Band Model Research Articles

Exploring ZnO nanoparticles: UV–visible analysis and different size estimation methods

The study investigates several theoretical and empirical methods for determining the size of zinc oxide nanoparticles (ZnO NPs) and studies their characteristics by using solely UV–visible spectroscopy. The investigation begins with the green synthesis of three samples of ZnO NPs using three different reducing agents Aspergillus Niger fungal biomass (AN), Aloe barbadensis Mille (ABM) and Ocimum tenuiflorum (OT) leaves, respectively with Zinc Sulphate as a precursor involving precipitation method. The study analyses the UV–visible data of each sample to obtain optical bandgap, Urbach energy, refractive index, optical conductivity. and apply several methods or Models such as Effective Mass Approximation (EMA) or Brus Equation (BE), Hyperbolic Band Model (HBM), Meulenkamp's Expression (ME), Tight Binding Model (TBM) and a simplified version of EMA (SEMA) to estimate the size of ZnO NPs. The effectiveness of size-determining models was assessed using two sets of one-way analysis of variance (ANOVA) for each ZnO sample and models Followed by Tukey post-hoc analysis to identify the exact sample that is different from other samples.

Thermoelectric optimization using first principles calculation and single parabolic band model: a case of Ca0.5La0.5−x Bi x MnO3 (x = 0, 0.25)

Conducting optimization calculations for thermoelectric performance can be beneficial in guiding the direction of further experimental work. In our study, we utilize a combination of the first principle, Boltzmann transport and restructured single parabolic band model to investigate the half-doped semiconductors based on manganite. Ca0.5La0.5−x Bi x MnO3 (x = 0, 0.25) as samples shows the power factor (PF) optimum value of 30% and 69% for x = 0 and 0.25, respectively at a temperature of 800 K. In addition, both samples show two to three orders of magnitude smaller lattice thermal conductivity than their electronic thermal conductivity. This excludes complex phononic transport mechanisms from the calculation of the figure of merit (ZT). The ZT calculations of Ca0.5L0.5MnO3 and Ca0.5L0.5−x Bi x MnO3 are corrected by the ratio of the transport relaxation time of electrical conductivity to the transport relaxation time of electronic thermal conductivity by the Lorenz number, resulting in ZT values of 0.063 and 0.327 at a temperature of 800 K, respectively.

Research on the Optimization of Multi-Class Land Cover Classification Using Deep Learning with Multispectral Images

With the advancement of artificial intelligence, deep learning has become instrumental in land cover classification. While there has been a notable emphasis on refining model structures to improve classification accuracy, it is imperative to also emphasize the pivotal role of data-driven optimization techniques. This paper presents an in-depth investigation into optimizing multi-class land cover classification using high-resolution multispectral images from Worldview3. We explore various optimization strategies, including refined sampling strategies, data band combinations, loss functions, and model enhancements. Our optimizations led to a substantial increase in the Mean Intersection over Union (mIoU) classification accuracy, improving from a baseline of 0.520 to a final accuracy of 0.709, which represents a 35.2% enhancement. Specifically, by optimizing the classic semantic segmentation network in four key aspects, we improved the mIoU by 15.5%. Further improvements through changes in data combinations, sampling methods, and loss functions led to an overall 17.2% increase in mIoU. The proposed model optimization methods enabled the OUNet to outperform the baseline model by providing more precise edge detection and feature representation, while reducing the model parameters scale. Experimental evidence shows that in the application of multi-class land surface classification, increasing the quantity and diversity of samples, avoiding data imbalance issues, is equally valuable for improving overall classification accuracy as it is for enhancing model performance.

Exploring Thermoelectric Transport Properties and Band Parameters of n-Type Bi2-xSbxTe3 Compounds Using the Single Parabolic Band Model

The n-type Bi2-xSbxTe3 compounds have been of great interest due to its potential to achieve a high thermoelectric performance, comparable to that of p-type Bi2-xSbxTe3. However, a comprehensive understanding on the thermoelectric properties remains lacking. Here, we investigate the thermoelectric transport properties and band characteristics of n-type Bi2-xSbxTe3 (x = 0.1 – 1.1) based on experimental and theoretical considerations. We find that the higher power factor at lower Sb content results from the optimized balance between the density of state effective mass and nondegenerate mobility. Additionally, a higher carrier concentration at lower x suppresses bipolar conduction, thereby reducing thermal conductivity at elevated temperatures. Consequently, the highest zT of ~ 0.5 is observed at 450 K for x = 0.1 and, according to the single parabolic band model, it could be further improved by ~70 % through carrier concentration tuning.

