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DOE OSTI2025

Improving the Transportability of a Deep Learning Denoising Model Using Transfer Learning Techniques

The adoption of machine learning techniques in the seismology community has led to great performance improvements in several areas, including signal processing. Specifically, the development of deep learning–based seismic waveform denoising models has the potential to yield improvements in signal detection capabilities for networks operating in particularly noisy environments. Recent advancements in the design of these deep learning denoising models have included the incorporation of continuous and discrete wavelet transform functions into the network architecture to improve the learning capabilities and efficiency of said models. These wavelet transform–based seismic denoising models have shown improved denoising capabilities in regions where there is good agreement between the data features present in the training and evaluation datasets. However, questions remain about the overall transportability of these models to other monitoring regions. Here, in this study, we will determine the baseline transportability of a newly developed multilevel wavelet‐transform convolutional neural network (MWCNN) seismic denoising model. We accomplish this by taking a version of the MWCNN denoising model trained on data collected from the Utah region and evaluating its denoising performance on datasets collected from the neighboring Nevada region, which differ with regard to monitoring sensor types and event histories. We find that there is a notable variability in denoising performance related to the degree of similarity between the initial and new target datasets. The most notable difference in denoising performance is the ability of the denoising model to preserve accurate amplitude information associated with the signal energy present in the waveform data. Finally, we evaluate the ability of transfer learning techniques to improve the transportability of the MWCNN denoising model. We find that although there is still a performance gap present in the denoising results of the MWCNN model, transfer learning did yield improved results.

Quinones, Louis [Sandia National Laboratories (SNL↗

DOE OSTI2017

Modelling and validation of particle size distributions of supported nanoparticles using the pair distribution function technique

The particle size of supported catalysts is a key characteristic for determining structure–property relationships. It is a challenge to obtain this information accurately and in situ using crystallographic methods owing to the small size of such particles (<5 nm) and the fact that they are supported. In this work, the pair distribution function (PDF) technique was used to obtain the particle size distribution of supported Pt catalysts as they grow under typical synthesis conditions. The PDF of Pt nanoparticles grown on zeolite X was isolated and refined using two models: a monodisperse spherical model (single particle size) and a lognormal size distribution. The results were compared and validated using scanning transmission electron microscopy (STEM) results. Both models describe the same trends in average particle size with temperature, but the results of the number-weighted lognormal size distributions can also accurately describe the mean size and the width of the size distributions obtained from STEM. Since the PDF yields crystallite sizes, these results suggest that the grown Pt nanoparticles are monocrystalline. This work shows that refinement of the PDF of small supported monocrystalline nanoparticles can yield accurate mean particle sizes and distributions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Surface Studies of β-1,3,5,7-Tetranitro-1,3,5,7-Tetrazoctane and Pentaerythritol Tetranitrate from Density Functional Tight-Binding Calculations and Implications on Crystal Shape

Here, we use density functional tight-binding (DFTB) theory to calculate the surface energies of two energetic crystals: monoclinic β-1,3,5,7-tetranitro-1,3,5,7-tetrazoctane (β-HMX) and tetragonal pentaerythritol tetranitrate (PETN). The results are then employed to determine crystal shapes using the Bravais–Friedel–Donnay–Harker, attachment energy, and surface energy models. We find that energy-based models yield predictions in good agreement with experimental observations. Additionally, we propose a simple model that reframes surface energy as a measure of the lost intermolecular interactions during the formation of a surface from the bulk. The model accurately captures the results from the DFTB calculations and enables us to explain and predict surface energies as a function of the local molecular environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Tuning Optical and Electrical Properties of Vanadium Oxide with Topochemical Reduction and Substitutional Tin

