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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Understanding the role of negative charge in the scaffold of an artificial enzyme for CO 2 hydrogenation on catalysis

Here, we have approached the construction of an artificial enzyme by employing a robust protein scaffold, lactococcal multidrug resistance regulator, LmrR, providing a structured secondary and outer coordination spheres around a molecular rhodium complex, [Rh I (P Et2 N gly P Et2 ) 2 ] - . Previously, we demonstrated a 2–3 fold increase in activity for one Rh-LmrR construct by introducing positive charge in the secondary coordination sphere. In this study, a series of variants was made through site-directed mutagenesis where the negative charge is located in the secondary sphere or outer coordination sphere, with additional variants made with increasingly negative charge in the outer coordination sphere while keeping a positive charge in the secondary sphere. Placing a negative charge in the secondary or outer coordination sphere demonstrates decreased activity by a factor of two compared to the wild-type Rh-LmrR. Interestingly, addition of positive charge in the secondary sphere, with the negatively charged outer coordination sphere restores activity. Vibrational and NMR spectroscopy suggest minimal changes to the electronic density at the rhodium center, regardless of inclusion of a negative or positive charge in the secondary sphere, suggesting another mechanism is impacting catalytic activity, explored in the discussion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Paenibacillus aquistagni sp. nov., isolated from an artificial lake accumulating industrial wastewater

Strain 11 T was isolated from water of an artificial lake accumulating industrial wastewater on the outskirts of Celje, Slovenia. In this paper, phenotypic characterisation showed strain 11 T to be a Gram-stain positive, spore forming bacterium. The 16S rRNA gene sequence identified strain 11 T as a member of the genus Paenibacillus, closely related to Paenibacillus alvei (96.2%). Genomic similarity with P. alvei 29 T was 73.1% (gANI), 70.2% (ANIb), 86.7% (ANIm) and 21.7 ± 2.3% (GGDC). The DNA G+C content of strain 11 T was determined to be 47.5%. The predominant menaquinone of strain 11 T was identified as MK-7 and the major fatty acid as anteiso-C 15:0 . The peptidoglycan was found to contain meso-diaminopimelic acid. In contrast to its close relatives P. alvei DSM 29 T , Paenibacillus apiarius DSM 5581 T and Paenibacillus profundus NRIC 0885 T , strain 11 T was found to be able to ferment d-fructose, d-mannose and d-xylose. A draft genome of strain 11 T contains a cluster of genes associated with type IV pilin synthesis usually found in clostridia, and only sporadically in other Gram-positive bacteria. Finally, genotypic, chemotaxonomic, physiological and biochemical characteristics of strain 11 T presented in this study support the creation of a novel species within the genus Paenibacillus, for which the name Paenibacillus aquistagni sp. nov. is proposed, with strain 11 T (=ZIM B1027 T =LMG 29561 T =CCM 8679 T ) as the type strain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Deep-learning based artificial intelligence tool for melt pools and defect segmentation

Accelerating fabrication of additively manufactured components with precise microstructures is important for quality and qualification of built parts, as well as for a fundamental understanding of process improvement. Accomplishing this requires fast and robust characterization of melt pool geometries and structural defects in images. This paper proposes a pragmatic approach based on implementation of deep learning models and self-consistent workflow that enable systematic segmentation of defects and melt pools in optical images. Deep learning is based on an image-to-image translation–conditional generative adversarial neural network architecture. An artificial intelligence (AI) tool based on this deep learning model enables fast and incrementally more accurate predictions of the prevalent geometric features, including melt pool boundaries and printing-induced structural defects. We present statistical analysis of geometric features that is enabled by the AI tool, showing strong spatial correlation of defects and the melt pool boundaries. The correlations of widths and heights of melt pools with dataset processing parameters show the highest sensitivity to thermal influences resulting from laser passes in adjacent and subsequent layer passes. The presented models and tools are demonstrated on the aluminum alloy and datasets produced with different sets of processing parameters. However, they have universal quality and could easily be adapted to different material compositions. The method can be easily generalized to microstructural characterizations other than optical microscopy.

