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At least 289 records · Page 16

Mixed Enthalpy–Entropy Descriptor for the Rational Design of Synthesizable High-Entropy Materials Over Vast Chemical Spaces

The practically unlimited high-dimensional composition space of high-entropy materials (HEMs) has emerged as an exciting platform for functional material design and discovery. However, the identification of stable and synthesizable HEMs and robust design rules remains a daunting challenge. Here, we propose a mixed enthalpy–entropy descriptor (MEED) that enables highly efficient, robust, high-throughput prediction of synthesizable HEMs across vast chemical spaces from first-principles. The MEED is based on two parameters: the relative formation enthalpy with respect to the most stable competing compound and the spread of the point-defect formation energy spectrum. The former measures the relative synthesizability of an HEM to its most stable competing phase, going beyond the conventional thermodynamic understanding. Further, the latter gauges the relative entropy forming ability of an HEM, entailing no sampling over numerous alloy configurations. By applying the MEED to two structurally distinct representative material systems (i.e., 3D rocksalt carbides and 2D layered sulfides), we not only successfully identify all experimentally reported HEMs within these systems but also reveal a cutoff criterion for assessing their relative synthesizability within each system. By the MEED, tens of new high-entropy carbides and 2D high-entropy sulfides are also predicted, which have the potential for a wide variety of applications such as coating in aerospace devices, energy conversion and storage, and flexible electronics.

36 MATERIALS SCIENCE↗

Rapid preparation of nanodiscs for biophysical studies

Nanodiscs, which are disc-shaped entities that contain a central lipid bilayer encased by an annulus of amphipathic helices, have emerged as a leading native-like membrane mimic. The current approach for the formation of nanodiscs involves the creation of a mixed-micellar solution containing membrane scaffold protein, lipid, and detergent followed by a time consuming process (3–12 h) of dialysis and/or incubation with sorptive beads to remove the detergent molecules from the sample. In contrast, the methodology described herein provides a facile and rapid procedure for the preparation of nanodiscs in a matter of minutes (<15 min) using Sephadex® G-25 resin to remove the detergent from the sample. A panoply of biophysical techniques including analytical ultracentrifugation, dynamic light scattering, gel filtration chromatography, circular dichroism spectroscopy, and cryogenic electron microscopy were employed to unequivocally confirm that aggregates formed by this method are indeed nanodiscs. We believe that this method will be attractive for time-sensitive and high-throughput experiments.

59 BASIC BIOLOGICAL SCIENCES↗

Experiments with a prototype titanium hot cavity surface ionization source intended for electromagnetic separation of radioactive samarium and other lanthanide elements

This paper reports experimental results of a prototype titanium surface ionization source. For the first time, a lanthanide ion beam has been produced with a surface ionizer composed completely of titanium metal. Titanium does not readily activate with neutron irradiation. This offers the potential for inserting an ion source made of titanium directly into a reactor with a pre-loaded non-radioactive lanthanide target. This seamlessly integrates target irradiation with isotope separation, eliminating post irradiation sample manipulation. Samarium ion beam currents up to 960 nA have been produced in an off-line test bench equipped with rudimentary beam optics. This is a crucial step toward the development of an ionization source adopted for the electromagnetic isotope separator (EMIS) facility, which has been designed for high throughput separations of radioactive 153 Sm and other lanthanides of interest in the field of nuclear medicine. The ion current and important factors affecting the performance of the ion source, such as the ionizer temperature and thermal gradient, are discussed. Lastly, the experimental results are presented together with a discussion of future modifications to optimize the overall surface ionization source performance.

