Effect of Al doping on the structural and optical properties of CuO nanoparticles prepared by solution combustion method: Experiment and DFT investigation

Islam, Muhammad R.; Obaid, Jaed Ebna; Saiduzzaman, Md; Nishat, Sadiq Shahriyar; Debnath, Tapas; Kabir, Alamgir

doi:10.1016/j.jpcs.2020.109646

Cited by 50 publications

(14 citation statements)

References 63 publications

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“…Functions Connecting Lattice Parameters with the Energy Gap. The empirical results of the developed stepwise regression (SWR), extreme learning machine predictive model with sine activation function (ELM-IP-Sin), and extreme learning machine predictive model with sigmoid activation function (ELM-IP-Sig) are, respectively, presented in Equations (11), (12), and (13). The developed SWR model linearly combines the distortion along a-axis, b-axis, and c-axis due to the incorporated dopants while ELM-based models incorporated nonlinearity existing between lattice parameters and the energy gaps.…”

Section: Intelligent and Stepwise Regression-based Empiricalmentioning

confidence: 99%

“…CuO semiconductor photo-induced behavior can be easily modified by oxygen vacancies which serve as photo-excited trap sites for charge recombination suppression [12]. Foreign material incorporation as well as varying experimental conditions potentially changes the bond length (Cu-O) and bond angles (Cu-O-Cu) of CuO semiconductor which in turn alters the lattice parameters [13]. The number of oxygen vacancies present in pure and un-doped CuO lattice is altered when Cu 2+ in the CuO semiconductor is replaced by dopant atoms.…”

Section: Introductionmentioning

confidence: 99%

“…This oxygen vacancy variation influences the number of Cu-O bond as well as Cu-O-Cu bond angle which subsequently affects the O-Cu-O bond angle of the material. The substitutional sites continue to accommodate the dopant atoms until thermodynamic limit is attained after which interstitial sites are occupied [13]. When dopant atoms are introduced into the CuO semiconductor, free electrons are released depending on the nature of the dopants.…”

Section: Introductionmentioning

confidence: 99%

“…When dopant atoms are introduced into the CuO semiconductor, free electrons are released depending on the nature of the dopants. Electron exchange interaction occurs due to the generated free electrons which leads to electron exchange potential that shrink the semiconductor energy gap [13]. Similarly, effective oxidation states differences due to substituting Cu 2+ ions with ions of higher oxidation states which leads to electron donation which induces Cu sorbital hybridization that causes energy gap alteration.…”

Section: Introductionmentioning

confidence: 99%

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Engineering the Energy Gap of Cupric Oxide Nanomaterial Using Extreme Learning Machine and Stepwise Regression Algorithms

Alqahtani

2021

Journal of Nanomaterials

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CuO is a narrow band gap semiconductor with distinct features that render it indispensable in many industrial and technological applications such as environmental friendly catalysts for organic pollutant removal, sensors, photovoltaic, solar cells, batteries, and storage media among others. Engineering of its energy gap becomes imperative and necessary for tailoring its light absorption capacity to a desired level required for a particular application. Elemental doping mechanisms with accompanied lattice distortion symmetry breaking effectively enhance the optical property of this semiconductor and serve as a major route through which material design is achieved. This work develops an extreme learning machine intelligent predictive (ELM-IP) model and stepwise regression (SWR) based model for estimating energy gap of a doped CuO semiconductor. The developed ELM-IP-Sin model which employs sine activation function performs better than the ELM-IP-Sig model (that utilizes sigmoid activation function) and SWR model with a percentage improvement of 14.15% and 50.05%, respectively, using root mean square error (RMSE) metric, while the developed ELM-IP-Sig model outperforms the SWR-based model. The developed models further investigate the dependence of CuO energy gap on iron and cobalt impurity incorporation, and the obtained results agree well with measured values. The outstanding performance of the developed models is highly meritorious in tailoring the light response capacity of CuO semiconductor for photocatalytic and optoelectronics applications at a reduced cost while the experimental stress is circumvented.

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Section: Intelligent and Stepwise Regression-based Empiricalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Engineering the Energy Gap of Cupric Oxide Nanomaterial Using Extreme Learning Machine and Stepwise Regression Algorithms

Alqahtani

2021

Journal of Nanomaterials

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“…The aluminothermic reactions of Al/CuO and Al/MnO 2 have also been employed to fully use the Al-based materials in propellants [ 9 , 10 , 11 , 12 ]. The results showed that thermites can reduce agglomeration and decrease diffusion distance more than common mechanically mixed materials.…”

Section: Introductionmentioning

confidence: 99%

Influences of Multilayer Graphene and Boron Decoration on the Structure and Combustion Heat of Al3Mg2 Alloy

Dong

Qiu²,

et al. 2020

Nanomaterials

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To improve the engine-driven performance of propellants, high-energy alloys such as Al and Mg are usually adopted as annexing agents. However, there is still room for improvement in the potential full utilization of alloy energy. In this study, we investigated how to improve combustion efficiency by decorating Al3Mg2 alloy with multilayer graphene and amorphous boron. Scanning electron microscopy and Raman tests showed that decorating with multilayer graphene and amorphous boron promoted the dispersion of Al3Mg2 alloy. The results showed that decorating with 1% boron and 2% multilayer graphene improved the combustion heat of Al3Mg2 alloy to 32.8 and 30.5 MJ/kg, respectively. The coexistence of two phases improved the combustion efficiency of the matrix Al3Mg2 alloy.

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Reconfigurable Resistive Switching Memory for Telegraph Code Sensing and Recognizing Reservoir Computing Systems

Kim,

Truong,

Lee

et al. 2024

Small

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Reservoir computing (RC) system is based upon the reservoir layer, which non‐linearly transforms input signals into high‐dimensional states, facilitating simple training in the readout layer—a linear neural network. These layers require different types of devices—the former demonstrated as diffusive memristors and the latter prepared as drift memristors. The integration of these components can increase the structural complexity of RC system. Here, a reconfigurable resistive switching memory (RSM) capable of implementing both diffusive and drift dynamics is demonstrated. This reconfigurability is achieved by preparing a medium with a 3D ion transport channel (ITC), enabling precise control of the metal filament that determines memristor operation. The 3D ITC‐RSM operates in a volatile threshold switching (TS) mode under a weak electric field and exhibits short‐term dynamics that are confirmed to be applicable as reservoir elements in RC systems. Meanwhile, the 3D ITC‐RSM operates in a non‐volatile bipolar switching (BS) mode under a strong electric field, and the conductance modulation metrics forming the basis of synaptic weight update are validated, which can be utilized as readout elements in the readout layer. Finally, an RC system is designed for the application of reconfigurable 3D ITC‐RSM, and performs real‐time recognition on Morse code datasets.

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Effect of Al doping on the structural and optical properties of CuO nanoparticles prepared by solution combustion method: Experiment and DFT investigation

Cited by 50 publications

References 63 publications

Engineering the Energy Gap of Cupric Oxide Nanomaterial Using Extreme Learning Machine and Stepwise Regression Algorithms

Engineering the Energy Gap of Cupric Oxide Nanomaterial Using Extreme Learning Machine and Stepwise Regression Algorithms

Influences of Multilayer Graphene and Boron Decoration on the Structure and Combustion Heat of Al3Mg2 Alloy

Reconfigurable Resistive Switching Memory for Telegraph Code Sensing and Recognizing Reservoir Computing Systems

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