INTELLIGENT ROUTE TO DESIGN EFCIENT CO2 REDUCTION ELECTROCATALYSTS USING ANFIS OPTIMIZED BYGA AND PSO

Recently, electrochemical reduction of CO2 into value-added fuels has been noticed as a promising process to decrease CO2 emissions. The development of such technology is strongly depended upon tuning the surface properties of the applied electrocatalysts. Considering the high cost and time-consuming experimental investigations, computational methods, particularly machine learning algorithms, can be the appropriate approach for efficiently screening the metal alloys as the electrocatalysts. In doing so, to represent the surface properties of the electrocatalysts numerically, d-band theory-based electronic features and intrinsic properties obtained from density functional theory (DFT) calculations were used as descriptors. Accordingly, a dataset containg 258 data points was extracted from the DFT method to use in machine learning method. The primary purpose of this study is to establish a new model through machine learning methods; namely, adaptive neuro-fuzzy inference system (ANFIS) combined with particle swarm optimization (PSO) and genetic algorithm (GA) for the prediction of *CO (the key intermediate) adsorption energy as the efficiency metric. The developed ANFIS–PSO and ANFIS–GA showed excellent performance with RMSE of 0.0411 and 0.0383, respectively, the minimum errors reported so far in this field. Additionally, the sensitivity analysis showed that the center and the filling of the d-band are the most determining parameters for the electrocatalyst surface reactivity. The present study conveniently indicates the potential and value of machine learning in directing the experimental efforts in alloy system electrocatalysts for CO2 reduction

HEAT TRANSFER THROUGH HYDROGENATED GRAPHENE SUPERLATTICE NANORIBBONS: A COMPUTATIONAL STUDY

Optimization of thermal conductivity of nanomaterials enables the fabrication of tailor-made nanodevices for thermoelectric applications. Superlattice nanostructures are correspondingly introduced to minimize the thermal conductivity of nanomaterials. Herein we computationally estimate the effect of total length and superlattice period ( lp ) on the thermal conductivity of graphene/ graphane superlattice nanoribbons using molecular dynamics simulation. The intrinsic thermal conductivity ( ) is demonstrated to be dependent on lp . The of the superlattice, nanoribbons decreased by approximately 96% and 88% compared to that of pristine graphene and graphane, respectively. By modifying the overall length of the developed structure, we identified the ballisticdiffusive transition regime at 120 nm. Further study of the superlattice periods yielded a minimal thermal conductivity value of 144 W m− 1 k− 1 at lp = 3.4 nm. This superlattice characteristic is connected to the phonon coherent length, specifically, the length of the turning point at which the wave-like behavior of phonons starts to dominate the particle-like behavior. Our results highlight a roadmap for thermal conductivity value control via appropriate adjustments of the superlattice period

Thermal rectification in polytelescopic Ge nanowires

Herein we served non-equilibrium molecular dynamics (NEMD) approach to simulate thermal rectification in the mono- and polytelescopic Ge nanowires (GeNWs). We considered mono-telescopic structures with different Fat-Thin configurations (15-10 nm-nm or Type (I); 15-5 nm-nm or Type (II); and 10–5 or Type (III) nm-nm) as generic models. We simulated the variation of thermal conductivity against interfacial cross-sectional temperature as well as the direction of heat transfer, where a higher thermal conductivity correlating to thicker nanowires, and a more significant drop (or discontinuity) in the average interface temperature in the positive (or negative) direction were detected. Noticeably, interfacial thermal resistance followed the order of Type (II) (48 K/μW, maximal) ˃ Type (III) ˃ Type (I) (5 K/μW, minimal). In the second stage, a series of polytelescopic nanostructures of GeNWs were born with consecutive cross-sectional interfaces. Surprisingly, larger interfacial cross-sectional areas equivalent to smaller diameter changes along the GeNWs were responsible for higher temperature rectification. This led to a very limited thermal conductivity loss or a very high unidirectional heat transfer along the polytelescopic structures - the key for manufacturing next generation high-performance thermal diodes.24 month embargo; available online: 13 June 2022This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

AN INSIGHT INTO THERMAL PROPERTIES OF BC3-GRAPHENE HETERO-NANOSHEETS: A MOLECULAR DYNAMICS STUDY

Simulation of thermal properties of graphene hetero-nanosheets is a key step in understanding their performance in nano-electronics where thermal loads and shocks are highly likely. Herein we combine graphene and boron-carbide nanosheets (BC3N) heterogeneous structures to obtain BC3N-graphene hetero-nanosheet (BC3GrHs) as a model semiconductor with tunable properties. Poor thermal properties of such heterostructures would curb their long-term practice. BC3GrHs may be imperfect with grain boundaries comprising non-hexagonal rings, heptagons, and pentagons as topological defects. Therefore, a realistic picture of the thermal properties of BC3GrHs necessitates consideration of grain boundaries of heptagon-pentagon defect pairs. Herein thermal properties of BC3GrHs with various defects were evaluated applying molecular dynamic (MD) simulation. First, temperature profles along BC3GrHs interface with symmetric and asymmetric pentagon-heptagon pairs at 300 K, ΔT= 40 K, and zero strain were compared. Next, the efect of temperature, strain, and temperature gradient (ΔT) on Kaptiza resistance (interfacial thermal resistance at the grain boundary) was visualized. It was found that Kapitza resistance increases upon an increase of defect density in the grain boundary. Besides, among symmetric grain boundaries, 5–7–6–6 and 5–7–5–7 defect pairs showed the lowest (2 × ­10–10 m2 K ­W−1) and highest (4.9× ­10–10 m2 K ­W−1) values of Kapitza resistance, respectively. Regarding parameters afecting Kapitza resistance, increased temperature and strain caused the rise and drop in Kaptiza thermal resistance, respectively. However, lengthier nanosheets had lower Kapitza thermal resistance. Moreover, changes in temperature gradient had a negligible efect on the Kapitza resistanc