07 ISOTOPE AND RADIATION SOURCES↗

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct-electron detection. An electron probe size down to 0.5 nm in diameter is used and the sample investigated is a gold–palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

47 OTHER INSTRUMENTATION↗

Advancing energy storage through solubility prediction: leveraging the potential of deep learning

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure–property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

25 ENERGY STORAGE↗

Development and validation of time‐domain 1 H‐NMR relaxometry correlation for high‐throughput phenotyping method for lipid contents of lignocellulosic feedstocks

Abstract The bioenergy crops such as energycane, miscanthus, and sorghum are being genetically modified using state of the art synthetic biotechnology techniques to accumulate energy‐rich molecules such as triacylglycerides (TAGs) in their vegetative cells to enhance their utility for biofuel production. During the initial genetic developmental phase, many hundreds of transgenic phenotypes are produced. The efficiency of the production pipeline requires early and minimally destructive determination of oil content in individuals. Current screening methods require time‐intensive sample preparation and extraction with chemical solvents for each plant tissue. A rapid screen will also be needed for developing industrial extraction as these crops become available. In the present study, we have devised a proton relaxation nuclear magnetic resonance ( 1 H‐NMR) method for single‐step, non‐invasive, and chemical‐free characterization of in‐situ lipids in untreated and pretreated lignocellulosic biomass. The systematic evaluation of NMR relaxation time distribution provided insight into the proton environment associated with the lipids in the biomass. It resolved two distinct lipid‐associated subpopulations of proton nuclei that characterize total in‐situ lipids into bound and free oil based on their “molecular tumbling” rate. The T1T2 correlation spectra also facilitated the resolution of the influence of various pretreatment procedures on the chemical composition of molecular and local 1 H population in each sample. Furthermore, we show that hydrothermally pretreated biomass is suitable for direct NMR analysis unlike dilute acid and alkaline pretreated biomass which needs an additional step for neutralization.

pretreatment↗

Online Characterization of Mixed Plastic Waste Using Machine Learning and Mid-Infrared Spectroscopy

To recycle the mixed plastic wastes (MPW), it is important to obtain the compositional information online in real time. We present a sensing framework based on a convolutional neural network (CNN) and mid-infrared spectroscopy (MIR) for the rapid and accurate characterization of MPW. The MPW samples are placed on a moving platform to mimic the industrial environment. The MIR spectra are collected at the rate of 100 Hz, and the proposed CNN architecture can reach an overall prediction accuracy close to 100%. Therefore, the proposed method paves the way toward the online MPW characterization in industrial applications where high throughput is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Rapid screening of secondary aromatic metabolites in Populus trichocarpa leaves

Abstract Background High-throughput metabolomics analytical methodology is needed for population-scale studies of bioenergy-relevant feedstocks such as poplar ( Populus sp.). Here, the authors report the relative abundance of extractable aromatic metabolites in Populus trichocarpa leaves rapidly estimated using pyrolysis-molecular beam mass spectrometry (py-MBMS). Poplar leaves were analyzed in conjunction with and validated by GC/MS analysis of extracts to determine key spectral features used to build PLS models to predict the relative composition of extractable aromatic metabolites in whole poplar leaves. Results The Pearson correlation coefficient for the relative abundance of extractable aromatic metabolites based on ranking between GC/MS analysis and py-MBMS analysis of the Boardman leaf set was 0.86 with R 2 = 0.76 using a simplified prediction approach from select ions in MBMS spectra. Metabolites most influential to py-MBMS spectral features in the Clatskanie set included the following compounds: catechol, salicortin, salicyloyl-coumaroyl-glucoside conjugates, α-salicyloylsalicin, tremulacin, as well as other salicylates, trichocarpin, salicylic acid, and various tremuloidin conjugates. Ions in py-MBMS spectra with the highest correlation to the abundance of extractable aromatic metabolites as determined by GC/MS analysis of extracts, included m/z 68, 71, 77, 91, 94, 105, 107, 108, and 122, and were used to develop the simplified prediction approach without PLS models or a priori measurements. Conclusions The simplified py-MBMS method is capable of rapidly screening leaf tissue for relative abundance of extractable aromatic secondary metabolites to enable prioritization of samples in large populations requiring comprehensive metabolomics that will ultimately inform plant systems biology models and advance the development of optimized biomass feedstocks for renewable fuels and chemicals.

