Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “predictive synthesis”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Deep-Learning-Based Koopman Modeling for Online Control Synthesis of Nonlinear Power System Transient Dynamics

Power system stability and control have become more challenging due to the increasing uncertainty associated with renewable generation. Here, the performance of conventional control is highly driven by the physics-based offline-developed dynamic models that can deviate from the actual system characteristics under different operating conditions and/or configurations. Data-driven approaches based on online measurements can be a better solution to addressing these issues by capturing real-time operation conditions. This article describes a novel fully data-driven probabilistic framework to derive a linear representation of postcontingency grid dynamics and online prescribe control based on the derived model to enhance transient stability. The complex nonlinear power system dynamics is approximated by a linear model by using multiple neural network modules that infer distributions of the observations and introducing a Koopman layer to sample possible Koopman linear models from the inferred distributions. The trained model features linearity that can be easily incorporated into the existing linear control design paradigm and ease the controller design process. The effectiveness of Koopman-based control designs is validated through comparative case studies, which demonstrate increased prediction accuracy and control performance when applied to a power system with heterogeneous generator dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Synthesis and thermodynamics of uranium-incorporated α-Fe 2 O 3 nanoparticles

Hematite nanoparticles were synthesized with U(VI) in circumneutral water through a coprecipitation and hydrothermal treatment process. XRD, TEM, and EXAFS analyses reveal that uranium may aggregate along grain boundaries and occupy Fe sites within hematite. The described synthesis method produces crystalline, single-phase iron oxide nanoparticles absent of surface-bound uranyl complexes. EXAFS data were comparable to spectra from existing studies whose syntheses were more representative of naturally occurring, extended aging processes. Herein this work provides and validates an accelerated method of synthesizing uranium-immobilized iron oxide nanoparticles for further mechanistic studies. High temperature oxide melt solution calorimetry measurements were performed to calculate the thermodynamic stability of uranium-incorporated iron oxide nanoparticles. Increasing uranium content within hematite resulted in more positive formation enthalpies. Standard formation enthalpies of U x Fe 2–2x O 3 were as high as 76.88 ± 2.83 kJ/mol relative to their binary oxides, or -764.04 ± 3.74 kJ/mol relative to their constituent elements, at x = 0.037. Data on the thermodynamic stability of uranium retention pathways may assist in predicting waste uranyl remobilization, as well as in developing more effective methods to retain uranium captured from aqueous environments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Identification of novel organic polar materials: A machine learning study with importance sampling

Recent advances in the synthesis of polar molecular materials have produced practical alternatives to ferroelectric ceramics, opening up exciting new avenues for their incorporation into modern electronic devices. However, in order to realize the full potential of polar polymer and molecular crystals for modern technological applications, it is paramount to assemble and evaluate all the available data for such compounds, identifying descriptors that could be associated with an emergence of ferroelectricity. In this paper, we utilized data-driven approaches to judiciously shortlist candidate materials from a wide chemical space that could possess ferroelectric functionalities. A machine learning study with importance sampling was employed to address the challenge of having a limited amount of available data on already-known organic ferroelectrics. Sets of molecular- and crystal-level descriptors were combined with a Random Forest Regression algorithm in order to predict the spontaneous polarization of the shortlisted compounds. First-principles simulations were performed to further validate the predictions obtained from the machine learning model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Uncertainty-aware mixed-variable machine learning for materials design

Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiscale Modeling of Vinyl-Addition Polynorbornenes: The Effect of Stereochemistry