Evidence of topological surface states with upward band bending in (Bi[formula omitted]Sb[formula omitted])[formula omitted]Te[formula omitted] topological insulator

Significant efforts have been put in the past to minimize the bulk conduction to probe the exotic surface states of topological insulators (TIs), nevertheless, the surface transport is usually hindered by the presence of bulk conduction. The identification and separation of the contribution of topological surface states (TSS) by electrical transport is important for understanding the unique properties of TIs and developing new electronic devices based on these materials. In this report we have studied Shubnikov-de Haas (SdH) oscillations, magneto-resistance (MR) and hall resistivity under high magnetic field in (Bi0.45Sb0.60)2Te3 single crystal grown by modified Bridgman method. Our results show the existence of SdH oscillations which arise from TSS. The obtained Fermi wave vector and Fermi velocity of surface charge carriers show a good agreement with those found by angle-resolved photoemission spectroscopy experiments. Two band model fit to hall resistivity confirms the high mobility of surface bands and low mobility of bulk bands. Band bending effect is investigated which exhibits a depletion of carriers from near by bulk towards the surface states resulting in an upward band bending. A large non-saturating MR (∼347)% is observed as a result of multichannel quantum coherent transport mechanism.

Practical approaches for evaluating radiative heat from high pressure hydrogen flame

As the construction of hydrogen stations and other hydrogen-related facilities progresses, it becomes increasingly crucial for safety designs to account for scenarios where leaked hydrogen might ignite. Since a hydrogen flame is usually invisible, detection must rely on radiative heat or temperature. While it is possible to predict accurate radiative heat flux from a high pressure hydrogen jet flame through detailed numerical calculations of hydrogen combustion with its radiation, the objective of this study is to provide a simpler and more accurate prediction method. Previous experimental data of radiative heat flux, Qr from high pressure hydrogen jet flames were compiled, and a simple experimental equation Qr=1463.3(Lo/Lf)−2.32 was derived, where Lf and Lo represent the flame length and the distance perpendicular to the flame axis from the midpoint of the flame length to the observer, respectively. Radiative heat fluxes were computed through detailed numerical simulations using three types of water vapor radiation models: Goody's narrow random band model, the exponential broad band model by Edward and Elsasser's narrow regular band model. The temperature and water vapor partial pressure distributions obtained from numerical simulations were used as input conditions. The results were found to be in good agreement with the experimental equation. Moreover, a simple diagram representing the radiative heat flux from a hydrogen flame was derived using the method of cylinder-like approximation. The results of this study show that if the hydrogen pressure and leakage aperture diameter are given, the radiative heat flux at a certain distance can be predicted with high accuracy. Additionally, the average value of emissivity for the hydrogen diffusion flame was found to be on the order of 0.03.

Topological phase transitions with zero indirect band gaps

Topological phase transitions in band models are usually associated to the gap closing between the highest valance band and the lowest conduction band, which can give rise to different types of nodal structures, such as Dirac/Weyl points, lines and surfaces. In this work, we show the existence of a different kind of topological phase transitions in one-dimensional systems, which are instead characterized by the presence of a robust zero indirect gap, which occurs when the top of the valence band coincides with the bottom of the conduction band in energy but not in momentum. More specifically, we consider an one-dimensional model on a trimer chain that is protected by both particle–hole and chiral-inversion symmetries. At the critical point, the system supports a Dirac-like point. After introducing a deforming parameter that breaks both inversion and chiral symmetries but preserves their combination, we observe the emergence of a zero indirect band gap, which results to be related to the persymmetry of our Hamiltonian. Importantly, the zero indirect gap holds for a range of values of the deforming parameter. We finally discuss the implementation of the deforming parameter in our tight-binding model through time-periodic (Floquet) driving.

