Random Stacking Research Articles

Impact of the stacking sequence on the bandgap and luminescence properties of bulk, bilayer, and monolayer hexagonal boron nitride

We examine the effects of stacking sequence and number of layers on the electronic and luminescence properties of hexagonal boron nitride (h-BN) structures with first-principles calculations based on density functional and many-body perturbation theory. We explored the variations of the magnitude and character (direct or indirect) of the quasiparticle bandgap and interband optical matrix elements for bulk, bilayer, and monolayer stacking polytypes. Although the fundamental gap for most structures is indirect, phonon-assisted transitions are strong (typically 600 times stronger than bulk Si) and enable efficient deep-ultraviolet (UV) luminescence. The polarization of the emitted light is transverse electric, which facilitates light extraction perpendicularly to the h-BN basal plane. Random stacking in turbostratic BN breaks the crystal symmetry and enables optical transitions across the quasi-direct bandgap, albeit with a weak matrix element. Our results demonstrate that h-BN is a promising material for efficient deep-UV light emitters.

Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection

Prostate cancer is a highly incident malignant cancer among men. Early detection of prostate cancer is necessary for deciding whether a patient should receive costly and invasive biopsy with possible serious complications. However, existing cancer diagnosis methods based on data mining only focus on diagnostic accuracy, while neglecting the interpretability of the diagnosis model that is necessary for helping doctors make clinical decisions. To take both accuracy and interpretability into consideration, we propose a stacking-based ensemble learning method that simultaneously constructs the diagnostic model and extracts interpretable diagnostic rules. For this purpose, a multi-objective optimization algorithm is devised to maximize the classification accuracy and minimize the ensemble complexity for model selection. As for model combination, a random forest classifier-based stacking technique is explored for the integration of base learners, i.e., decision trees. Empirical results on real-world data from the General Hospital of PLA demonstrate that the classification performance of the proposed method outperforms that of several state-of-the-art methods in terms of the classification accuracy, sensitivity and specificity. Moreover, the results reveal that several diagnostic rules extracted from the constructed ensemble learning model are accurate and interpretable.

Research Progress On Comprehensive Utilization Of Fruit And Vegetable Waste

Random stacking and inefficient treatment of fruit and vegetable waste is one of the main reasons for environmental pollution and resource waste, and the research on its resource utilization has become the first problem to be solved. This paper reviews the research progress of fruit and vegetable waste in energy, fertilizer and feed utilization in order to provide reference for resources utilization of fruit and vegetable waste in China.

Water Resources Assessment of China's Transboundary River Basins Using a Machine Learning Approach

AbstractA comprehensive and reliable assessment of the water resources in China's transboundary river basins is vital for water resources management and peaceful development. In this study, we built machine learning (random forest, gradient boosting, and stacking) and traditional linear models to identify the relation between the runoff coefficient and its influencing factors, including topography, climate, land cover, and soil. The cross‐validation results show that the machine learning models greatly outperform the traditional linear model in predicting runoff coefficient. High‐resolution (0.1°) runoff coefficient and runoff maps for the China's transboundary river basins riparian countries were produced and compared with other estimates at the country level. The best water resources estimates achieved from the machine learning model are consistent with the Food and Agriculture Organization of the United Nations AQUASTAT database (root‐mean‐square error = 76.97 km3/year, normalized root‐mean‐square error = 12%) at the country level. This outperformed two currently available runoff products: the UNH/GRDC Global Composite Runoff Fields and the Global Streamflow Characteristics Dataset. The study also demonstrated that accurate precipitation data can improve runoff and water resources estimation accuracy and that climate and topographic factors have a controlling role in prediction, whereas the influences of land cover and soils are weak. Finally, China's transboundary water resources were calculated and thoroughly assessed at basin and country levels.

First-principles investigation of Sc-III/IV under high pressure

Using an ab initio evolutionary structure prediction method in conjunction with density functional theory, we performed a systematic investigation of the structural transition of elemental scandium under pressure up to 250 GPa. Our prediction successfully reproduced several allotropes which have been reported in the literature, including the Sc-I, Sc-II, and Sc-V. Moreover, we observed a series of energetically degenerate and geometrically similar structures at 110--195 GPa, which can partly explain the experimental observations regarding the unsolved phases III and IV reported by Y. Akahama et al. [Phys. Rev. Lett. 94, 195503 (2005)]. A detailed comparison of the powder x-ray diffraction pattern suggested that the $Ccca\text{\ensuremath{-}}20$ phase is a likely candidate for the observed Sc-III, while Sc-IV may be explained by a structure with random stacking of two different structural units. We also used the candidate Sc-III structure as the model system to explore its superconducting behavior under pressures between 80 and 130 GPa. The predicted superconducting transition temperature ${T}_{\mathrm{c}}$ values are in satisfactory agreement with previous experimental results.