Vinyl-addition polynorbornenes are candidates for designing high-performance polymers due to unique characteristics, which include a high glass transition temperature associated with a rigid backbone. Recent studies have established that the processability and properties of these polymers can be fine-tuned by using targeted substitutions. However, synthesis with different catalysts results in materials with distinct properties, potentially due to the presence of various stereoisomers that are difficult to quantify experimentally. Herein, we develop all-atom models of polynorbornene oligomers based on classical force fields and density functional theory. To establish the relationship between chemical architecture, chain conformations, and melt structure, we perform detailed molecular dynamics simulations with the fine-tuned atomistic force field and propose simpler coarse-grained descriptions to address the high molecular weight limit. All-atom simulations of oligomers suggest high glass transition temperatures in the range of 550–600 K. In the melt state (800 K), meso chains form highly rigid extended coils (C∞≈11) with amorphous structural characteristics similar to the X-ray diffraction data observed in the literature. In contrast, simulations with racemo chains predict highly helical tubular chain conformations that could promote assembly into crystalline structures.

Polymer Science↗
DOE OSTI2023

Warming decreases desert ecosystem functioning by altering biocrusts in drylands

Warming and precipitation fluctuations are changing desert ecosystems in global drylands. However, the effects of climate change on keystone species such as cryptogamic biocrust in drylands remain relatively under-investigated, even though biocrusts play a vital role in desert ecosystems. Here we conducted a long-term experiment (14 years) to simulate the responses of two main types of biocrusts to warming coupled with reduction in precipitation that was achieved by open-top chambers (OTCs) to simulate the predicted warming and precipitation decreasing under climate change scenario. We also conduct a structural analysis to evaluate the resulting changes in desert ecosystem functioning (carbon and nitrogen cycling). Neither warming and corresponding rainfall reduction treatments had a negative effect on lichen species richness, but both treatments reduced lichen cover and biomass. The negative effects of warming on moss-dominated crusts were much greater than those on lichen-dominated crusts. Although mosses and lichens had varying degree responses to warming, the loss of mosses and decreased lichen cover and biomass, as well as the shortening of the wet time, resulted in a reduction in carbon and nitrogen fixation, soil enzyme activity and water-holding capacity of biocrusts and topsoil. These impacts collectively change the water balance of drylands and weaken the hydrological and biogeochemical function of biocrusts. Synthesis and applications: Results from this long-term experiment suggest that the ecosystem C and N cycling and water balance of global drylands may be highly impacted by climate change, in part because of the response of biocrusts, which contribute an important implication for both dryland restoration and earth system dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Synthesis of observed and simulated rain microphysics to inform a new Bayesian statistical framework for microphysical parameterization in climate models (Final Scientific Report)

This project aimed to investigate a new approach to bulk microphysics schemes. We identified the need to move beyond the fixed structural assumptions and approximation of existing schemes. For example, most schemes assume some functional form for the rain drop size distribution (e.g. a gamma or exponential distribution). Most schemes then evolve some number of statistical moments of that distribution via various processes, such as evaporation, sedimentation, collision-coalescence, and collisional breakup. These microphysical processes, in turn, are typically some fixed functional form derived from either some other (more detailed) model, or using some single-particle rates that are then integrated over the size distribution. While these process rate functions may have some free parameters to adjust, in other cases doing so is impossible (e.g. when the process rates are analytical or piecewise solutions to some target function). Our goal was to build and test a scheme that assumed no size distribution form, and used a series of power laws as the basis for the process rates. Moments of the size distribution would be predicted, but no underlying size distribution would be specified. Any number or choice of size distribution moments could be used, and any number of power laws could be employed to model the process rates. Thus, our approach is flexible, and can seamlessly scale across levels of complexity, as demanded by the data. Bayesian inference then would provide the formalism to estimate the model parameters and structure, allowing for robust uncertainty quantification. We proposed testing this framework in idealized simulations using bin microphysical schemes as a data source. We also proposed using real data to inform our microphysics scheme, and also that we would integrate our scheme into WRF.