Latitude-dependent Atmospheric Waves and Long-period Modulations in Luhman 16 B from the Longest Light Curve of an Extrasolar World

In this work, we present the longest photometric monitoring of up to 1200 hr of the strongly variable brown dwarf binaries Luhman 16 AB and provide evidence of ±5% variability on a timescale of several to hundreds of hours for this object. We show that short-period rotational modulation around 5 hr (k = 1 wavenumber) and 2.5 hr (k = 2 wavenumber) dominate the variability under 10 hr, where the planetary-scale wave model composed of k = 1 and k = 2 waves provides good fits to both the periodograms and light curve. In particular, models consisting of three to four sine waves could explain the variability of the light-curve durations up to 100 hr. We show that the relative range of the k = 2 periods is narrower compared to the k = 1 periods. Using simple models of zonal banding in solar system giants, we suggest that the difference in period range arises from the difference in wind-speed distribution at low and mid-to-high latitudes in the atmosphere. Last, we show that Luhman 16 AB also exhibits long-period ±5% variability, with periods ranging from 15 hr up to 100 hr over the longest monitoring of this object. Our results for the k = 1 and k = 2 waves and long-period evolution are consistent with previous 3D atmosphere simulations, demonstrating that both latitude-dependent waves and slow-varying atmospheric features are potentially present in Luhman 16 AB atmospheres and are a significant contribution to the light-curve modulation over hundreds of rotations.

Quasilinear Kane conduction band model in nitrogen-doped indium tin oxide

Quasilinear Kane conduction band model in nitrogen-doped indium tin oxide

A Thermodynamically Based Modified Cam‐Clay Model for Post‐Bifurcation Behavior of Deformation Bands

AbstractCompaction bands are a type of localized deformation that can occur as diffuse or discrete bands in porous rocks. While modeling of shear bands can replicate discrete and diffusive bands, numerical models of compaction have so far only been able to describe the formation of discrete compaction bands. In this study, we present a new thermodynamic approach to model compaction bands that is able to capture both discrete and diffuse compaction band growth. The approach is based on a reaction‐diffusion formalism that includes an additional entropy flux. This entropic velocity regularizes the solution, by introducing a characteristic diffusion length scale and controlling the mode change from discrete to diffusive post‐localisation growth. The approach is used to model compaction band growth in highly porous carbonates. The model can replicate the areas of material damage exhibiting reduced porosity which are often observed as nuclei for the growth of compaction bands in experiments. The model also has the versatility to predict the formation of diffuse compaction bands, which is a significant advance in the field of compaction band modeling. The method can potentially be used for investigating the effect of material heterogeneities on compaction band growth and is heuristic for developing new methodologies for forecasting compaction band formation.

Long-term bio-functional performance of a novel, self-positioning balloon expandable transcatheter biological aortic valve system in the ovine aortic banding model.

The aim of the study was to evaluate bio-functionality of a novel, proprietary balloon-expandable biological transcatheter aortic valve implantation (TAVI) system (InFlow, CardValve Consortium, Poland) in an ovine model of aortic banding. Surgical ascending aorta banding was created in 21 sheep. Two weeks later, 18 biological valves were implanted within the model using 15-16 F InFlow TAVI systems and carotid cut-down approach. Follow-up transthoracic echocardiography was performed at 30, 90, and 180-day. At designated time, animals were euthanized and valves harvested for analysis. All sheep survived the banding procedure. There were 4 (22%) procedure related deaths within a 7-day period. During the observation an additional 2 sheep died. In one, the valve dislocated after the procedure - the animal was excluded. Two animals completed 30-day follow up, five 90-day follow-up and four terminal follow-up of 180 days. Valves examined via transesophageal echocardiography showed proper hemodynamic parameters without evidence of structural valve deterioration. The maximum and average flow gradients at 180 days were 31.4 (23.3-37.7) and 17.5 (13.1-20.2) mmHg, respectively. There was one case of moderate insufficiency and no case of perivalvular leaks. By histopathology, there were no inflammation, thrombosis, nor calcifications in any tested valves at long-term follow-up. Neointimal coverage of stent struts increased with time from basal part in "early" groups to nearly 3/4 of stent length in the 180-day group. The pannus tissue showed maturation that increased with time with no stenotic "collar" visible in orthotopically implanted valves. The study showed good hemodynamic performance, durability and biocompatibility of the novel biological THV.

