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At least 451 records · Page 25

Large-scale continuous carbon/glass fiber additive-compression molded composites

Additive Manufacturing (AM) or 3-D printing has advanced from small-scale desktop printers to large-scale printers. Most of the present large-scale printers utilize feedstock materials in the form of pellets to create composite structures. To create structurally robust composite parts, reinforcements in the form of short fibers (carbon or glass) are often used to impart mechanical properties to the printed parts. However, poor mechanical properties in Z-direction and high porosity of pellets-based printed composites compared to composite manufactured using traditional methods are serious concerns. The authors report a combined approach in the present work, where fiber reinforced composites are printed with a high-throughput continuous fiber deposition method followed by a secondary compression molding process. A specially designed end-effector mounted on a robotic arm is used to print composite preforms. Continuous comingled fibers (Thermofiber 12K CF-PA12, Thermofiber 12K S2-PA12, and Hybrid Thermofiber 12K CF-PA12+PEEK PA6) embedded in the thermoplastic nylon matrix are printed to create composite preform plaques. The printed preforms were further compression molded (CM) using a hydraulic hot-press to create highly consolidated composite parts. The mechanical properties of the continuous fiber composites produced by this combined approach are improved significantly due to the highly aligned continuous fibers and reduced porosity. Flexural strength, flexural modulus, and tensile modulus of AM-CM Thermofiber 12K CF-PA12 UD sample were 615.37 MPa, 75.65 GPa, and 122.23 GPa, respectively.

Kumar, Vipin↗

Investigation of Nanoparticle Degradation in Hydrogen Fuel Cell Systems through Automated Electron Microscopy

Proton exchange membrane fuel cells (PEMFC) are promising devices for the deployment of hydrogen-powered heavy-duty vehicles, providing a higher efficiency for similar driving range and fueling time than the existing ones. However, PEMFCs still encounter durability challenges mainly due to catalyst degradation in the cathode. Mitigating these performance losses requires a better understanding of the degradation mechanisms under heavy-duty accelerated stress tests (ASTs) [1]. Scanning transmission electron microscopy (STEM) combined with energy dispersive X-ray spectroscopy (EDS) are key tools for the analysis of Pt and PtCo nanoparticle size, spatial distribution and composition [2]. Electron tomography is also used to determine the rate and type of degradation of catalyst nanoparticles as a function of their position on the carbon support. In this work, automated data acquisition software, paired with a custom Python code, have been used to study the effect of different accelerated stress tests (ASTs) on nanoparticle coarsening [2]. Figure 1 shows high-angle annular dark-field (HAADF)-STEM images and EDS maps comparing the cathodes of membrane electrode assemblies (MEAs) following an electrocatalyst AST performed under H2/N2 with that of the heavy-duty AST performed under H2/air. We will discuss how AST conditions affect considerably the spatial distribution of the nanoparticles across the electrode between the membrane and microporous layer. Although the median particle size increased more in the MEA aged under the heavy-duty AST, as determined using a high-throughput image analysis, the quantitative EDS measurements demonstrate that the electrocatalyst AST resulted in more Pt and Co dissolution from the cathode, which is another important indicator of electrocatalyst degradation. We will further present the impact of the relative humidity (% RH) on the degradation mechanisms demonstrated using the same approach. Electron tomography has been used to distinguish the Pt nanoparticles residing on the carbon support surface (exterior) from those within the pore structure (interior) in order to determine the relative stability of interior and exterior nanoparticles. As shown in Figure 2, we will compare the Pt catalyst particle size at the beginning of test (BOT) and end of test (EOT), and discuss the importance of automating the electron tomography workflow, i.e. acquisition, reconstruction, and visualization, to increase sampling and determine the standard deviation of these measurement. The outlook for utilizing low-dose cryo-tomography for limiting damage to the catalyst, support, and especially proton-conducting ionomer will also be discussed [3].