58 GEOSCIENCES↗
DOE OSTI2024

Versatile stochastic model for predictive KMC simulation of fcc metal nanostructure evolution with realistic kinetics

Stochastic lattice-gas models provide the natural framework for analysis of the surface diffusion-mediated evolution of crystalline metal nanostructures on the appropriate time scale (often 10 1 –10 4 s) and length scale. Model behavior can be precisely assessed by kinetic Monte Carlo simulation, typically incorporating a rejection-free algorithm to efficiently handle the broad range of Arrhenius rates for hopping of surface atoms. The model should realistically prescribe these rates, or the associated barriers, for a diversity of local surface environments. However, commonly used generic choices for barriers fail, even qualitatively, to simultaneously describe diffusion for different low-index facets, for terrace vs step edge diffusion, etc. We introduce an alternative Unconventional Interaction–Conventional Interaction formalism to prescribe these barriers, which, even with few parameters, can realistically capture most aspects of behavior. Here, the model is illustrated for single-component fcc metal systems, mainly for the case of Ag. It is quite versatile and can be applied to describe both the post-deposition evolution of 2D nanostructures in homoepitaxial thin films (e.g., reshaping and coalescence of 2D islands) and the post-synthesis evolution of 3D nanocrystals (e.g., reshaping of nanocrystals synthesized with various faceted non-equilibrium shapes back to 3D equilibrium Wulff shapes).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Inverse size-dependent Stokes shift in strongly quantum confined CsPbBr 3 perovskite nanoplates

Colloidal semiconductor nanocrystals (NCs) are used as bright chromatic fluorophores for energy-efficient displays. Here we focus here on the size-dependent Stokes shift for CsPbBr 3 nanocrystals. The Stokes shift, i.e., the difference between the wavelengths of absorption and emission maxima, is crucial for display application, as it controls the degree to which light is reabsorbed by the emitting material reducing the energetic efficiency. One major impediment to the industrial adoption of NCs is that slight deviations in manufacturing conditions may result in a wide dispersion of the product's properties. A data-driven analysis of over 2000 reactions comparing two data sets, one produced via standard colloidal synthesis and the other via high-throughput automated synthesis is discussed. We show that differences in the reaction conditions of colloidal CsPbBr 3 nanocrystals yield nanocrystals with opposite Stokes shift size-dependent trends. These match the morphologies of two-dimensional nanoplatelets (NPLs) and nanocrystal cubes. The Stokes shift size dependence trend of NPLs and nanocubes is non-monotonic indicating different physics is at play for the two nanocrystal morphologies. For nanocrystals with cubic shape, with the increase of edge length, there is a significant decrease in Stokes shift values. However, for NPLs with the increase of thickness (1–4 ML), Stokes shift values will increase. The study emphasizes the transition from a spectroscopic point of view and relates the two Stokes shift trends to 2D and 0D exciton dimensionalities for the two morphologies. Our findings highlight the importance of CsPbBr 3 nanocrystal morphology for Stokes shift prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Ultrathin Boron Growth onto Nanodiamond Surfaces via Electrophilic Boron Precursors

Diamond as a templating substrate is largely unexplored, and the unique properties of diamond, including its large bandgap, thermal conductance, and lack of cytotoxicity, makes it versatile in emergent technologies in medicine and quantum sensing. Surface termination of an inert diamond substrate and its chemical reactivity are key in generating new bonds for nucleation and growth of an overlayer material. Oxidized high-pressure high temperature (HPHT) nanodiamonds (NDs) are largely terminated by alcohols that act as nucleophiles to initiate covalent bond formation when an electrophilic reactant is available. In this work, we demonstrate a templated synthesis of ultrathin boron on ND surfaces using trigonal boron compounds. Boron trichloride (BCl 3 ), boron tribromide (BBr 3 ), and borane (BH 3 ) were found to react with ND substrates at room temperature in inert conditions. BBr 3 and BCl 3 were highly reactive with the diamond surface, and sheet-like structures were produced and verified with electron microscopy. Surface-sensitive spectroscopies were used to probe the molecular and atomic structure of the ND constructs’ surface, and quantification showed the boron shell was less than 1 nm thick after 1–24 h reactions. Observation of the reaction supports a self-terminating mechanism, similar to atomic layer deposition growth, and is likely due to the quenching of alcohols on the diamond surface. X-ray absorption spectroscopy revealed that boron-termination generated midgap electronic states that were originally predicted by density functional theory (DFT) several years ago. DFT also predicted a negative electron surface, which has yet to be confirmed experimentally here. The boron-diamond nanostructures were found to aggregate in dichloromethane and were dispersed in various solvents and characterized with dynamic light scattering for future cell imaging or cancer therapy applications using boron neutron capture therapy (BNCT). The unique templating mechanism based on nucleophilic alcohols and electrophilic trigonal precursors allows for covalent bond formation and will be of interest to researchers using diamond for quantum sensing, additive manufacturing, BNCT, and potentially as an electron emitter.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Raman Digital Twin of Monolayer Janus Transition Metal Dichalcogenides