additive manufacturing↗
DOE OSTI2018

Creating Spin-One Fermions in the Presence of Artificial Spin–Orbit Fields: Emergent Spinor Physics and Spectroscopic Properties

We propose the creation and investigation of a system of spin-one fermions in the presence of artificial spin–orbit coupling, via the interaction of three hyperfine states of fermionic atoms to Raman laser fields. We explore the emergence of spinor physics in the Hamiltonian described by the interaction between light and atoms, and analyze spectroscopic properties such as dispersion relation, Fermi surfaces, spectral functions, spin-dependent momentum distributions and density of states. Connections to spin-one bosons and SU(3) systems is made, as well relations to the Lifshitz transition and Pomeranchuk instability are presented.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Removal of Congo Red from Aqueous Solution by Hydroxyapatite Nanoparticles Loaded on Zein as an Efficient and Green Adsorbent: Response Surface Methodology and Artificial Neural Network-Genetic Algorithm

This study is based on the application of hydroxyapatite nanoparticles loaded on Zein (Zein/nHAp) as an efficient adsorbent for the removal of Congo red from aqueous solutions. The properties of the adsorbent were characterized using various techniques including FT-IR, XRD, FE-SEM, and BET. The influence of five parameters such as pH, temperature, contact time, initial dye concentration, and adsorbent dosage on the removal percentage of the dye was investigated. The optimum conditions of 5.83, 34.32 °C, 5.20 min, 392.10 ppm, and 0.007 g were achieved for pH, temperature, contact time, initial dye concentration, and adsorbent dosage, respectively. The maximum removal percentage of 99.48% was obtained under the optimum condition that exhibited high adsorption potential of the used adsorbent. Central composite design (CCD) under response surface methodology and artificial neural network-genetic algorithm (ANN-GA) were utilized for optimization of parameters. Comparison of the results of the two models in terms of coefficient of determination (R{sup 2}) and mean absolute percentage error confirmed the prediction potential of CCD and ANN-GA. Higher ability and accuracy of ANN-GA in prediction was found based on given results. The experimental equilibrium data were studied by Langmuir, Freundlich, Temkin and Dubinin-Radushkevic isotherm models and explored that the data well presented by Langmuir model with maximum adsorption capacity of 416.66 mg/g. The adsorption kinetic was well-fitted by the pseudo-second-order model. The thermodynamics of the adsorption displayed spontaneous and endothermic nature of the process. Regeneration investigation showed that Zein/nHAp can impressively be reused, indicating that the adsorbent was a promising one for the removal of Congo red from aqueous solution. Graphical Abstract: .

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2017

Waterlogging risk assessment based on self-organizing map (SOM) artificial neural networks: A case study of an urban storm in Beijing

Due to rapid urbanization, waterlogging induced by torrential rainfall has become a global concern and a potential risk affecting urban habitant's safety. Widespread waterlogging disasters have occurred almost annually in the urban area of Beijing, the capital of China. Based on a self-organizing map (SOM) artificial neural network (ANN), a graded waterlogging risk assessment was conducted on 56 low-lying points in Beijing, China. Social risk factors, such as Gross domestic product (GDP), population density, and traffic congestion, were utilized as input datasets in this study. The results indicate that SOM-ANN is suitable for automatically and quantitatively assessing risks associated with waterlogging. The greatest advantage of SOM-ANN in the assessment of waterlogging risk is that a priori knowledge about classification categories and assessment indicator weights is not needed. As a result, SOM-ANN can effectively overcome interference from subjective factors, producing classification results that are more objective and accurate. In this paper, the risk level of waterlogging in Beijing was divided into five grades. As a result, the points that were assigned risk grades of IV or V were located mainly in the districts of Chaoyang, Haidian, Xicheng, and Dongcheng.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Biocompatibility of Hydrogen-Diluted Amorphous Silicon Carbide Thin Films for Artificial Heart Valve Coating