Vanadium oxides are widely tunable materials, with many thermodynamically stable phases suitable for applications spanning catalysis to neuromorphic computing. The stability of vanadium in a range of oxidation states enables mixed-valence polymorphs of kinetically accessible metastable materials. Low-temperature synthetic routes to, and the properties of, these metastable materials are poorly understood and may unlock new optoelectronic and magnetic functionalities for expanded applications. In this work, we demonstrate topochemical reduction of α-V 2 O 5 to produce metastable vanadium oxide phases with tunable oxygen vacancies (>6%) and simultaneous substitutional tin incorporation (>3.5%). The chemistry is carried out at low temperature (65 °C) with solution-phase SnCl 2 , where Sn 2+ is oxidized to Sn 4+ as V 5+ sites are reduced to V 4+ during oxygen vacancy formation. Despite high oxygen vacancy and tin concentrations, the transformations are topochemical in that the symmetry of the parent crystal remains intact, although the unit cell expands. Band structure calculations show that these vacancies contribute electrons to the lattice, whereas substitutional tin contributes holes, yielding a compensation doping effect and control over the electronic properties. The SnCl 2 redox chemistry is effective on both solution-processed V 2 O 5 nanoparticle inks and mesoporous films cast from untreated inks, enabling versatile routes toward functional films with tunable optical and electronic properties. The electrical conductance rises concomitantly with the SnCl 2 concentration and treatment time, indicating a net increase in density of free electrons in the host lattice. This work provides a valuable demonstration of kinetic tailoring of electronic properties of vanadium–oxygen systems through top-down chemical manipulation from known thermodynamic phases.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Predictive Model of Charge Mobilities in Organic Semiconductor Small Molecules with Force-Matched Potentials

Charge mobility of crystalline organic semiconductors (OSC) is limited by local dynamic disorder. Recently, the charge mobility for several high mobility OSCs, including TIPS-pentacene, were accurately predicted from a density functional theory (DFT) simulation constrained by the crystal structure and the inelastic neutron scattering spectrum, which provide direct measures of the structure and the dynamic disorder in the length scale and energy range of interest. However, the computational expense required for calculating all of the atomic and molecular forces is prohibitive. In this work, we demonstrate the use of density functional tight binding (DFTB), a semiempirical quantum mechanical method that is 2 to 3 orders of magnitude more efficient than DFT. We show that force matching a many-body interaction potential to DFT derived forces yields highly accurate DFTB models capable of reproducing the low-frequency intricacies of experimental inelastic neutron scattering (INS) spectra and accurately predicting charge mobility. We subsequently predicted charge mobilities from our DFTB model of a number of previously unstudied structural analogues to TIPS-pentacene using dynamic disorder from DFTB and transient localization theory. The approach we establish here could provide a truly rapid simulation pathway for accurate materials properties prediction, in our vision applied to new OSCs with tailored properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Effect of trace amounts of added Sc on microstructure and mechanical properties of 2055 aluminum alloy

Highlights: • Effect of trace Sc on microstructure and mechanical properties in 2055 Al-alloy was investigated. • T{sub 1} is the main strengthening phase in Sc free and Sc added 2055 alloy at the aging temperature of 175°C and 200°C. • The refinement of T{sub 1} by Sc addition depends on the aging temperature. • Mechanical properties were deteriorated by Sc at the aging temperature of 175°C and improved at 200°C aging treatment. - Abstract: The effects of Sc additions and isothermal aging on the microstructure and mechanical properties of 2055 aluminum alloy have been investigated. The isothermal aging temperatures investigated were 175 °C and 200 °C. Results show that the aging temperature does not affect the type of precipitates in both Sc-free and Sc-added 2055 Al alloys. While remarkable changes in the size and number density of T{sub 1} (Al{sub 2}CuLi) precipitates have been observed. For samples aged at 175 °C for 8 h, the average length of T{sub 1} precipitates was increased from 39.9 nm to 45.6 nm, the number density decreased from 5.77 × 10{sup 22} m{sup −3} to 3.68 × 10{sup 22} m{sup −3} after Sc addition. On the contrary, when samples were aged at 200 °C for 8 h, the length decreased from 59.89 nm to 43.32 nm and number density increased from 2.13 × 10{sup 22} m{sup −3} to 3.00 × 10{sup 22} m{sup −3} by adding trace Sc. Slight deterioration of tensile strength and yield strength with higher ductility was founded at the aging temperature of 175 °C for 8 h due to the larger size and lower number density of T{sub 1} precipitates along with the formation of a W (AlCuSc) phase during the casting or homogenization processes. When aging temperature increased to 200 °C, tensile strength and yield strength were significantly improved by Sc addition without losing ductility. Highly dense fine T{sub 1} precipitates take responsible for the strength improvement, and to some extent it compensated the strength loss caused by the formation of W phase. A “vacancy-prison” mechanism was adopted to explain the effect of Sc additions on the precipitation of T{sub 1} phase as a function of aging temperature.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Nitrosylation of Nitric-Oxide-Sensing Regulatory Proteins Containing [4Fe-4S] Clusters Gives Rise to Multiple Iron-Nitrosyl Complexes