Monolayer transition metal dichalcogenides (TMDs) are a key class of two-dimensional (2D) materials with broad technological potential. Their Janus counterparts exhibit unique properties due to broken out-of-plane symmetry and further enrich the functionalities of TMDs. However, experimental synthesis and identification of Janus TMDs remain challenging. It is thus highly desirable to have a rapid, simple, and in situ characterization technique to monitor, in real time, the conversion process from the parent to Janus structure. Raman spectroscopy stands out for such a task as it is a powerful, nondestructive, and very commonly used tool to characterize 2D materials both in situ and ex situ. To realize the full potential of Raman spectroscopy on rapid characterization of Janus TMDs, we present a computational “Raman digital twin” library for various monolayer Janus TMDs in both 2H and Td phases. We focus on group-6 TMDs: MoS 2 , WS 2 , MoSe 2 , WSe 2 , MoTe 2 , WTe 2 and their Janus variants: MoSSe, MoSTe, MoSeTe, WSSe, WSTe, and WSeTe. Using first-principles density functional theory (DFT), we calculate their vibrational properties and predict distinct Raman fingerprints. These phonon and Raman signatures reflect each material’s structural symmetry and atomic composition, enabling clear identification via Raman spectroscopy. Our theoretical work supports experimental efforts by providing benchmarks for material identification, structural analysis, and quality control. In conclusion, the computational library expedites the discovery and development of Janus 2D materials, facilitating tighter integration between theoretical predictions and experimental validation.

Chalcogenides↗
DOE OSTI2026

Prediction of dynamic behavior of a system undergoing an exchange of components using a transmission simulator method

In systems with modular design, it is of interest to perform component exchanges to upgrade components and explore new system configurations. If the dynamics of the components are known, then dynamic substructuring can be used to obtain a prediction of system performance for the new configuration. The transmission simulator method is a substructuring technique that utilizes an additional fixture to apply an elastic boundary condition to a test article such that an appropriate dynamic response can be obtained for a given substructuring application, and the technique has been used widely for performing coupling and decoupling operations. Here, in this work, a modified transmission simulator method is proposed that allows a direct exchange of components to be performed. In a numerical demonstration, the proposed method is shown to accurately predict the dynamics of an assembly that has undergone a component exchange in which the exchanged components have different material properties and different geometric properties. This approach can be applied to dynamic qualification frameworks in which structures undergo modifications or upgrades rather than complete redesigns.

Component exchange↗
DOE OSTI2022

Band gap and electronic structure of defects in the ternary nitride BP 3 N 6 : experiment and theory