Theoretical Maximum Thermoelectric Performance of p‐Type Hf‐ and Zr‐Doped NbFeSb Half‐Heusler Compounds

AbstractHalf‐Heusler compounds are promising materials for thermoelectric applications due to their high zT at elevated temperatures. However, their intrinsic high thermal conductivity limits their efficiency. Doping with Hf or Zr can improve the zT of these materials. Recently, a high zT of 1.5 at 1200 K achieved in p‐type Nb1‐xHfxFeSb has attracted much attention. While the effect of doping Hf in thermal conductivity is studied thoroughly, the effect of Hf doping on band parameters is not fully evaluated. This study investigates the effect of Hf and Zr doping on the electronic band parameters and thermoelectric properties of NbFeSb using the Single Parabolic Band model. The results show that Hf doping increases the weighted mobility of the samples, while Zr doping has no significant effect. Hf doping with x = 0.14 is predicted to improve the zT of NbFeSb by 35% at 300 K (0.19 → 0.26). These results show the intricate effects of Hf and Zr doping on the electronic and thermal properties of NbFeSb.

Construction of cotton leaf nitrogen content estimation model based on the PROSPECT model

Leaf nitrogen content (LNC) is an important index to measure the nitrogen deficiency in cotton. The rapid and accurate monitoring of LNC is of great significance for understanding the growth status of cotton and guiding precise fertilization in the field. At present, the hyperspectral technology monitoring of LNC is very mature, but it is interfered with by external factors such as shadow and soil, and data acquisition is still dependent on manpower. Therefore, on the basis of clarifying the correlation and quantitative relationship between physiological parameters and cotton LNC, the 400-2500 nm spectral curve was simulated based on PROSPECT-5 model. Combined with the measured spectra, the sensitive bands of leaf nitrogen content were screened, and four machine learning algorithms based on the reflectance of the sensitive bands were compared to construct a model for the estimation of LNC in cotton and determine the optimal model. The results show the following: (1) The parameter with the best correlation with nitrogen content was Cab, and the linear relationship was y=0.3942x+12.521, R2=0.81, RMSE=12.87 g/kg. (2) The shuffled frog leaping algorithm (SFLA) and the successive projections algorithm (SPA) were used to screen the relevant bands sensitive to LNC. SFLA selected nine characteristic bands, mainly distributed between 700 and 750 nm. SPA screened seven characteristic bands, mainly distributed between 670 and 760 nm. The characteristic bands of both screening methods were distributed near the red edge. (3) Based on the sensitive bands, the four machine learning algorithms were compared. Among them, the band modeling of SFLA screening under the random forest (RF) algorithm was the best (modeling set R2=0.973, RMSE=1.001 g/kg, rRMSE=3.41%, validation set R2=0.803, RMSE=3.191 g/kg, rRMSE=10.85%). In summary, this study proposes an optimal estimation model of cotton leaf nitrogen content based on the radiative transfer model, which provides a theoretical basis for the dynamic, accurate, and non-destructive monitoring of cotton leaf nitrogen content.

Establishment and comparison of in situ detection models for foodborne pathogen contamination on mutton based on SWIR-HSI.

Rapid and accurate detection of food-borne pathogens on mutton is of great significance to ensure the safety of mutton and its products and the health of consumers. The feasibility of short-wave infrared hyperspectral imaging (SWIR-HSI) in detecting the contamination status and species of Escherichia coli (EC), Staphylococcus aureus (SA) and Salmonella typhimurium (ST) contaminated on mutton was explored. The hyperspectral images of uncontaminated and contaminated mutton samples with different concentrations (108, 107, 106, 105, 104, 103 and 102 CFU/mL) of EC, SA and ST were acquired. The one dimensional convolutional neural network (1D-CNN) model was constructed and the influence of structure hyperparameters on the model was explored. The effects of different spectral preprocessing methods on partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM) and 1D-CNN models were discussed. In addition, the feasibility of using the characteristic wavelength to establish simplified models was explored. The best full band model was the 1D-CNN model with the convolution kernels number of (64, 16) and the activation function of tanh established by the original spectra, and its accuracy of training set, test set and external validation set were 100.00, 92.86 and 97.62%, respectively. The optimal simplified model was genetic algorithm optimization support vector machine (GA-SVM). For discriminating the pathogen species, the accuracies of SVM models established by full band spectra preprocessed by 2D and all 1D-CNN models with the convolution kernel number of (32, 16) and the activation function of tanh were 100.00%. In addition, the accuracies of all simplified models were 100.00% except for the 1D-CNN models. Considering the complexity of features and model calculation, the 1D-CNN models established by original spectra were the optimal models for pathogenic bacteria contamination status and species. The simplified models provide basis for developing multispectral detection instruments. The results proved that SWIR-HSI combined with machine learning and deep learning could accurately detect the foodborne pathogen contamination on mutton, and the performance of deep learning models were better than that of machine learning. This study can promote the application of HSI technology in the detection of foodborne pathogens on meat.