Structural evolution of pitch fibers during low temperature carbonization

Isotropic pitch-based carbon fibers (IPCFs) were prepared at various carbonization temperatures from 400 to 1200 °C using pitch fibers stabilized at 290 °C to investigate their structural evolution during the carbonization process. The chemical structure changes of IPCFs as a function of carbonization temperature were thoroughly analyzed using various characterization tools, such as Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and laser desorption/ionization time-of-flight mass spectrometry. The characterization results were systematically compared with those of the ordering structures obtained from Raman spectroscopy and X-ray diffraction. The results demonstrated that the transformation of the chemical structure, ordering structure, and physical properties of IPCFs proceeds with three distinct steps depending on the carbonization temperature: fusion of small aromatic structures of pitch, random stacking of the fused structure and conversion into mostly disordered carbon structures. In addition, we found a correlation of the chemical and ordering structure changes with the enhancement of physical properties, such as density, tensile modulus, and strength.

Spray Dried Cerium Oxide/Graphene/Polyaniline Composite for Supercapacitor Applications

Supercapacitor materials can be broadly classified into three types: carbon based, metal oxide based and conductive polymers. Though, each of these materials has their own advantages, they also have unique drawbacks, like poor cycle life for conductive polymers, inherently low conductivity for most metal oxides and low energy density for carbon based material. In the study, a composite of these three materials is synthesized through an easily scalable process to yield a supercapacitor material with high specific capacitance, excellent rate capability and cycle life. Micron sized cerium oxide (CeO2)/graphene/polyaniline (PANI) composites are synthesized using spray-drying and chemical reduction. First, graphene oxide/PANI composites are prepared by an in-situ polymerization technique. A hydrothermally one-step process is used synthesized CeO2 nanoparticles. These raw materials are then thoroughly mixed through ultrasonication and spray dried at 200°C. The use of PANI has two benefits. One, it acts as a binder between the inorganic CeO2 nanoparticles and GO during the spray dried process to yield micron sized spherical particles. Second, PANI also forms a uniform highly conductive layer, which boosts the electronic transmission and thereby the supercapacitance of the composite. The CeO2 improves the poor cycle life of PANI. The random stacking and agglomerating of GO and CeO2 during the spray drying process produces numerous channels which acts as pathways for the electrolytic ions. Thus the individual elements work synergistically to improve the supercapacitor performance. The synthesized composites have been structurally characterized using XRD, FTIR and SEM. The electrochemical properties have been evaluated in standard three-electrode set-up with 3M KOH electrolyte using cyclic voltammetry, galvanostatic charge discharge curves and impedance analysis. The results from the electrochemical analysis show that the spray dried composite material show high specific capacitance, energy density and long cycle life.

The Properties of Layered Cathode Material LiNi1/3Co1/3Mn1/3O2 with Fe-doping

A layered cathode-active material LiNi1/3Co1/3Mn1/3O2 was synthesized by sintering the admixture of oxalate co-precipitation powder of Cobalt, Nickel, and Manganese and Li2CO3. The influence of Fe-doped on the structural and electrochemical properties of LiNi1/3Co1/3Mn1/3O2 was characterized by XRD, SEM, BET and electrochemical experiments. The results reveal that LiNi1/3Co1/3Mn1/3O2 with small amounts (≤6%) of Fe-doping keeps the α-NaFeO2 layered structure. As the doping content increases, the lattice parameters increase and the random stacking happens. The average particle size is about 0.3-0.4 μm and the specific surface area decreases. The Fe-doping improved charge and discharge capacity. The 2% Fe-doped materials deliver an initial charge and discharge capacity of 243.2 and 167.4mAh·g−1.

Close-packed block copolymer micelles induced by temperature quenching

Close-packed structures of uniformly sized spheres are ubiquitous across diverse material systems including elements, micelles, and colloidal assemblies. However, the controlled access to a specific symmetry of self-assembled close-packed spherical particles has not been well established. We investigated the ordering of spherical block copolymer micelles in aqueous solutions that was induced by rapid temperature changes referred to as quenching. As a function of quench depth, the quenched self-assembled block copolymer micelles formed three different close-packed structures: face-centered cubic (fcc), random stacking of hexagonal-close-packed layers (rhcp), and hexagonal-close-packed (hcp). The induced hcp and rhcp structures were stable for at least a few weeks when maintained at their quench temperatures, but heating or cooling these hcp and rhcp structures transformed both structures to fcc crystallites with coarsening of the crystal grains, which suggests that these noncubic close-packed structures are intermediate states. Time-resolved scattering experiments prove that the micellar rhcp structures do not originate from the rapid growth of competing close-packed structures. We speculate that the long-lived metastable hcp and rhcp structures originate from the small size of crystal grains, which introduces a nonnegligible Laplace pressure to the crystal domains. The reported transitions from the less stable hcp to the more stable rhcp and fcc are experimental observations of Ostwald's rule manifesting the transition order of the key close-packed structures in the crystallization of close-packed uniform spheres.