Recent advances in methods to access nitride systems by a high-pressure high-temperature approach have made possible the one-step synthesis of mixed ternary non-metal nitrides. As a prerequisite to use in a practical device, it is important to understand important bulk electronic properties, such as the band gap, as well as characterizing the presence and effect of defects that are present. In this work, the novel ternary nitride BP 3 N 6 is studied using techniques sensitive to the partial electronic density of states, specifically X-ray absorption spectroscopy and X-ray emission spectroscopy. Complementary full-potential all-electron density functional theory (DFT) calculations allow important bulk electronic parameters, such as the band gap, to be elucidated. The band gap of BP 3 N 6 has been determined to be 3.9 ± 0.2 eV and 4.1 ± 0.4 eV at the B K- and N K-edges, respectively. This is close to a theoretical value of 4.3 eV predicted by the PBEsol exchange–correlation functional and considerably less than a value of 5.8 eV predicted by the modified Becke–Johnson exchange–correlation functional. X-Ray excited optical luminescence (XEOL) measurements are performed to interrogate the presence of point defects in this system. Together with DFT calculations, these measurements reveal the presence of nitrogen vacancies which lead to multiple mid-gap trap states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Forest Age Rivals Climate to Explain Reproductive Allocation Patterns in Forest Ecosystems Globally

ABSTRACT Forest allocation of net primary productivity (NPP) to reproduction (carbon required for flowers, fruits, and seeds) is poorly quantified globally, despite its critical role in forest regeneration and a well‐supported trade‐off with allocation to growth. Here, we present the first global synthesis of a biometric proxy for forest reproductive allocation (RA) across environmental and stand age gradients from a compiled dataset of 824 observations across 393 sites. We find that ecosystem‐scale RA increases ~60% from boreal to tropical forests. Climate shows important non‐linear relationships with RA, but is not the sole predictor. Forest age effects are comparable to climate in magnitude (MAT: ß = 0.24,p = 0.021; old growth forest: ß = 0.22,p < 0.001), while metrics of soil fertility show small but significant relationships with RA (soil pH: ß = 0.07,p = 0.001; soil N: ß = −0.07,p = 0.001). These results provide strong evidence that ecosystem‐scale RA is mediated by climate, forest age, and soil conditions, and is not a globally fixed fraction of positive NPP as assumed by most vegetation and ecosystem models. Our dataset and findings can be used by modellers to improve predictions of forest regeneration and carbon cycling.

Environmental Sciences & Ecology↗
DOE OSTI2022

Identification of structural transitions in bacterial fatty acid binding proteins that permit ligand entry and exit at membranes

Fatty acid (FA) transfer proteins extract FA from membranes and sequester them to facilitate their movement through the cytosol. Detailed structural information is available for these soluble protein–FA complexes, but the structure of the protein conformation responsible for FA exchange at the membrane is unknown. Staphylococcus aureus FakB1 is a prototypical bacterial FA transfer protein that binds palmitate within a narrow, buried tunnel. Here, we define the conformational change from a “closed” FakB1 state to an “open” state that associates with the membrane and provides a path for entry and egress of the FA. Using NMR spectroscopy, we identified a conformationally flexible dynamic region in FakB1, and X-ray crystallography of FakB1 mutants captured the conformation of the open state. In addition, molecular dynamics simulations show that the new amphipathic α-helix formed in the open state inserts below the phosphate plane of the bilayer to create a diffusion channel for the hydrophobic FA tail to access the hydrocarbon core and place the carboxyl group at the phosphate layer. The membrane binding and catalytic properties of site-directed mutants were consistent with the proposed membrane docked structure predicted by our molecular dynamics simulations. Finally, the structure of the bilayer-associated conformation of FakB1 has local similarities with mammalian FA binding proteins and provides a conceptual framework for how these proteins interact with the membrane to create a diffusion channel from the FA location in the bilayer to the protein interior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

The critical role of intrinsic defects and many-body interactions on the stability of MnBi2Te4