Dynamic Receptance Analysis Combined with Hybrid FE-SEA Method to Predict Structural Noise from Long-Span Cable-Stayed Bridge in Urban Rail Transit

In this study, dynamic receptance analysis (DRA) is proposed and combined with hybrid finite element (FE)-statistical energy analysis (SEA) method to accurately predict structural noise from long-span cable-stayed bridge (LSCB) with steel box composite girder (SBCG) in urban rail transit (URT). To begin with, a vertical vehicle–track coupling model in frequency domain is established based on DRA, in which the rail is represented by an infinite Timoshenko beam supported by a series of fasteners that are regarded as springs with complex stiffness. The floating slab is regarded as the Euler beam with both ends free supported by steel springs. Using this model, the spectrums of the wheel–rail force and the forces transferred to the bridge can be efficiently obtained by taking rail roughness as the excitation. Due to the low modal density of the concrete deck, the hybrid FE-SEA method is introduced to establish the noise prediction model, in which the discontinuity caused by using distinct models for different frequency bands is avoided. Then the on-site noise tests of a LSCB with SBCG in URT are carried out to verify of the proposed method. Finally, based on the prediction method, the acoustic contributions of the bridge components are analyzed in detail. The force transfer characteristics as well as the noise reduction effects of different track structures are thoroughly investigated, so as to provide reference for the future research on bridge-borne noise control.

δ-Bonding and Spin-Orbit Coupling Make SrAg4Sb2 a Topological Insulator.

Bonding interactions and spin-orbit coupling in the topological insulator SrAg4Sb2 are investigated using DFT with orbital projection analysis. Ag-Ag delta bonding is a key ingredient in the topological insulating state because the delta antibonding band forms a band inversion with the 5 s sigma bonding band. Spin-orbit coupling is required to lift d orbital degeneracies and lower the antibonding band enough to create the band inversion. These bonding effects are enabled by a longer-than-covalent Ag-Ag distance in the crystal lattice, which might be a structural characteristic of other transition metal based topological insulators. A simplified model of the topological bands is constructed to capture the essence of the topological insulating state in a way that may be engineered in other materials.

Sensing behavior of pentagonal two-dimensional noble-metal dichalcogenide PdSeS for NO2 gas: DFT and semiclassical studies

The gas sensing performance of the pentagonal two-dimensional noble-metal dichalcogenide PdSeS for NO2 gas detection has been extensively studied using DFT, the nudged elastic band (NEB) model, and the semiclassical Boltzmann theory. The NEB calculations indicate a highly effective physical NO2 adsorption, implying the potential of the PdSeS monolayer as an excellent material for creating efficient and reusable gas sensors. Additionally, the PdSeS monolayer, characterized as a semiconductor with a band gap of 1.34 eV, demonstrates the emergence of a deep trap in the band structure following the adsorption of NO2 gas. Subsequently, the values of σxx and σyy decrease significantly from 11.41 and 42.82 S/cm to 2.24 and 0.99 S/cm, respectively. The decrease in conductivity can be attributed to several factors, including an increase in effective mass, the formation of deep traps, and a reduction in the number of charge carriers. Furthermore, our calculations demonstrate the potential use of the PdSeS monolayer in work function (WF) type sensors. Consequently, the pentagonal two-dimensional PdSeS monolayer fulfills the demands of portable, affordable, and reusable NO2 gas sensors by exhibiting remarkable sensitivity and short recovery time.