09 BIOMASS FUELS↗

Geographic patterns and determinants of antibiotic resistomes in coastal sediments across complex ecological gradients

Coastal areas are highly influenced by terrestrial runoffs and anthropogenic disturbances, commonly leading to ecological gradients from bay, nearshore, to offshore areas. Although the occurrence and distribution of sediment antibiotic resistome are explored in various coastal environments, little information is available regarding geographic patterns and determinants of coastal sediment antibiotic resistomes across ecological gradients at the regional scale. Here, using high-throughput quantitative PCR, we investigated the geographic patterns of 285 antibiotic resistance genes (ARGs) in coastal sediments across a ~ 200 km scale in the East China Sea. Sediment bacterial communities and physicochemical properties were characterized to identify the determinants of sediments antibiotic resistome. Higher richness and abundance of ARGs were detected in the bay samples compared with those in nearshore and offshore samples, and significant negative correlations between the richness and/or abundance of ARGs and the distance to coastline (DTC) were identified, whereas different types of ARGs showed inconsistency in their relationships with DTC. The composition of antibiotic resistome showed significant correlations with nutrition-related variables (including NH 4 + -N, NO 3 − -N, and total phosphorus) and metals/metalloid (including As, Cu, Ni, and Zn), suggesting that terrestrial disturbances largely shape the antibiotic resistome. The Bipartite network showed strong associations between ARGs and mobile genetic elements (MGEs), and Partial Least Squares Path Modeling further revealed that terrestrial disturbance strength (as indicated by DTC) directly affected abiotic environmental conditions and bacterial community composition, and indirectly affected antibiotic resistome via MGEs. These findings provide insights into regional variability of sediment antibiotic resistome and its shaping path across complex ecological gradients, highlighting terrestrial disturbances as determinative forces in shaping coastal sediment antibiotic resistomes.

Xiong, Shangling↗

High-throughput, in situ imaging of multi-layer powder-blown directed energy deposition with angled nozzle

Laser metal additive manufacturing (AM) has become an increasingly popular technology due to its flexibility in geometry and materials. As one of the commercialized additive processes, powder-blown directed energy deposition (DED) has been used in multiple industries such as aerospace, automotive, medical device, etc. However, a lack of fundamental understanding remains for this process, and many opportunities for alloy development and implementation can be identified. A high-throughput, in situ DED system capable of multi-layer builds that can address these issues is presented here. In this work, implications of layer heights and energy density are investigated through an extensive process parameter sweep, showcasing the power of a high-throughput setup while also discussing multi-layer interactions.

47 OTHER INSTRUMENTATION↗

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗

Fine-scale evaluation of two standard 16S rRNA gene amplicon primer pairs for analysis of total prokaryotes and archaeal nitrifiers in differently managed soils

The advance of high-throughput molecular biology tools allows in-depth profiling of microbial communities in soils, which possess a high diversity of prokaryotic microorganisms. Amplicon-based sequencing of 16S rRNA genes is the most common approach to studying the richness and composition of soil prokaryotes. To reliably detect different taxonomic lineages of microorganisms in a single soil sample, an adequate pipeline including DNA isolation, primer selection, PCR amplification, library preparation, DNA sequencing, and bioinformatic post-processing is required. Besides DNA sequencing quality and depth, the selection of PCR primers and PCR amplification reactions arguably have the largest influence on the results. This study tested the performance and potential bias of two primer pairs, i.e., 515F (Parada)-806R (Apprill) and 515F (Parada)-926R (Quince) in the standard pipelines of 16S rRNA gene Illumina amplicon sequencing protocol developed by the Earth Microbiome Project (EMP), against shotgun metagenome-based 16S rRNA gene reads. The evaluation was conducted using five differently managed soils. We observed a higher richness of soil total prokaryotes by using reverse primer 806R compared to 926R, contradicting to in silico evaluation results. Both primer pairs revealed various degrees of taxon-specific bias compared to metagenome-derived 16S rRNA gene reads. Nonetheless, we found consistent patterns of microbial community variation associated with different land uses, irrespective of primers used. Total microbial communities, as well as ammonia oxidizing archaea (AOA), the predominant ammonia oxidizers in these soils, shifted along with increased soil pH due to agricultural management. In the unmanaged low pH plot abundance of AOA was dominated by the acid-tolerant NS-Gamma clade, whereas limed agricultural plots were dominated by neutral-alkaliphilic NS-Delta/NS-Alpha clades. This study stresses how primer selection influences community composition and highlights the importance of primer selection for comparative and integrative studies, and that conclusions must be drawn with caution if data from different sequencing pipelines are to be compared.