Intrinsic antisite defects pose a major challenge to understanding and predicting the exotic properties of the layered topological magnetic insulator MnBi2Te4 (MBT). In this work, we study the origin of the abundance of intrinsic defects in MBT, including many-body defect–defect interactions and many-body electronic correlations. Until now, ab initio methods have struggled to explain thermodynamic stability and properties influenced by defect behavior in MBT. We model native Mn–Bi antisite defects in MBT at finite temperatures using a cluster expansion that includes defect–defect interactions. To overcome the limitations of conventional density functional theory (DFT), we introduce a hybrid approach that incorporates high-accuracy quantum Monte Carlo (QMC) calculations, introducing missing correlations. This strategy allows for accurate estimation of defect energetics and finite-temperature properties. We compute the configurational free energy, defect concentration, and configurational heat capacity, revealing a second-order order–disorder phase transition near the experimental synthesis temperature. Our study provides the first theoretical insight into the thermodynamics of intrinsic defects in MBT. The negative free energy relative to pristine MBT at synthesis temperatures indicates that Mn–Bi antisite formation is thermodynamically spontaneous. We also present a broadly applicable general framework for correcting low-level theoretical theories using highly accurate many-body corrections from QMC.

Ghaffar, Abdul [ORNL] (ORCID:0000000241190168)↗
DOE OSTI2023

Control of Cement Timing, Mineralogy, and Texture on Hydro-chemo-mechanical Coupling from CO2 Injection into Sandstone: A Synthesis

Carbon capture, utilization, and storage (CCUS) has been widely applied to enhance oil recovery (CO2-EOR). A thorough investigation of the impact of injecting CO2 into a heterogeneous reservoir is critical to understanding the overall reservoir robustness and storage performance. We conducted fifteen flow-through tests on Morrow B sandstone that allowed for chemical reactions between a CO2-rich brackish solution and the sandstones, and four creep/flow-through tests that simultaneously allowed for chemical reactions and stress monitoring. From fluid chemistry and X-ray computed tomography, we found that the dissolution of disseminated cements and the precipitation of iron-rich clays did not significantly affect the permeability and geomechanical properties. Minor changes in mechanical properties from Brazilian and creep tests indicated that the matrix structure was well-supported by early diagenetic quartz overgrowth cement and the reservoir’s compaction history at deep burial depths. However, one sample experienced a dissolution of poikilotopic calcite, leading to a permeability increase and significant tensile strength degradation due to pore opening, which overcame the effect of the early diagenetic cements. We concluded that the Morrow B sandstone reservoir is robust for CO2 injection. Most importantly, cement timing, the abundance and texture of reactive minerals, and the reservoir’s burial history are critical in predicting reservoir robustness and storage capacity for CO2 injection.

Wu, Zhidi (ORCID:0000000338869492)↗
DOE OSTI2022

Synthesis of CdZnTeSe single crystals for room temperature radiation detector fabrication: mitigation of hole trapping effects using a convolutional neural network

In this article, we report the growth of Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 (CZTS) wide bandgap semiconductor single crystals for room temperature gamma-ray detection using a modified vertical Bridgman method. Charge transport properties measured in the radiation detectors, fabricated from the grown CZTS crystals, indicated signs of hole trapping. Hole traps inhibit high-resolution radiation detection especially for energetic gamma rays. Machine learning (ML) applications are gaining tremendous mpetus in improving device and sensor performance by compensating for limi tations arising from such intrinsic material properties. In this article, we describe a deep convolutional neural network (CNN) that has demonstrated remarkable efficiency in identifying the energy of a gamma photon detected by a CZTS detector. The CNN has been trained using simulated data that resemble output pulses from actual CZTS detectors when exposed to 662-keV gamma photons. The device properties required for the simulation have been derived from radiation detection measurements on a real Cd 0.9 Zn 0.1 Te 0.97 Se 0.03 detector fabricated in our laboratory. The CNN has been trained with detector pulses arising through photoelectric (PE) and Compton scattering (CS) separately. The percentage error in predicting the detected energies, within an extremely small duration of 0.28 ms, was found to be lower than 0.1% for gamma energies above 50 keV and for training datasets con taining PE and CS events separately. The CNN was also validated for a mixed PE and CS dataset to obtain a prediction error of 1%. Additionally, the effect of detector resolution on the efficiency of the CNN was also explored.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