puerto Rico, the TRACE site is located in a mature, closed-canopy tropical rainforest within the Luquillo Experimental Forest (LEF). These data quantify fungal mat growth and the time to leaf colonization of fungi species Gymnopus johnstonii and Marasmius crinis-equi. The experiment was conducted in ambient (control) and experimentally warmed plots (4°C above ambient) during spring and summer periods to assess how litter mat-forming fungi respond to a range of environmental conditions of tropical wet forests. The data files include tables of relative fungal mat growth rates, time to leaf colonization, averages of soil temperature (°C), and number of dry days before leaf attachment. The data are stored in comma-separated values (CSV) format and viewable with any text editor, spreadsheet, or statistical software (e.g., R, Python, Excel). Associated metadata describe plot identifiers, measurement descriptions, and processing steps.
Agaric fungi↗
Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. Here, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.
36 MATERIALS SCIENCE↗