Amichi, Lynda↗

Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets

Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

42 ENGINEERING↗

Comparison of methodologies used to determine aromatic lignin unit ratios in lignocellulosic biomass

Abstract Background Multiple analytical methods have been developed to determine the ratios of aromatic lignin units, particularly the syringyl/guaiacyl (S/G) ratio, of lignin biopolymers in plant cell walls. Chemical degradation methods such as thioacidolysis produce aromatic lignin units that are released from certain linkages and may induce chemical changes rendering it difficult to distinguish and determine the source of specific aromatic lignin units released, as is the case with nitrobenzene oxidation methodology. NMR methods provide powerful tools used to analyze cell walls for lignin composition and linkage information. Pyrolysis-mass spectrometry methods are also widely used, particularly as high-throughput methodologies. However, the different techniques used to analyze aromatic lignin unit ratios frequently yield different results within and across particular studies, making it difficult to interpret and compare results. This also makes it difficult to obtain meaningful insights relating these measurements to other characteristics of plant cell walls that may impact biomass sustainability and conversion metrics for the production of bio-derived fuels and chemicals. Results The authors compared the S/G lignin unit ratios obtained from thioacidolysis, pyrolysis-molecular beam mass spectrometry (py-MBMS), HSQC liquid-state NMR and solid-state (ss) NMR methodologies of pine, several genotypes of poplar, and corn stover biomass. An underutilized approach to deconvolute ssNMR spectra was implemented to derive S/G ratios. The S/G ratios obtained for the samples did not agree across the different methods, but trends were similar with the most agreement among the py-MBMS, HSQC NMR and deconvoluted ssNMR methods. The relationship between S/G, thioacidolysis yields, and linkage analysis determined by HSQC is also addressed. Conclusions This work demonstrates that different methods using chemical, thermal, and non-destructive NMR techniques to determine native lignin S/G ratios in plant cell walls may yield different results depending on species and linkage abundances. Spectral deconvolution can be applied to many hardwoods with lignin dominated by S and G units, but the results may not be reliable for some woody and grassy species of more diverse lignin composition. HSQC may be a better method for analyzing lignin in those species given the wealth of information provided on additional aromatic moieties and bond linkages. Additionally, trends or correlations in lignin characteristics such as S/G ratios and lignin linkages within the same species such as poplar may not necessarily exhibit the same trends or correlations made across different biomass types. Careful consideration is required when choosing a method to measure S/G ratios and the benefits and shortcomings of each method discussed here are summarized.

09 BIOMASS FUELS↗

Emulation of Core Flight System Applications for Flight Software Development and Validation

The Mars Sample Return (MSR) campaign is an unprecedented attempt in the return of Martian samples back to Earth. The ascent from the surface will be performed by the Mars Ascent Vehicle (MAV), a critical element in the mission that National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) is developing. To this end, innovations in flight software development, verification, and validation are occurring. The MAV flight computer will run Core Flight System (cFS), an open-source software environment developed by NASA Goddard Space Flight Center (GSFC). NASA Marshall’s MAV Mission and Fault Management (M&FM) Team has implemented an emulation of two applications of this architecture: Limit Checker and Stored Command. Using an emulation of the functionalities of these applications allows for rapid prototyping of table-based algorithms. Further, M&FM is leveraging an in-house, low-fidelity but high-throughput State Analysis Model (SAM), an integrated MATLAB Stateflow Plant and Software model. This model is run in parallel with the cFS emulation for full flyout testing of the M&FM algorithms, verification of intent of these algorithms, and for future auto-generation of application-ingestible M&FM tables. The tables can then be delivered to the MAV Flight Software (FSW) team in a seamless process, reducing the cost of traditional FSW development and the risk of starting M&FM FSW development at later points in the NASA program life cycle.

Cody Wheeler↗

Emulation of Core Flight System Applications for Flight Software Development and Validation

The Mars Sample Return (MSR) campaign is an unprecedented attempt in the return of Martian samples back to Earth. The ascent from the surface will be performed by the Mars Ascent Vehicle (MAV), a critical element in the mission that National Aeronautics and Space Administration (NASA) Marshall Space Flight Center (MSFC) is developing. To this end, innovations in flight software development, verification, and validation are occurring. The MAV flight computer will run Core Flight System (cFS), an open-source software environment developed by NASA Goddard Space Flight Center (GSFC). NASA Marshall’s MAV Mission and Fault Management (M&FM) Team has implemented an emulation of two applications of this architecture: Limit Checker and Stored Command. Using an emulation of the functionalities of these applications allows for rapid prototyping of table-based algorithms. Further, M&FM is leveraging an in-house, low-fidelity but high-throughput State Analysis Model (SAM), an integrated MATLAB Stateflow Plant and Software model. This model is run in parallel with the cFS emulation for full flyout testing of the M&FM algorithms, verification of intent of these algorithms, and for future auto-generation of application-ingestible M&FM tables. The tables can then be delivered to the MAV Flight Software (FSW) team in a seamless process, reducing the cost of traditional FSW development and the risk of starting M&FM FSW development at later points in the NASA program life cycle.