Amorphous silicon carbide (a-SiC:H) thin films were synthesized using trichloromethylsilane by a hot wire chemical vapor deposition process. The deposited films were characterized by Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, x-ray diffraction and x-ray photoelectron spectroscopy to confirm its chemical bonding, structural network and composition of the a-SiC:H films. The optical microscopy images reveal that hydrogen dilution increased the surface roughness and pore density of a-SiC:H thin film. The Raman spectroscopy and FTIR spectra reveal chemical network consisting of Si-Si, C-C and Si-C bonds, respectively. The XRD spectroscopy and Raman spectroscopy indicate a-SiC:H still has short-range order. In addition, in vitro cytotoxicity test ensures the behavior of cell–semiconductor hybrid to monitor the proper coordination. The live–dead assays and MTT assay reveal an increase in green nucleus cell, and cell viability is greater than 88%, respectively, showing non-toxic nature of prepared a-SiC:H film. Moreover, the result indicated by direct contact assay, and cell prefers to adhere and proliferate on a-SiC:H thin films having a positive effect as artificial heart valve coating material.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An Artificial Neural Network Model to Predict the Bainite Plate Thickness of Nanostructured Bainitic Steels Using an Efficient Network-Learning Algorithm

Nanostructured bainitic steel has an extraordinary ultrahigh strength of about 2.0 GPa along with good toughness of 30 MPa m{sup 1/2}. The finer thickness of plate 20-80 nm is largely responsible for achieving such a large hardness of 690-720 HV. In this work, a multilayer perceptron-based artificial neural network (ANN) model has been developed to predict the thickness of bainite plate pertaining to nanostructured bainitic steels. The inputs of the ANN model are, namely, Gibbs free energy for bainitic transformation, austenite strength, transformation temperature for bainite and carbon concentration in the steel. The model prediction revealed that the bainite plate thickness critically depends on austenite strength and Gibbs free energy. From the neural prediction, it is concluded that formation of nanostructured bainitic steel is feasible only if sufficient austenite strength (above 165 MPa) has been achieved. This can be accomplished with a minimum carbon content of 0.5 wt.% in steel and transformation temperature below 300 °C. Higher driving force (Gibbs free energy) below − 1800 J/mol is another prerequisite condition of formation of nanostructured bainite steel. The network-learning architecture has been optimized using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm to minimize the network training error within eight training cycles. The algorithm facilitates a faster convergence of network training and testing errors.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Using an Artificial Neural Network for Nondestructive Evaluation of the Heat Treating Processes for D2 Tool Steels

In nondestructive evaluation (NDE) of heat-treated steels, different variables of the heat treating process have complex effects on outputs of NDE methods, and hence, the effect of one desired variable on NDE outputs should be evaluated to help interpreting the changes. In the present paper, the potential of the magnetic hysteresis method was evaluated for simultaneous detection of the austenitizing and tempering temperatures of AISI D2 samples parts subjected to different heat treatment conditions. To produce the microstructural changes, five groups of the samples were austenitized at 1025-1130 °C, quenched in oil and finally each group was tempered in the range 200-650 °C. SEM and x-ray diffractometry techniques were used to characterize different produced microstructures. For accurate and simultaneous prediction of tempering and austenitizing temperatures, an artificial neural network (ANN) was implemented for magnetic hysteresis outputs including magnetic saturation, coercivity and maximum differential permeability. The study revealed that the magnetic NDE system coupled to ANN has the ability to be adopted as an effective expert NDE tool to predict heat treatment effects on D2 tool steels.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Comparative Study on Artificial Neural Network, Phenomenological-Based Constitutive and Modified Fields–Backofen Models to Predict Flow Stress in Ti-4Al-3V-2Mo-2Fe Alloy

The flow curve was obtained from the superplastic tensile tests in the temperature range of 1033-1153 K and strain rate range of 0.001-0.1 s{sup −1}. Results showed that strain hardening (SH) was insensitive to deformation parameter variation but with the nature of material, while stable state (SS) and dynamic softening (DS) were significantly associated with a thermal activated process. The occurrence of secondary hardening and softening was attributed to discontinuous dynamic recrystallization. And the generation of necklace microstructure at grain boundaries was caused by bulging. Based on the experimental data, the artificial neural network (ANN), phenomenological-based constitutive (PBC) and modified Fields–Backofen (MFB) models were developed, and their predictability and adaptability for the description of the flow behavior during hot processing were evaluated by analyzing the employed standard statistical parameters. Comparatively, ANN model is capable of tracing the complex flow behavior including SH, SS and DS substages. Concerning PBC model, it can not only predict flow stress with satisfactory accuracy, but also represent the flow trend with the larger strain. MFB model adapts well to the flow curve with a broad SH region without DS effect.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Influence of Temperature Variation on the Formation and Corrosion Protective Performance of Calcium Carbonate Deposits in Artificial Seawater

