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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.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multi-catalytic active site biochar-based catalysts for glucose isomerized to fructose: Experiments and density functional theory study

In this study, this work provides an innovative method for preparing different isomerization catalysts by impregnating different proportions of MgCl 2 and AlCl 3 and combining different K compounds on cellulose-derived biochar, followed by pyrolysis. Results show MgO and Al(OH) 3 existing in 4 Mg- 1 Al-C catalyst can obtain better catalytic effect on glucose isomerization than the singe of Al presenting in 0 Mg- 1 Al-C catalyst. Moreover, the synergism effects of the multi-catalytic active sites such as β-, γ -Al(OH) 3 , KCl, MgO, and K 4 H 2 (CO 3 ) 3 in Mg-Al-KHCO 3 -C catalyst can further lead to an increase in glucose isomerization, compared to the 4 Mg- 1 Al-C catalyst. The X-ray diffraction results present that the value of O/Al in Mg-Al-KHCO 3 -C catalyst is as high as 13.38, which provides many unsaturated acidic catalysis sites and benefits the glucose isomerization. Simultaneously, the TPD results reveal that the main active sites (MgO, Al(OH) 3 , and K 4 H 2 (CO 3 ) 3 ) in Mg-Al-KHCO 3 -C catalyst can provide weakly acidic and basic sites and avoid strongly acidic and basic sites to excessively attack the glucose. Based on the DFT analysis, the results indicate that the MgO has a great effect on the ring-opening reaction to form acyclic glucose, while Al(OH) 3+ has a great effect on promoting acyclic glucose hydrogen transfer isomerized to form fructose. Compared to other carbon-based metal catalysts, the prepared Mg-Al-KHCO 3 -C has excellent catalytic performance, which gives a higher fructose yield (38.7%) and selectivity (87.72%) and glucose conversion (44.12%) at 100 °C in 30 min. In this study, we develop a highly efficient Mg-Al-K-biochar catalyst for glucose isomerization and provide an efficient method for cellulose valorization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A machine learning approach for increased throughput of density functional theory substitutional alloy studies

In this study, a machine learning-based technique is developed to reduce the computational cost required to explore large design spaces of substitutional alloys. The first advancement is based on a neural network (NN) approach to predict the initial position of minority and majority ions prior to DFT relaxation. The second advancement is to allow the NN to predict the total energy for every possibility minority ion position and select the most stable configuration in the absence of relaxing each trial minority configuration. A bismuth oxide materials system, (Bi x La y Yb z ) 2 MoO 6 , was used as the model system to demonstrate the developed methods and quantify the resulting computational speedup. Compared to a brute force method that requires the calculation of every permutation of minority configuration and subsequent DFT relaxation, a 1.3× speedup was realized if the NN predicted the initial configuration of ions prior to relaxation. Implementation of the second advancement allowed the NN to predict the total energy for all possible trial configurations and downselect the most stable configurations prior to relaxation, resulting in a speedup of approximately 37×. Validation was done by comparing position and energy between the NN and DFT predictions. A maximum position vector mean squared error (MSE) of 1.6 × 10 -2 and a maximum energy MSE of 2.3 × 10 -7 was predicted for the worst case configuration. This method demonstrates a significant computational speedup, which has the potential for even greater computational savings for larger compositional design spaces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine-learning-aided density functional theory calculations of stacking fault energies in steel

A combined large-scale first principles approach with machine learning and materials informatics is proposed to quickly sweep the chemistry-composition space of advanced high strength steels (AHSS). AHSS are composed of iron and key alloying elements such as aluminum and manganese. A systematic exploration of the distribution of aluminum and manganese atoms in iron is used to investigate low stacking fault energies configurations using first principles calculations. To overcome the computational cost of exploring the composition space, this process is sped up using an automated machine learning tool: DeepHyper. Here our results predict that it is energetically favorable for Al to stay away from a stacking fault, but Mn atoms do not affect the stacking fault energy and can stay in the vicinity of the fault. The distribution of Al and Mn atoms in systems containing stacking faults and the effects of their interactions on the equilibrium distribution are systematically analyzed.

36 MATERIALS SCIENCE↗