Cody Wheeler↗

High pressure, high flow rate batch mixing apparatus for high throughput experiments

An automated, high pressure, high flow rate batch mixing apparatus has been designed and constructed for rapid, stable, and repeatable mixing of multiple gases and vapors. The apparatus operates as an intermittent batch mixer with cycles of topping off fresh mixture to maintain pressure in an accumulator tank until consumed in an experimental apparatus. At high duty cycles, the apparatus can also function at steady state. This style of mixing is suitable for experiments such as high repetition rate shock tubes and other devices with intermittent flow demands. It is compact and portable, facilitating use in locations such as synchrotron light sources. The entire apparatus is heated to permit the mixing of vapors from species with low volatilities. The apparatus is fully automated and runs for extended periods with the only intervention being to refresh reagent supplies. Here, the accuracy and repeatability of the apparatus were verified by periodic gas sampling and analysis with gas chromatography. Multi-component mixtures spanning a wide range of complexity, dilution, and volatility of constituents have been prepared. The compositions of the majority of the mixture were found to be stable over several filling cycles, repeatable, and with the proper calibration of set-point conditions, accurate. Challenges were encountered preparing a mixture from multi-component liquids, and potential solutions are discussed.

47 OTHER INSTRUMENTATION↗

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗

Hyperspectral radiance mapping and chromatic correction for temperature measurement in laser-heated diamond anvil cells

In laser-heated diamond anvil cell (DAC) experiments, the effective heated region typically decreases in size with increasing pressure, leading to steeper thermal gradients. Under these conditions, chromatic aberration in the optical path from sample to detector can significantly create bias in spectro-radiometric temperature measurement. We present a radiance-mapping approach using a hyperspectral camera that records 25 spectral channels spanning 605–875 nm at each pixel in a single exposure, providing spatially and spectrally resolved radiance in each frame. This enables chromatic effects to be recorded and corrected in data processing. We developed a procedure for hyperspectral mapping, involving per-camera calibration, crosstalk removal, measured spectral throughput functions, and optional sub-pixel co-registration to minimize chromatic distortion. The calibrated radiance maps are then used to derive temperature maps of the laser-heated hotspots. For smaller heating spots, the radiance mapping approach reveals chromatic shifts that conventional spectro-radiometric methods cannot quantify. Ambient-pressure heating experiments confirm accurate temperature retrieval. At high pressure, application of the hyperspectral system to a platinum-heating experiment at 12 GPa demonstrates stable temperature reconstruction under steep thermal gradients. Beyond mitigating chromatic aberrations, the ability to diagnose optical artifacts separately from emissivity variations during controlled test experiments or in situ suggests a path toward more rigorous spectral emissivity analysis and improved modeling of thermal transport in laser-heated DAC experiments.

47 OTHER INSTRUMENTATION↗

Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study

BACKGROUND:We previously established a 53-gene prognostic signature for overall survival (OS) of gastric cancer patients. This retrospective multi-center study aimed to develop a clinically applicable gene expression detection assay and to investigate the prognostic value of this signature. METHODS:A TCGA gastric adenocarcinoma cohort (TCGA-STAD) was used for comparing 53-gene signature with other gene signatures. A high-throughput mRNA hybridization gene expression assay was developed to quantify the expression of 53-genes in formalin-fixed paraffin-embedded tissues of 540 patients enrolled from three hospitals. 180 patents were randomly selected from two hospitals to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method together with Cox regression and Kaplan-Meier analysis, and the model was assessed on three validation cohorts. FINDINGS:In the evaluation phase, studies based on TCGA-STAD showed that the 53-gene signature was significantly superior to other three prognostic signatures and was independent of TCGA molecular subtypes and clinical factors. For clinical validation and utility, the prognostic scores were generated using the newly developed assay, which was reliable and sensitive, in 100 sampling training sets and were significantly associated with OS in 100 sampling validation sets. The scores were significantly associated with OS in three independent and combined validation cohorts, and in patients with stages II and III/IV. The multivariate Cox regression demonstrated that the prognostic power of the score was independent of clinical factors, consistent with those findings in the TCGA dataset. Finally, patients with good prognostic scores exhibited significantly a better 5-year OS rate from adjuvant FOLFOX chemotherapy after surgery than from other chemotherapies. INTERPRETATION:The 53-gene prognostic score system is clinically applicable for predicting the OS of patients independent of clinical factors in gastric cancers, which could also be a promising predictive biomarker for FOLFOX regimen. FUNDING:Chinese National Science and Technology, National Natural Science Foundation and Natural Science Foundation of Jiangsu Province.