Thermal conductivity of random polycrystalline BC3 nanosheets: A step towards realistic simulation of 2D structures

Boron carbide nanosheets (BC3NSs) are semiconductors possessing non-zero bandgap. Nevertheless, there is no estimation of their thermal conductivity for practical circumstances, mainly because of difficulties in simulation of random polycrystalline structures. In the real physics world, BC3NS with perfect monocrystalline is rare, for the nature produces structures with disordered grain regions. Therefore, it is of crucial importance to capture a more realistic picture of thermal conductivity of these nanosheets. Polycrystalline BC3NS (PCBC3NSs are herein simulated by Molecular Dynamics simulation to take their thermal conductivity fingerprint applying ΔT of 40 K. A series of PCBC3NSs were evaluated for thermal conductivity varying the number of grains (3, 5, and 10). The effect of grain rotation was also modeled in terms of Kapitza thermal resistance per grain, varying the rotation angle (θ/2 = 14.5, 16, 19, and 25°). Overall, a non-linear temperature variation was observed for PCBC3NS, particularly by increasing grain number, possibly because of more phonon scattering (shorter phonon relaxation time) arising from more structural defects. By contrast, the heat current passing across the slab decreased. The thermal conductivity of nanosheet dwindled from 149 W m−1 K−1 for monocrystalline BC3NS to the values of 129.67, 121.32, 115.04, and 102.78 W m−1 K−1 for PCBC3NSs having 2, 3, 5, and 10 grains, respectively. The increase of the grain̛s rotation angle (randomness) from 14.5° to 16°, 19° and 25° led to a rise in Kapitza thermal resistance from 2⨯10−10 m2 K·W−1 to the values of 2.3⨯ 10−10, 2.9⨯10−10, and 4.7⨯ 10−10 m2 K·W−1, respectively. Thus, natural 2D structure would facilitate phonon scattering rate at the grain boundaries, which limits heat transfer across polycrystalline nanosheets. © 2021 Elsevier Inc.24 month embargo; available online 25 June 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Applying molecular dynamics simulation to take the fracture fingerprint of polycrystalline SiC nanosheets

Graphene-like nanosheets are the key elements of advanced materials and systems. The mechanical behavior of the structurally perfect 2D nanostructures is well documented, but that of polycrystalline ones is less understood. Herein, we applied molecular dynamics simulation (MDS) to take the fracture fingerprint of polycrystalline SiC nanosheets (PSiCNS), where monocrystalline SiC nanosheets (MSiCNS) was the reference nanosheet. The mechanical responses of defect-free and defective MSiCNS and PSiCNS having regular cracks and circular-shaped notches were captured as a function of temperature (100–1200 K), such that elevated temperatures were unconditionally deteriorative to the properties. Moreover, larger cracks and notches more severely decreased the strength of PSiCNS, e.g. Young's modulus dropped to ca. 41% by the crack enlargement. The temperature rise similarly deteriorated the failure stress and Young's modulus of PSiCNS. However, the stress intensity factor increased by the enlargement of the crack length but decreased against temperature. We believe that the findings of the present study can shed some light on designing mechanically stable nanostructures for on-demand working conditions. © 2021 Elsevier B.V.24 month embargo; available online 19 August 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

THEORETICAL ENCAPSULATION OF FLUOROURACIL (5-FU) ANTI-CANCER CHEMOTHERAPY DRUG INTO CARBON NANOTUBES (CNT) AND BORON NITRIDE NANOTUBES (BNNT)

Chemotherapy with anti-cancer drugs is considered the most common approach for killing cancer cells in the human body. However, some barriers such as toxicity and side effects would limit its usage. In this regard, nano-based drug delivery systems have emerged as cost-effective and efficient for sustained and targeted drug delivery. Nanotubes such as carbon nanotubes (CNT) and boron nitride nanotubes (BNNT) are promising nanocarriers that provide the cargo with a large inner volume for encapsulation. However, understanding the insertion process of the anti-cancer drugs into the nanotubes and demonstrating drug-nanotube interactions starts with theoretical analysis. Methods: First, interactions parameters of the atoms of 5-FU were quantified from the DREIDING force field. Second, the storage capacity of BNNT (8,8) was simulated to count the number of drugs 5-FU encapsulated inside the cavity of the nanotubes. In terms of the encapsulation process of the one drug 5-FU into nanotubes, it was clarified that the drug 5-FU was more rapidly adsorbed into the cavity of the BNNT compared with the CNT due to the higher van der Waals (vdW) interaction energy between the drug and the BNNT. Results: The obtained values of free energy confirmed that the encapsulation process of the drug inside the CNT and BNNT occurred spontaneously with the free energies of −14 and −25 kcal·mol−1 , respectively. Discussion: However, the lower value of the free energy in the system containing the BNNT unraveled more stability of the encapsulated drug inside the cavity of the BNNT comparing the system having CNT. The encapsulation of Fluorouracil (5-FU) anti-cancer chemotherapy drug (commercial name: Adrucil®) into CNT (8,8) and BNNT (8,8) with the length of 20 Å in an aqueous solution was discussed herein applying molecular dynamics (MD) simulatio