The reaction of protein-bound iron–sulfur (Fe-S) clusters with nitric oxide (NO) plays key roles in NO-mediated toxicity and signaling. Elucidation of the mechanism of the reaction of NO with DNA regulatory proteins that contain Fe-S clusters has been hampered by a lack of information about the nature of the iron-nitrosyl products formed. Herein, we report nuclear resonance vibrational spectroscopy (NRVS) and density functional theory (DFT) calculations that identify NO reaction products in WhiD and NsrR, regulatory proteins that use a [4Fe-4S] cluster to sense NO. This work reveals that nitrosylation yields multiple products structurally related to Roussin's Red Ester (RRE, [Fe 2 (NO) 4 (Cys) 2 ]) and Roussin's Black Salt (RBS, [Fe 4 (NO) 7 S 3 ]. In the latter case, the absence of 32 S/ 34 S shifts in the Fe-S region of the NRVS spectra suggest that a new species, Roussin's Black Ester (RBE), may be formed, in which one or more of the sulfide ligands is replaced by Cys thiolates.

Serrano, Pauline N.↗

DOE OSTI2021

Nth-plant supply: corn stover supplies and costs in a fleet of biorefineries

Feedstock cost and cost variability is expected to increase with the number of biorefineries. To quantify this effect, this spatial-economic analysis simulates feedstock cost and cost variability of an industry based on corn stover as a function of the number of biorefineries. Results are reported for nine scenarios (a base case and sensitivity analysis of four variables – harvest efficiency, sustainability constraints, opportunity cost, and corn grain yield) under deterministic and stochastic simulations, assuming biorefineries using 658 000 Mg (725 000 tons) year- 1 of corn stover in 2019. The resulting supply curves are highly elastic (i.e. little change in cost) for the first 50 of the 121 biorefineries, with price increases in subsequent biorefineries depending on scenario. In the base-case deterministic scenario, weighted-average stover costs are $66 Mg -1 ($60 ton- 1 ), $69 Mg -1 ($62 ton -1 ), and $156 Mg -1 ($142 ton -1 ), at the first, 60th, and 121st biorefineries, respectively. The stochastic simulations, subject to observed 30-year corn yield variability, follow a similar pattern, with price distributions that vary by scenario. The base-case stochastic simulations illustrate minimal cost variability for the first 60 biorefineries, but rapid increases in cost variability in the second half of potential biorefineries, with similar patterns observed in the other scenarios. Of the four variables explored, price was most sensitive to harvest efficiency, followed by sustainability constraints, corn yield, and opportunity cost. Results suggest that, under conventional logistics, about half of the US corn stover resource is reliably available with minimum cost increase and variability. Interactive visualization is available at https://doi.org/10.11578/1828779.

09 BIOMASS FUELS↗

DOE OSTI2015

Enhancing isobutyric acid production from engineered Acidithiobacillus ferrooxidans cells via media optimization

ABSTRACT The chemolithoautotrophic bacterium Acidithiobacillus ferrooxidans has previously been genetically modified to produce isobutyric acid (IBA) from carbon dioxide while obtaining energy from the oxidation of ferrous iron. Here, a combinatorial approach was used to explore the influence of medium composition in both batch and chemostat cultures in order to improve IBA yields (g IBA/mol Fe 2+ ) and productivities (g IBA/L/d). Medium pH, ferrous concentration (Fe 2+ ), and inclusion of iron chelators all had positive impact on the IBA yield. In batch experiments, gluconate was found to be a superior iron chelator because its use resulted in smaller excursions in pH. In batch cultures, IBA yields decreased linearly with increases in the final effective Fe 3+ concentrations. Chemostat cultures followed similar trends as observed in batch cultures. Specific cellular productivities were found to be a function of the steady state ORP (Oxidation‐reduction potential) of the growth medium, which is primarily determined by the Fe 3+ to Fe 2+ ratio. By operating at low ORP, chemostat cultures were able to achieve volumetric productivities as high as 3.8 ± 0.2 mg IBA/L/d which is a 14‐fold increase over the previously reported value. Biotechnol. Bioeng. 2016;113: 790–796. © 2015 Wiley Periodicals, Inc.