60 APPLIED LIFE SCIENCES↗

The Feasibility of Tropospheric and Total Ozone Determination Using a Fabry-perot Interferometer as a Satellite-based Nadir-viewing Atmospheric Sensor

Monitoring of the global distribution of tropospheric ozone (O3) is desirable for enhanced scientific understanding as well as to potentially lessen the ill-health impacts associated with exposure to elevated concentrations in the lower atmosphere. Such a capability can be achieved using a satellite-based device making high spectral resolution measurements with high signal-to-noise ratios; this would enable observation in the pressure-broadened wings of strong O3 lines while minimizing the impact of undesirable signal contributions associated with, for example, the terrestrial surface, interfering species, and clouds. The Fabry-Perot Interferometer (FPI) provides high spectral resolution and high throughput capabilities that are essential for this measurement task. Through proper selection of channel spectral regions, the FPI optimized for tropospheric O3 measurements can simultaneously observe a stratospheric component and thus the total O3 column abundance. Decreasing stratospheric O3 concentrations may lead to an increase in biologically harmful solar ultraviolet radiation reaching the earth's surface, which is detrimental to health. In this research, a conceptual instrument design to achieve the desired measurement has been formulated. This involves a double-etalon fixed-gap series configuration FPI along with an ultra-narrow bandpass filter to achieve single-order operation with an overall spectral resolution of approximately .068 cm(exp -1). A spectral region of about 1 cm(exp -1) wide centered at 1054.73 cm(exp -1) within the strong 9.6 micron ozone infrared band is sampled with 24 spectral channels. Other design characteristics include operation from a nadir-viewing satellite configuration utilizing a 9 inch (diameter) telescope and achieving horizontal spatial resolution with a 50 km nadir footprint. A retrieval technique has been implemented and is demonstrated for a tropical atmosphere possessing enhanced tropospheric ozone amounts. An error analysis assessing the impact on retrieved O3 amounts of the most significant uncertainties associated with this particular measurement has been performed for several different types of atmospheres. Results show the proposed instrumentation to enable a good measurement of absolute ozone amounts and an even better determination of relative changes, with a range of accuracy to within 7.55 to 20.6 percent for integrated tropospheric amounts (and 1.99 to 4.02 percent for total O3 column abundance) and a corresponding range in precision to within 7.73 to 10.4 percent (and 3.30 to 3.95 percent for total O3 column abundance), for the atmospheric conditions considered.

Larar, Allen Maurice↗

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.

36 MATERIALS SCIENCE↗

High Throughput In-Line Coating Metrology Development for Solid Oxide Fuel Cell Manufacturing