Structural disorder of natural BimSen superlattices grown by molecular beam epitaxy

The structure and morphology of BimSen epitaxial layers with compositions ranging from Bi2Se3 to the Bi1Se1 grown by molecular beam epitaxy with different flux compositions are investigated by transmission electron microscopy, high-resolution x-ray diffraction, and atomic force microscopy. It is shown that the lattice structure changes significantly as a function of the beam flux composition, i.e., Se/BiSe flux ratio that determines the stoichiometry of the layers. A perfect Bi2Se3 phase is formed only with a sufficiently high additional Se flux, whereas Bi1Se1 is obtained when only a BiSe compound source without additional Se is used. For intermediate values of the excess Se flux during growth, Bi2Se3-delta layers are obtained with the Se deficit delta varying between 0 and 1. This Se deficit is accommodated by incorporation of additional Bi-Bi double layers into the Bi2Se3 structure that otherwise exclusively consists of Se-Bi-Se-Bi-Se quintuple layers. While a periodic insertion of such Bi double layers would result in the formation of natural BimSen superlattices, we find that this Bi double-layer insertion is rather stochastic with a high degree of disorder depending on the film composition. Therefore, the structure of such epilayers is better described by a one-dimensional paracrystal model, consisting of disordered sequences of quintuple and double layers rather than by strictly periodic natural superlattices. From detailed analysis of the x-ray diffraction data, we determine the dependence of the lattice parameters a and c and distances of the individual (0001) planes d(j) as a function of composition, evidencing that only the in-plane lattice parameter a shows a linear dependence on composition. The simulation of the diffraction curves with the random stacking paracrystal model yields an excellent agreement with the experimental data and it brings quantitative information on the randomness of the stacking sequence, which is compared to growth modeling using Monte Carlo simulations. The analysis of transmission electron microscopy data furthermore confirms that the Bi-Bi bilayers contain a large amount of vacancies of up to 25%. Conductivity and Hall data confirm that BimSen phases containing Bi-Bi double layers exhibit a rather semimetallic behavior.

Band engineering in twisted molybdenum disulfide bilayers

In order to explore the theoretical relationship between interlayer spacing, interaction and band offset at the atomic level in vertically stacked two-dimensional (2D) van der Waals (vdW) structures, we propose an analytical model to address the evolution of interlayer vdW coupling with random stacking configurations in MoS2 bilayers based on the atomic-bond-relaxation correlation mechanism. We found that interlayer spacing changes substantially with respect to the orientations, and the bandgap increases from 1.53 eV (AB stacking) to 1.68 eV (AA stacking). Our results reveal that the evolution of interlayer vdW coupling originates from the interlayer interaction, leading to interlayer separations and electronic properties changing with stacking configurations. Our predictions constitute a demonstration of twist engineering the band shift in the emergent class of 2D crystals, transition-metal dichalcogenides.

The role of solvent preparation in soft template assisted synthesis of mesoporous alumina

Synthesis of mesoporous aluminas (MAs) in aqueous and alcoholic solutions in soft template assisted sol-gel process was systematically studied with a particular attention to the role of the initial solvent preparation. Two synthesis pathways were employed, namely, the aqueous phase synthesis in water and water/ethanol mixtures and the solvent evaporation-induced self-assembly (EISA) synthesis in alcohols (ethanol, isopropanol and n-butanol). The results showed that in all the reaction mediums, the hydrolysis of aluminium isopropoxide played a crucial role. Decreasing water proportion reduced the hydrolysis rate of aluminium isopropoxide and consequently led to a more uniform stacking of the boehmite colloids formed and narrower the pore size distribution (PSD), higher surface area and greater pore volume of the MA synthesized. For the EISA pathway conducted in alcohols, the tardiness and incompleteness of hydrolysis in isopropanol provoked a random stacking of the aluminium precursor species, resulted in a disordered mesoporosity and a broad PSD. Under the experimental conditions studied for the EISA pathway, the addition of 3.0 mL HNO3 per 20 mmol of aluminium isopropoxide in ethanol favoured the formation of a uniform MA with very narrow PSD.