Li, Xiaozheng↗

DOE OSTI2022

Insights into Methodologies and Operational Details of Resource Adequacy Assessment: A Case Study with Application to a Broader Flexibility Framework

Assessing and maintaining resource adequacy (RA) is a core pillar of power systems. However, recent changes in the physical makeup of these systems and the conditions under which these systems must operate have yielded a renewed interest in the methods, metrics, and assumptions that underpin RA assessments. In this paper, we systematically explore a wide range of RA modeling dimensions, including: the objective function and level of operational detail in the underlying model formulation; the quantity (look-ahead) and quality (accuracy) of data that is available for making operational decisions within those models; and the physical configuration of solar photovoltaics (PV) with battery storage hybrid resources. We apply a set of probabilistic RA tools and production cost modeling tools to a realistic test system based loosely on a future Electric Reliability Council of Texas power system dominated by solar PV resources. Under the assumptions of our system and models, we find that multi-stage probabilistic assessments may provide a more robust evaluation of RA by capturing a wider range of operational and system interactions, but this comes at a computational cost of 1-2 orders of magnitude longer run time depending on the specific configuration. In addition, the information on thermal generator availability impacts RA performance by an order of magnitude more than solar resource forecasts, which is driven by the comparatively larger magnitude of thermal outages than solar forecast errors within our test system. Lastly, the flexibility provided by hybrid and other resources can help reduce system load-shedding event frequencies and enable the system to be more robust to inaccurate forecast information, and alternative hybrid inverter sizes can impact RA levels by 1-2 orders of magnitude. Our results point to the importance of a broader flexibility framework to describe the interaction between (1) flexibility "supply" from both physical resource capabilities and operational constraints considered in the modeling, and (2) flexibility "demand" from forecast errors, thermal generator outages, and other sources of uncertainty, as well as their RA impacts. Results are likely sensitive to the system buildout explored; future work could consider additional system configurations and conditions.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗

DOE OSTI2023

Ab initio study of tungsten-based alloys under fusion power-plant conditions

Tungsten (W) is considered a leading candidate for structural and functional materials in future fusion energy devices. The most attractive properties of tungsten for magnetic and inertial fusion energy reactors are its high melting point, high thermal conductivity, low sputtering yield, and low long-term disposal radioactive footprint. However, tungsten also presents a very low fracture toughness, primarily associated with intergranular failure and bulk plasticity, limiting its applications. In recent years, several families of tungsten-based alloys have been explored to overcome the aforementioned limitations of pure tungsten. These include tungsten-based high-entropy alloys (W-HEAs) and tungsten-based Self-passivating Metal Alloys with Reduced Thermo-oxidation or “SMART alloys” (W-SAs). Given their proximity to the plasma, it is crucial to understand how the exposure of these candidate plasma-facing materials (PFMs) to the neutron fluxes expected in fusion reactors impacts their material behavior over time. In this work, we present a computational approach that combines inventory codes and first-principles DFT electronic structure calculations to understand the behavior of transmuting tungsten-based PFMs. In particular, we calculate the changes in the chemical composition, production uncertainties, the elastic and ductility properties, and the density of states for five tungsten-based PFMs when exposed to EU-DEMO fusion first wall conditions for ten years.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Side Chain Regioisomers that Dictate Optical Properties and Mechanofluorochromism through Crystal Packing