Coatings play key roles in solid oxide fuel cell (SOFC) stack durability. For example, diffusion barrier coatings on Cr-containing interconnect and balance of plant (BOP) components protect electrodes from Cr poisoning over the long operational lifetimes (>10,000 hours) of the fuel cell stack. Common defects in coatings, such as cracks, pinholes, and porosity, result in a failure to protect the electrodes, resulting in shorter operational lifetime and thus higher cost. It is very unlikely, even in the best coating process, that all these defects can be mitigated, hence identifying critical defects in parts, and removing defective parts from production before they can damage the stack, becomes paramount. Furthermore, these quality control techniques must be operational in the production/assembly line (in-line), i.e., high throughput and non-destructive, and cost effective. Redox Power Systems, LLC (Redox) together with the National Renewable Energy Laboratory (NREL) developed much needed high throughput, in-line metrology techniques for protective coatings. The overall goal of the project is to lower cost while increasing robustness, reliability, and endurance of SOFC stacks. To accomplish this, we had several objectives, including: to identify key coating and substrate defects that lead to coating failure through the use of detailed characterization methods (e.g., microscopy, XRD, EDS, electrochemistry); to assess capabilities of in-line metrology techniques, e.g., optical profilometry (Redox) and thermography (NREL), to probe these defects, or evidence thereof; demonstrate long-term performance of “defect-free” protective coatings, as identified by in-line metrology, in solid oxide fuel cell (SOFC) stack operation. In the first part of this project, the ability to identify key defects expected to lead to coating and SOFC degradation using in-line metrology tools were evaluated. Coated interconnect samples with controlled defect types and populations were tested under conditions similar to SOFC operation, followed by detailed post-test analysis to reveal the defects responsible for observed degradation. In the second part of the project, the optimal in-line metrology techniques and methodologies were used to map the defect distribution in full-size interconnects with critical defects intentionally allowed to exist in some cases. These interconnects underwent SOFC testing for extended periods (up to ~3,000 hours) followed by post-test analysis to evaluate the effectiveness of in-line metrology techniques in mitigating MCO coating related degradation. Key accomplishments in this project included the following: Demonstrated ASR of < 0.05 ohm-cm 2 at 650 °C for 1,000 hours with low defect (determined by in-line metrology) interconnect samples (average ASR=37 milliohms-cm 2 after over 1,000 hours). Demonstrated that low defect coatings on interconnects (as screened using in-line metrology) have low volatilization of chromium at ~650 °C for 1,000 hours as detected using Cr-getter material (< 5 at% increase above baseline); 1022 hour duration tests under humidified, elevated temperature (750 °C rather than 650 °C) compared a base case against different coating thicknesses. Demonstrated capability to identify initial key defects of interest with in-line metrology techniques using up to 8 cm by 10 cm having coatings with and without intentional defects of interest using thermal imaging and optical profilometry. Correlated key defects identified using metrology techniques with observed coating performance (e.g., ASR and Cr volatility). Conducted several 4 cm by 4 cm cell tests using MCO-interconnects that were pre-screened using some of the metrology techniques developed in the project (e.g., optical profilometry). An analysis of ASR measurements were able to show that defect-free coatings resulted in the anticipated performance in the cell tests.

01 COAL, LIGNITE, AND PEAT↗

Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator

The abilities of plant biologists and breeders to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation, and particularly to collect this data at a high-throughput scale with low cost. Although deep learning methods have demonstrated unprecedented potential to automate plant phenotyping, these methods commonly rely on large training sets that can be time-consuming to generate. Intelligent algorithms have therefore been proposed to enhance the productivity of these annotations and reduce human efforts. We propose a high-throughput phenotyping system which features a Graphical User Interface (GUI) and a novel interactive segmentation algorithm: Semantic-Guided Interactive Object Segmentation (SGIOS). By providing a user-friendly interface and intelligent assistance with annotation, this system offers potential to streamline and accelerate the generation of training sets, reducing the effort required by the user. Our evaluation shows that our proposed SGIOS model requires fewer user inputs compared to the state-of-art models for interactive segmentation. As a case study of the use of the GUI applied for genetic discovery in plants, we present an example of results from a preliminary genome-wide association study (GWAS) of in planta regeneration in Populus trichocarpa (poplar). We further demonstrate that the inclusion of a semantic prior map with SGIOS can accelerate the training process for future GWAS, using a sample of a dataset extracted from a poplar GWAS of in vitro regeneration. The capabilities of our phenotyping system surpass those of unassisted humans to rapidly and precisely phenotype our traits of interest. The scalability of this system enables large-scale phenomic screens that would otherwise be time-prohibitive, thereby providing increased power for GWAS, mutant screens, and other studies relying on large sample sizes to characterize the genetic basis of trait variation. Our user-friendly system can be used by researchers lacking a computational background, thus helping to democratize the use of deep segmentation as a tool for plant phenotyping.