16S rRNA gene amplicon Illumina sequencing↗

Near-infrared spectroscopy as a green analytical tool for sustainable biomass characterization for biofuels and bioproducts: An overview

Biomass, a widely used renewable energy source, requires characterization to optimize biofuel and bioproduct processes, customize feedstocks, and ensure economic and environmental sustainability. Conventional wet-chemistry methods for biomass analysis are slow, expensive, and require significant reagents and skilled personnel. In contrast, near-infrared (NIR) spectroscopy, a faster, cost-effective, and reagent-free green technology, enables non-destructive biomass analysis with minimal sample preparation. This study provides an overview of the fundamentals of NIR spectroscopy and explores its recent applications for analyzing various biomass properties important to the biofuel and bioproduct industry. The study also critically evaluates the challenges and opportunities of using NIR spectroscopy for biomass analysis. This review aims to guide future research for rapid and high throughput characterization of biomass in the biomass industry, supporting the United Nations’ sustainable development goal (SDG) 7: producing affordable and sustainable energy.

Biofuels↗

HT-SIP: a semi-automated stable isotope probing pipeline identifies cross-kingdom interactions in the hyphosphere of arbuscular mycorrhizal fungi

Abstract Background Linking the identity of wild microbes with their ecophysiological traits and environmental functions is a key ambition for microbial ecologists. Of many techniques that strive for this goal, Stable-isotope probing—SIP—remains among the most comprehensive for studying whole microbial communities in situ. In DNA-SIP, actively growing microorganisms that take up an isotopically heavy substrate build heavier DNA, which can be partitioned by density into multiple fractions and sequenced. However, SIP is relatively low throughput and requires significant hands-on labor. We designed and tested a semi-automated, high-throughput SIP (HT-SIP) pipeline to support well-replicated, temporally resolved amplicon and metagenomics experiments. We applied this pipeline to a soil microhabitat with significant ecological importance—the hyphosphere zone surrounding arbuscular mycorrhizal fungal (AMF) hyphae. AMF form symbiotic relationships with most plant species and play key roles in terrestrial nutrient and carbon cycling. Results Our HT-SIP pipeline for fractionation, cleanup, and nucleic acid quantification of density gradients requires one-sixth of the hands-on labor compared to manual SIP and allows 16 samples to be processed simultaneously. Automated density fractionation increased the reproducibility of SIP gradients compared to manual fractionation, and we show adding a non-ionic detergent to the gradient buffer improved SIP DNA recovery. We applied HT-SIP to 13 C-AMF hyphosphere DNA from a 13 CO 2 plant labeling study and created metagenome-assembled genomes (MAGs) using high-resolution SIP metagenomics (14 metagenomes per gradient). SIP confirmed the AMF Rhizophagus intraradices and associated MAGs were highly enriched (10–33 atom% 13 C), even though the soils’ overall enrichment was low (1.8 atom% 13 C). We assembled 212 13 C-hyphosphere MAGs; the hyphosphere taxa that assimilated the most AMF-derived 13 C were from the phyla Myxococcota, Fibrobacterota, Verrucomicrobiota, and the ammonia-oxidizing archaeon genus Nitrososphaera . Conclusions Our semi-automated HT-SIP approach decreases operator time and improves reproducibility by targeting the most labor-intensive steps of SIP—fraction collection and cleanup. We illustrate this approach in a unique and understudied soil microhabitat—generating MAGs of actively growing microbes living in the AMF hyphosphere (without plant roots). The MAGs’ phylogenetic composition and gene content suggest predation, decomposition, and ammonia oxidation may be key processes in hyphosphere nutrient cycling.