Designing strong interactions into $π$-conjugated materials can direct their typically uncontrolled molecular packing and is an important strategy toward tuning their technologically relevant properties in the solid state. In this paper we demonstrate unconventional control over solid-state fluorescence and mechanofluorochromism (MFC) through regiochemical substitutions in side chains of 12 three-ring phenylene ethynylenes (PEs) that employ fluoroarene–arene (ArF–ArH) interactions between tetrafluoro ArF pendants and PE main chains to direct crystal packing, with single-atom variations of the lone proton in ArF pendants between 4-hydro, 5-hydro, and 6-hydro substitution yielding in two cases dramatically different fluorescence and MFC behavior. X-ray crystal structures reveal key intermolecular interactions of ArF rings, including their lone protons, which vary as a function of substitution pattern and contribute to optical properties in solids. Rarely reported, particularly with such dramatic results, this systematic study on regiochemical effects over solid-state properties demonstrates the versatility of using discrete, directional interactions of side chains for noncovalent control in conjugated materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

A First-Principles Study of Hydrogen Diffusivity and Dissociation on δ-Pu (100) and (111) Surfaces

The diffusivity and chemical reactivity of hydrogen on the δ-Pu (100) and (111) surfaces has been studied with density functional theory using both spin-polarization and spin–orbit coupling calculations. Comparison of the total electronic density of states and atomic hydrogen diffusion energies indicates that spin-polarization yields accurate results, with spin–orbit coupling yielding slightly smaller barriers to hopping between adsorption sites. On the (100) surface, both sets of calculations indicate that the dissociation reaction for molecular hydrogen is highly active at ambient conditions and results in the hydrogen ions bonded within the Pu surface, similar to hydride formation. In contrast, calculations on the (111) surface indicate a lower barrier for dissociation without the formation of the hydride-like end product. Our results help quantify the effect of spin–orbit coupling on hydrogen surface chemistry on δ-Pu. Additionally, we observe that the degree of resulting corrosion could depend on the specific geometry of the plutonium surface, which could have ramifications for future engineering applications.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Controlling Internal Organization of Multilayer Poly(methacrylic acid) Hydrogels with Polymer Molecular Weight

Here, we report on tailoring the internal architecture of multilayer-derived poly(methacrylic acid) (PMAA) hydrogels by controlling the molecular weight of poly(N-vinylpyrrolidone) (PVPON) in hydrogen-bonded (PMAA/PVPON) layer-by-layer precursor films. The hydrogels are produced by cross-linking PMAA in the spin-assisted multilayers followed by PVPON release. We found that the thickness, morphology, and architecture of hydrogen-bonded films and the corresponding hydrogels are significantly affected by PVPON chain length. For all systems, an increase in PVPON molecular weight from M w = 2.5 to 1300 kDa resulted in increased total film thickness. We also show that increasing polymer M w smooths the hydrogen-bonded film surfaces but roughens those of the hydrogels. Using deuterated dPMAA marker layers in neutron reflectometry measurements, we found that hydrogen-bonded films reveal a high degree of stratification which is preserved in the cross-linked films. We observed dPMAA to be distributed more widely in the hydrogen-bonded films prepared with small M w PVPON due to the greater mobility of short-chain PVPON. Furthermore, these variations in the distribution of PMAA are erased after cross-linking, resulting in a distribution of dPMAA over about two bilayers for all M w but being somewhat more widely distributed in the films templated with higher M w PVPON. Finally, our results yield new insights into controlling the organization of nanostructured polymer networks using polymer molecular weight and open opportunities for fabrication of thin films with well-organized architecture and controllable function.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO 2 Reduction

Bimetallic catalysts are promising for the most difficult thermal and electrochemical reactions, but modeling the many diverse active sites on polycrystalline samples is an open challenge. Here, we present a general framework for addressing this complexity in a systematic and predictive fashion. Active sites for every stable low-index facet of a bimetallic crystal are enumerated and cataloged, yielding hundreds of possible active sites. The activity of these sites is explored in parallel using a neural-network-based surrogate model to share information between the many density functional theory (DFT) relaxations, resulting in activity estimates with an order of magnitude fewer explicit DFT calculations. Sites with interesting activity were found and provide targets for follow-up calculations. This process was applied to the electrochemical reduction of CO 2 on nickel gallium bimetallics and indicated that most facets had similar activity to Ni surfaces, but a few exposed Ni sites with a very favorable on-top CO configuration. This motif emerged naturally from the predictive modeling and represents a class of intermetallic CO 2 reduction catalysts. These sites rationalize recent experimental reports of nickel gallium activity and why previous materials screens missed this exciting material. Most importantly these methods suggest that bimetallic catalysts will be discovered by studying facet reactivity and diversity of active sites more systematically.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Catalytic chemoselective functionalization of methane in a metal-organic framework