The aim of this research was to investigate the influence of temperature variation on the formation and protective behavior of calcium carbonate scale deposited on steel electrodes in artificial seawater. Chronoamperometry measurement, electrochemical impedance spectroscopy (EIS), scanning electron microscope (SEM) and x-ray diffraction (XRD) were employed for analysis. An increase in temperature from 20 to 50 °C accelerated the oxygen reduction reaction, which increased the nucleation/deposition rate. The chronoamperometry, SEM and XRD results indicated that complete coverage of the electrode surface was achieved at higher temperatures because a denser layer of scale deposits covered the entire surface of the metal and acted as a barrier against the passage of oxygen. The EIS results showed that better corrosion protection was obtained after deposition that included a stepwise decrease in temperature, indicating fewer pathways through the deposited layer onto the electrode surface.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Hot Flow Curve Description of CuFe2 Alloy via Different Artificial Neural Network Approaches

In this research, a mathematical description of hot flow curves of CuFe2 copper alloy has been assembled. Experimental flow curves of the investigated alloy were created on the basis of hot compression dataset. This dataset was acquired in the temperature range of 923-1223 K and the strain rate range of 0.1-10 s{sup −1}, with the maximum true strain value of 1.0. The experimental flow curves were described by two artificial neural network approaches. In the first case, a neural network has been created to approximate the experimental flow curves with respect to the true strain, strain rate and temperature. In the second case, a hybrid approach based on the combination of predictive models with neural networks has been utilized. In this case, five neural networks were used to describe parameters of these models in relation to the temperature and strain rate. Results have shown that the hybrid approach allows an accurate description of the experimental data and also provides more reliable prediction.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A mathematical discussion concerning the performance of multilayer perceptron-type artificial neural networks through use of orthogonal bipolar vectors

This work proposes the use of orthogonal bipolar vectors (OBV) as targets for multilayer perceptron (MLP) Artificial neural networks pertaining to the pattern recognition field. Implications involving this proposal are supported by a proper mathematical discussion. This study shows that the use of OBV as targets improves the class identification performance of MLP networks for pattern recognition with more than two classes when compared to the widely used conventional target bipolar vector (CBV). In addition to the overall performance improvement, it is possible to obtain high recognition rates with fewer training epochs, while keeping a feasible generalization with other data. This behavior is related to the Euclidean distance of the points in the output space, which is much higher in the case of OBVs. Therefore, with a greater distance between the points in the output space, the MLP network becomes less prone to associate trained outputs with incorrect targets. The mathematical discussion in this study is based on the backpropagation training algorithm. In addition to the mathematical discussion, results from experiments with real data are presented. These results follow those obtained by mathematical derivations. Real data sets used in the experiments are available from: (a) the Semeion Handwritten Digit of Machine Learning Repository, international repository; (b) Iris Image Database from the Chinese Academy of Sciences—CASIA; (c) Australian Sign Language, signs of Machine Learning Repository, international repository. Experiments were conducted to measure the mean Euclidean distance between the MLP network output subjected to a given input pattern and outputs related to other pattern classifications. Experimental results show that MLP networks trained with OBVs were able to ensure a Euclidean distance between the output and incorrect targets greater than MLP networks trained with CBVs (by up to 4 times for handwritten, by up to 12 times for iris images and by up to 10 times for Auslan signs language). As such, the adoption of OBVs as target vectors for MLPs reduces the computational effort for training the network.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A collaborative detection approach for internal steam leakage of tyre vulcanization workshop with artificial immune algorithm