59 BASIC BIOLOGICAL SCIENCES↗

High throughput automated characterization of enamel microstructure using synchrotron tomography and optical flow imaging

The remarkable damage-tolerance of enamel has been attributed to its hierarchical microstructure and the organized bands of decussated rods. A thorough characterization of the microscale rod evolution within the enamel is needed to elucidate this complex structure. While prior efforts in this area have made use of single particle tracking to track a single rod evolution to various degrees of success, such a process can be both computationally and labor intensive, limited to the evolution path of a single rod, and is therefore prone to error from potentially tracking outliers. Particle image velocimetry (PIV) is a well-established algorithm to derive field information from image sequences for processes that are time-dependent, such as fluid flows and structural deformation. In this work, we demonstrate the use of PIV in extracting the full-field microstructural distribution of rods within the enamel. Further, enamel samples from a wild African lion were analyzed using high-energy synchrotron X-ray micro-tomography. Results from the PIV analysis provide sufficient full-field information to reconstruct the growth of individual rods that can potentially enable rapid analysis of complex microstructures from high resolution synchrotron datasets. Such information can serve as a template for designing damage-tolerant bioinspired structures for advanced manufacturing.

36 MATERIALS SCIENCE↗

Dimensional Control over Metal Halide Perovskite Crystallization Guided by Active Learning

Metal halide perovskite (MHP) derivatives, a promising class of optoelectronic materials, have been synthesized with a range of dimensionalities that govern their optoelectronic properties and determine their applications. We demonstrate a data-driven approach combining active learning and high-throughput experimentation to discover, control, and understand the formation of phases with different dimensionalities in the morpholinium (morph) lead iodide system. Using a robot-assisted workflow, we synthesized and characterized two novel MHP derivatives that have distinct optical properties: a one-dimensional (1D) morphPbI 3 phase ([C 4 H 10 NO][PbI 3 ]) and a two-dimensional (2D) (morph) 2 PbI 4 phase ([C 4 H 10 NO] 2 [PbI 4 ]). To efficiently acquire the data needed to construct a machine learning (ML) model of the reaction conditions where the 1D and 2D phases are formed, data acquisition was guided by a diverse-mini-batch-sampling active learning algorithm, using prediction confidence as a stopping criterion. Querying the ML model uncovered the reaction parameters that have the most significant effects on dimensionality control. Based on these insights, we discuss possible reaction schemes that may selectively promote the formation of morph-Pb-I phases with different dimensionalities. The data-driven approach presented here, including the use of additives to manipulate dimensionality, will be valuable for controlling the crystallization of a range of materials over large reaction-composition spaces.

36 MATERIALS SCIENCE↗

Evaluating inkjet dispenser/liquid vortex capture-mass spectrometry for single-cell metabolomics in Hep G2 steatosis caused by tamoxifen