Methane constitutes the largest fraction of natural gas reserves and is a low-cost abundant starting material for the synthesis of value-added chemicals and fuel. Selective catalytic functionalization of methane remains a vital goal in the chemical sciences due to its low intrinsic reactivity. Borylation has recently emerged as a promising route for the catalytic functionalization of methane. A major challenge in this regard is selective borylation towards the monoborylated product that is more active than methane and can easily lead to over-functionalization. Herein, we report a highly selective microporous metal-organic framework- supported iridium(III) catalyst for methane borylation that exhibits a chemoselectivity of > 99% (mono versus bis at 19.5% yield; turnover number = 67) for monoborylated methane, with bis(pinacolborane) as the borylation reagent in dodecane, at 150 °C and 34 atm of methane, employing as little as 1.8 wt% of iridium. The preference for the monoborylated product is ascribed to the shape-selective effect of the metal-organic framework pore structures.

Zhang, Xuan (ORCID:0000000182147265)↗

DOE OSTI2023

A coarse-grained simulation model for colloidal self-assembly via explicit mobile binders

Colloidal particles with mobile binding molecules constitute a powerful platform for probing the physics of self-assembly. Binding molecules are free to diffuse and rearrange on the surface, giving rise to spontaneous control over the number of droplet–droplet bonds, i.e., valence, as a function of the concentration of binders. This type of valence control has been realized experimentally by tuning the interaction strength between DNA-coated emulsion droplets. Optimizing for valence two yields droplet polymer chains, termed ‘colloidomers’, which have recently been used to probe the physics of folding. To understand the underlying self-assembly mechanisms, here we present a coarse-grained molecular dynamics (CGMD) model to study the self-assembly of this class of systems using explicit representations of mobile binding sites. Further, we explore how valence of assembled structures can be tuned through kinetic control in the strong binding limit. More specifically, we optimize experimental control parameters to obtain the highest yield of long linear colloidomer chains. Subsequently tuning the dynamics of binding and unbinding via a temperature-dependent model allows us to observe a heptamer chain collapse into all possible rigid structures, in good agreement with recent folding experiments. Our CGMD platform and dynamic bonding model (implemented as an open-source custom plugin to HOOMD-Blue) reveal the molecular features governing the binding patch size and valence control, and opens the study of pathways in colloidomer folding. This model can therefore guide programmable design in experiments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Optimizing dicyandiamide pretreatment conditions for enhanced structure and electronic properties of polymeric graphitic carbon nitride

Graphitic carbon nitride, a polymeric semiconductor, possesses a distinctive electronic band structure and exceptional chemical stability, making it a highly promising material for various catalytic applications such as electrocatalysis, photocatalysis, and photo-electrocatalysis. However, its practical applications remain limited due to its low active site density and poor electrical conductivity. In this study, to overcome such limitations, we have conducted a thorough investigation to explore the impact of dicyandiamide (DCDA) precursor pretreatment prior to thermal polymerization to graphitic carbon nitride. The DCDA precursor was subjected to various pretreatment methods including grinding using mortar and pestle, recrystallization through stirring or probe sonication after dissolving in deionized water, and freeze drying, prior to thermal polymerization at 550 °C for 2 hours. The structural and morphological properties of the catalysts prepared were compared and characterized by X-ray diffraction (XRD), attenuated total reflectance–Fourier transform infrared (ATR-IR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS) analysis while the electronic band gap properties were determined based on density functional theory (DFT) calculations for a set of crystalline systems having C/N ratios similar to those identified experimentally. In comparison to direct thermal polymerization, pretreated samples rendered the same product yield, diverse morphologies with flat or wrinkle structures, and reduced electrochemical resistance, making them suitable for use in various catalytic processes.

36 MATERIALS SCIENCE↗