54 ENVIRONMENTAL SCIENCES↗

Evaluating Retention Index Score Assumptions to Refine GC–MS Metabolite Identification

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography–mass spectrometry (GC–MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, in this study, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RIs were matched to library RI with a generous window of ±35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive (TP), true negative, or unknown. Metabolites with at least 30 TP identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, and so on.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical metabolite-specific distributions improved the TP and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimize the effort to complete manual verification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Induction ultrafast sintering

This study proposes and demonstrates induction ultrafast sintering (IUS), which enables rapid densification of refractory and other materials via two contactless modalities: direct IUS (d-IUS), where heating occurs through electromagnetic coupling with the sample, and susceptor IUS (s-IUS), where heating is achieved indirectly via an induction-heated metal case. Ultrahigh heating rates of ∼75 to >450 °C/s and temperatures exceeding 2500 °C are readily achieved. Both d-IUS and s-IUS densify molybdenum to high densities within 120 s, with only ∼1–3 % porosity observed by image analysis. Similarly, 3 mol % yttria-stabilized zirconia (3YSZ) reaches ∼97 % relative density in 30 s via s-IUS. This study further demonstrates ultrafast reactive sintering of two difficult-to-sinter materials: a refractory compositionally complex alloy–carbide (RCCA–CCC) composite, NbMoTaW–(Nb 0.37 Mo 0.11 Ta 0.39 W 0.13 ) 2 C, using d-IUS, and a compositionally complex silicide (CCS), (Mo 1/3 Nb 1/3 Zr 1/3 )Si 2 , using s-IUS. This IUS platform offers a versatile route for high-throughput materials discovery and energy-efficient fabrication of bulk refractory materials.

36 MATERIALS SCIENCE↗

Merging Fungal and Bacterial Community Profiles via an Internal Control

Integrated measurements of fungi and bacteria are critical to understand how interactions between these taxa drive key processes in ecosystems ranging from soils to animal guts. High-throughput amplicon sequencing is commonly used to census microbiomes, but the genetic markers targeted for fungi and bacteria (typically ribosomal regions) are domain-specific so profiling must be performed separately, obscuring relationships between these groups. To solve this problem, we developed a spike-in method with an internal control (IC) construct containing primer sites commonly used for bacterial and fungal taxonomic profiling. The internal control offers several advantages: estimation of absolute abundances, estimation of fungal to bacterial ratios (F:B), integration of bacterial and fungal profiles for holistic community analysis, and lower costs compared to other quantitation methods. To validate the IC as a scaling method, we compared IC-derived measures of F:B to measures from quantitative PCR (qPCR) using a commercial mock community (the ZymoBiomic Microbial Community DNA Standard II, containing two fungi and eight bacteria) and complex environmental samples. For both the mock community and the environmental samples, the IC produced F:B values that were statistically consistent with qPCR. Merging the environmental fungal and bacterial profiles based on the IC-derived F:B values revealed new relationships among samples in terms of community similarity. As a result, this IC method is the first spike-in method to employ a single construct for cross-domain amplicon sequencing, offering more reliable measurements.

59 BASIC BIOLOGICAL SCIENCES↗

Enhanced Spatial Mapping of Histone Proteoforms in Human Kidney Through MALDI-MSI by High-Field UHMR-Orbitrap Detection

Core histones including H2A, H2B, H3, and H4 are key modulators of cellular repair, transcription, and replication within eukaryotic cells, playing vital roles within the pathogenesis of disease and cellular responses to environmental stimuli. Traditional mass spectrometry (MS) based bottom-up and top-down proteomics allows for the comprehensive identification of proteins and of post-translational modification (PTM) harboring proteoforms. However, these methodologies have difficulties preserving near cellular spatial distributions because they typically require laser capture microdissection (LCM) and advanced sample preparation techniques. Herein, we coupled matrix-assisted laser desorption/ionization (MALDI) source with a Thermo Scientific Q-Exactive HF Orbitrap MS upgraded with ultra-high mass range (UHMR) boards for the first demonstration of complementary high-resolution accurate mass measurements of proteoforms directly from tissue using this benchtop mass spectrometer. The platform achieved isotopic resolution throughout the detected mass range, providing confident assignments of proteoforms with low ppm mass error and a vastly improved duty cycle over other Fourier transform mass analyzers. Proteoform mapping of core histones was demonstrated on sections of human kidney at near-cellular spatial resolution, with several key distributions of histone and other proteoforms noted within both healthy biopsy and a section from a renal cell carcinoma (RCC) containing nephrectomy. Further, the use of MALDI-MS imaging (MSI) for proteoform mapping demonstrates several steps towards high-throughput accurate identification of proteoforms and provides a new tool for mapping biomolecule distributions throughout tissue sections in extended mass ranges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