In manufacturing industry, internal leakage of steam trap usually results in great steam waste. In particular, internal steam leakage in tyre vulcanization workshop has significant influences on its production safety and energy efficiency. In practice, internal steam leakage problem often is ignored and it tends to be difficult to detect this leakage due to lack of comprehensive flow meter or method. This paper presents a collaborative detection method for this problem in tyre vulcanization workshop with artificial immune algorithm. In the method, internal leakage of steam trap is defined as nonself antigens, and steam pressure differentials between steam pipe and steam rooms (or bladders) are extracted as epitopes of antigens. Furthermore, periodic energy efficiency and steam pressure of vulcanizer as the danger signals are simultaneously detected. Energy efficiency is represented by damaged cells which will be identified firstly for locating the leaking steam straps through antibodies. Furthermore, the self-adaptive danger thresholds for energy efficiency are evaluated through a steam consumption model and the Levenberg–Marquardt back propagation algorithm. An immune-based clustering algorithm aiNet is then adopted to generate antibodies (detectors) for detection on the steam pressure of vulcanizer. Finally, a case study is implemented to validate this method, which shows that collaborative detection method allows locating the specific leaking steam trap and is a feasible tool to reduce steam waste and ensure the safety of steam supply.

42 ENGINEERING↗
DOE OSTI2021

Hidden symmetries, spin and charge of artificial magnetic monopoles

Here, we discuss the non-Abelian artificial magnetic monopoles associated with n-level energy crossing in quantum systems. We found that hidden symmetries reveal themselves as observables such as spin, hypercharge, and other physical degrees of freedom. We illustrated our results on concrete examples of two and three energy-level crossings. Our findings can be helpful for modeling various phenomena in physical systems such as SU(N) topological insulators and spintronic devices based on topological insulators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
DOE OSTI2022

Artificial-intelligence-based prediction and control of combustion instabilities in spark-ignition engines

In recent years, as engine control strategies have grown increasingly sophisticated in a continued drive for increasing efficiency and reducing emissions, engine operation has been pushed into regimes that are limited by combustion instabilities. These instabilities produce undesirable abnormal combustion events, which pose barriers to further improvement in engine efficiency. Many of the phenomena involved are difficult to model or control using traditional, purely physics-based models and reactive control approaches. Artificial intelligence (AI) techniques offer some promise for achieving more effective combustion stability control, especially when appropriately applied within a physics-informed framework. This chapter illustrates the current state-of-the-art in applying AI to combustion stability control and examines three case studies with application to the dilute stability limit in spark-ignition engines to illustrate the utility and limitations of AI in these applications.

Maldonado Puente, Bryan↗
DOE OSTI2023

Assessing parallel path cooling tower performance via artificial neural networks

Real-time monitoring of a research nuclear reactor, a system in which all generated power is dissipated to the environment, can be performed via analysis of the heat rejection from the cooling system. Given an inlet water temperature and flow rate, the reactor power can be well-approximated from the outlet water temperature; however, the instrumentation to measure outlet conditions may not be robust or accurate. If we know how a cooling tower performs from historical data, but cannot measure the outlet temperature, a mathematical representation of the system can be inverted to obtain the outlet water temperature that describes the cooling capacity. Unfortunately, model inversion processes are computationally expensive. To address this, an artificial neural network (ANN) is implemented to assess the performance of a multi-cell cooling tower for a nuclear reactor. This approach leverages the Merkel model to obtain an extensive data set describing performance of the cooling tower cells throughout a wide array of potential operating conditions. The Merkel model is expressed as a function of four parameters: the inlet and outlet water temperatures, inlet air wet bulb temperature, and ratio of liquid-to-gas mass flow rates (L/G), which together provide a non-dimensional number indicative of cooling tower performance, called the Merkel integral. Computing a 4-dimensional data structure that describes finite combinations of the Merkel integral, an inverse model is then generated using an ANN to determine the cell outlet water temperature from the other three model parameters along with the computed Merkel integral. Compared to traditional model inversion methods, the ANN reduces the computational time by approximately 4 orders of magnitude, with effectively no sacrifice to solution accuracy, and could be applied for different cooling towers in the event the performance curve is known. Finally, three use cases of the ANN are then reviewed: (1) determining the cell outlet water temperatures when gas flow at rated conditions (GFRC) is known, (2) performing the prior case without knowledge of the GRFC, and (3) assessing performance differences between the individual tower cells.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