Single-cell mass spectrometry (MS) is advancing our understanding of metabolic pathways in heterogeneous cell populations; however, many techniques are slow or require disruptive sample preparations. This study evaluated coupling a modified HP D100 single-cell inkjet dispenser with liquid vortex capture-mass spectrometry (D100/LVC-MS). The D100 is a single-cell inkjet dispenser capable of titrating solutions and isolating single cells via disposable cassettes equipped with microfluidic channels and an impedance sensor. The LVC-MS enables high-throughput capture, lysis, and ionization of analytes for mass spectrometric analysis. The D100/LVC-MS system was characterized through titration and single-cell experiments. Propranolol titration demonstrated linearity across a broad concentration range using the D100/LVC-MS system. Additionally, Hep G2 hepatocarcinoma cells and Chlamydomonas reinhardtii algae were used to showcase the D100’s high-throughput or low-buffer-volume single-cell dispensing strategies. The D100/LVC-MS system’s performance was validated by evaluating tamoxifen-induced steatosis in Hep G2 cells. Tamoxifen, associated with nonalcoholic fatty liver disease in breast cancer patients following long-term use, was tested in Hep G2 cells at 20 µM for 72 h against DMSO-treated controls. High-throughput analysis of 500 cells per condition, completed in 25 min per run, demonstrated the system’s efficiency. The D100/LVC-MS system simultaneously quantified tamoxifen and measured triglycerides and phosphatidylcholines. Triglycerides were upregulated in the tamoxifen-treated cells and the results indicated two distinct cell populations, differing in tamoxifen and phosphatidylcholines levels, suggesting heterogeneity within the treated population. In conclusion, these findings highlight the D100/LVC-MS system as a cost-effective, high-throughput platform for single-cell metabolomics and lipidomics, with significant potential for evaluating metabolic alterations.

Chromatography↗

Silver Nanowire-Indium Zinc Oxide Composite Flexible Transparent Conducting Electrodes Made by Spin- coating and Photonic Curing

Realizing high-throughput, low-cost perovskite solar cell (PSC) manufacturing is highly sought-after in photovoltaic (PV) research in recent years. To fully achieve roll-to-roll (R2R) manufacturing of PSCs, it is important to consider the flexible transparent electrode (TE). PET/ITO is a commonly used substrate for making flexible PSCs. When optimizing transparent conducting materials, there is a tradeoff between sheet resistance (Rsh) and optical transparency. Because commercial PET/ITO substrates are made with slow (~1 m/min) vacuum deposition processes, they tend to be expensive. Therefore, it would be advantageous to develop a high-throughput, R2R compatible, solution-deposition approach for fabricating the TE on PET substrates. While various solution-deposition processes, such as blade coating or slot-die coating, can achieve the desired web speed of > 10 m/min, there is still a need to improve the post-deposition annealing step. One promising post-deposition processing technique is intense-pulsed-light processing, also known as photonic curing. Photonic curing delivers short (0.01 – 100 ms) pulses of broadband (200 – 1500 nm) light from a xenon flash lamp to the samples. Any materials in the sample stack that absorb light will convert the impinging light pulse into heat within the sample, which drives changes in the sample (calcination, phase change, crystallization, etc.). Photonic curing has three main advantages over thermal annealing: 1. Faster processing speed (milliseconds or seconds). 2. Compatibility with plastic substrates. 3. Smaller physical footprint and less wasted energy. Since the light pulses are on for a short time, the intensity can be high while the total energy delivered to the sample is low, minimizing damages to the plastic substrates. In this work, a hybrid TE material is fabricated on PET substrates using photonic curing. The hybrid TE material contains a layer of silver nanowires (AgNWs) and a layer of metal-oxide (InOx, ITO, IZO, etc.). The AgNWs increase the light absorbed by the film during the photonic curing process, which leads to higher processing temperatures, possibly improving the conversion of the metal-oxide layer. The AgNWs also enhance the electrical conductivity of the final TE layer after photonic curing. A AgNW and metal-oxide bilayer is formed by spin coating each solution onto the PET substrate sequentially followed by a single photonic curing process. We use average optical transmittance (Tavg) from 400 to 700 nm and average Rsh to evaluate the TE performance. The following photonic curing parameters are varied to optimize Tavg (maximize) and Rsh (minimize): Pulse voltage, pulse envelope, number of micro-pulses, duty cycle, number of pulses, and pulse repetition rate. Preliminarily, we also observe a significant impact on the TE properties by the volume of AgNW deposited during the spin coating deposition step. Using dispense volumes of 80 µL and 20 µL, we achieve samples with Tavg = 73%, Rsh = 19 Ω/sq, and roughness = 9 nm, and Tavg = 83%, Rsh = 58 Ω/sq, and roughness = 5.6 nm, respectively, after photonic curing.

14 SOLAR ENERGY↗