New data-driven estimation of metal element in rocks using a hyperspectral data and geochemical data

The inversion of Cu contents in rocks based on hyperspectral remote sensing is indicative of geochemical exploration as well as mineral resource exploration. Due to the strong redundancy of band information in hyperspectral data, characteristic band selection is the key to improving the accuracy of metal element inversion models in hyperspectral data. In this study, we proposed a framework that innovatively focuses on secondary screening method (SSM) as the core, allowing users to add different data preprocessing methods and machine learning algorithms to the framework based on expert experience, to obtain high-precision metal element inversion models suitable for sampling data. we first preprocessed the measured spectral curves using the first order derivative, second order derivative, continuum removal, multiple scattering correction and variance analysis to highlight band absorption characteristics. In addition, we performed SSM to obtain Cu-related characteristic bands, which were further used as input data to train and compare the performance of Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest (RF) and Gradient Boosting Regression Tree (GBRT). The results show that both the spectral pretreatment and modeling method could potentially impact the inversion accuracy. The accuracy of the ensemble method applied in fresh faces data (i.e., a CR-SSM-RF with R2 values of 0.77 and CR-SSM-GBRT with R2 values of 0.81) performed better than other models with an accuracy improvement of up to 80%. Compared to the full band model, the accuracy of characteristic bands selection model increased 50%. This also illustrates that the CR-SSM-RF or CR-SSM-GBRT model significantly improves the accuracy of the inversion of Cu contents in rocks compared to the original spectral reflectance. Our study provides a new idea for selecting different spectral preprocessing methods as the input values for the future model and will also offer an executable framework for the inversion of Cu contents in rocks of different lithology on a large scale in the future.

Study on the photovoltaic characteristics of solar cells based on PbS quantum dots and carbon counter electrode

Lead sulfide (PbS) quantum dots (QDs) with their customizable and precise energy levels, have emerged as highly promising photosensitizers in quantum dots sensitized solar cells (QDSSCs). This research focuses on fabricating high-performance QDSSCs anchored on PbS QDs sensitized mesoporous titania coated FTO electrodes. The PbS QDs were grown on mesoporous TiO2 through the Successive Ionic Layer Absorption and Reaction (SILAR) method. The optical properties were analyzed using UV–visible absorption spectroscopy, revealing that the SILAR cycles impacted the size and band gap of the PbS QDs. By analyzing the absorption data and the hyperbolic band model (HBM), the size of the PbS QDs were estimated to be 1.65 nm, 2.126 nm, 2.326 nm, 2.5692 nm, and 3.048 nm for 1 to 5 SILAR cycles, respectively. The effect of SILAR cycles was investigated, demonstrating a direct correlation between the number of SILAR cycles and the photovoltaic performance of the device. The maximum power conversion efficiency of 2.07 % was attained using 2 SILAR cycles, in contrast to 1.33 % and 1.63 % obtained with one and three SILAR cycles, respectively. Furthermore, impedance spectroscopy (IS), (Cs-V) (Rs-V) was employed at different frequencies, highlighting lowest series resistance for 2 SILAR cycles confirming its highest photovoltaic performance. Moreover, The utilization of a low-cost polysulfide electrolyte and carbon electrode has yielded superior or comparable outcomes compared to previously reported data.

‘The Event of the Age’: Appraising the Sousa Band’s 1911 Australasian Tour

This article argues for the significance and impact of the Sousa Band’s 1911 Australasian tour on local musical practice, through the lenses of cultural dynamics, reception, and legacy. Set against growing nationalism in the years preceding the Great War, the Sousa Band’s thirteen-week tour was enthusiastically welcomed by Australian audiences and underscored by the band’s alignment with the growing popularity of ragtime music, itself a point of negotiation between Anglo-Celtic and US American cultural influence in Australia. The majority-woodwind instrumentation of the Sousa Band is contextualized within the shifting favours of the time, given Australian audiences’ greater familiarity with British-inspired all-brass band models, within which the Sousa Band’s use of saxophones presaged certain changes in local musical practice that would unfold over subsequent decades. The influence of John Philip Sousa’s concert programming and statements as a public intellectual are also examined to contend that, while never quite ‘highbrow,’ an underlying theme of cultural uplift contributed towards the tour’s enduring impression on a new generation of Australian musicians.