Indium Incorporation Induced Morphological Evolution and Strain Relaxation of High Indium Content InGaN Epilayers Grown by Metal–Organic Chemical Vapor Deposition

The indium (In) incorporation induced morphological evolution and strain relaxation of high In content InGaN epilayers grown by metal–organic chemical vapor deposition (MOCVD) were investigated. With the decrease of growth temperature from 753 to 627 °C, In incorporation increases from 0.10 to 0.42, and the surface morphology evolves from initially mound-like three-dimensional surface roughness to progressive smoothness. Such morphology evolution mechanism can be well accounted for by a self-regulating model of islands’ proportions considering both strain relaxation and In surfactant effect comprehensively. Additionally, the strain relaxation and microstructural defects of such InGaN epilayers were investigated by X-ray diffraction reciprocal space mapping and cross-sectional transmission electron microscopy, respectively. It is found that, with the increase of In content, plastic relaxation via generating random stacking faults along with the surface sawtooth roughening becomes a more important strain re...

Thermodynamics and Kinetics of Nucleobase Stacking Oligomerization Revealed by Molecular Dynamics Simulations.

The aggregation of N6N9-dimethyladenine in aqueous solution into stacked oligomers represents an important model system for oligomerization processes. Molecular dynamics simulations allowed us to extract statistically converged stacking thermodynamics as well as converged association and dissociation kinetics. The simulations confirm an oligomerization mechanism according to a random isodesmic stacking model without significant cooperative effects. Multiple oligomerization and dissociation events were used to characterize stacking and unstacking processes and intermediate states in atomic detail. Standard three-point rigid body water force field models lead to significantly accelerated dynamics and an underestimation of enthalpic and entropic contributions. The fully flexible SPCFW water model yielded close agreement with the experimental equilibrium constant, entropy contribution, and, importantly, kinetic observables. In addition, the performance of several other water models was systematically tested in stacking dimerization simulations indicating that the realistic prediction of thermodynamics and kinetics will likely benefit from the use of flexible water models.

One-pot synthesis of MoSe2 hetero-dimensional hybrid self-assembled by nanodots and nanosheets for electrocatalytic hydrogen evolution and photothermal therapy

We designed and prepared a hetero-dimensional hybrid (HDH) based on molybdenum selenide (MoSe2) nanodots (NDs) anchored in few-layer MoSe2 nanosheets (NSs) (MoSe2 HDH) via a one-pot hydrothermal process. The MoSe2 HDH exhibits excellent electrocatalytic activity toward hydrogen evolution reaction (HER). This is because, on the one hand, the edge-abundant features of MoSe2 NDs and the unique defect-rich structure at the interface of MoSe2 NSs/NDs could bring in more active sites for HER; on the other hand, the random stacking of the flake-like MoSe2 NSs on the surface of the supporting electrode may achieve efficient charge transport. Additionally, the MoSe2 HDH shows good water stability, desirable biocompatibility, and high near infrared (NIR) photothermal conversion efficiency. Therefore, the MoSe2 HDH is investigated as a nanomedicine in NIR photothermal therapy (PTT) for cancer. Specifically, the MoSe2 HDH can be applied as a dual-modal probe for computed tomography (CT) and photoacoustic tomography (PA) imaging owing to its strong X-ray attenuation ability and NIR absorption. Therefore, the MoSe2 HDH, combining PTT with CT/PA imaging into one system, holds great potential for imaging-guided cancer theranostics. This work may provide an ingenious strategy to prepare other hetero-dimensional layered transition metal dichalcogenides.

Microbial reduction of graphene oxide by Azotobacter chroococcum

We report an eco-friendly route for the reduction of graphene oxide (GO) by Azotobacter chroococcum at room temperature. Examinations using X-ray photoelectron spectroscopy, X-ray diffraction, and transmission electron microscopy indicate the formation of reduced graphene oxide (rGO) with a low degree of agglomeration. Quantitative analyses of electron diffraction data show that the produced rGO consists of single-layer sheets with a random stacking. It is suggested that the GO was reduced directly by the nitrogenase via sequential additions of electrons and protons followed by dehydration. This approach avoids the use of toxic chemical agents and also reduces the agglomeration of rGO.

TLEL: A two-layer ensemble learning approach for just-in-time defect prediction

ContextDefect prediction is a very meaningful topic, particularly at change-level. Change-level defect prediction, which is also referred as just-in-time defect prediction, could not only ensure software quality in the development process, but also make the developers check and fix the defects in time [1]. ObjectiveEnsemble learning becomes a hot topic in recent years. There have been several studies about applying ensemble learning to defect prediction [2–5]. Traditional ensemble learning approaches only have one layer, i.e., they use ensemble learning once. There are few studies that leverages ensemble learning twice or more. To bridge this research gap, we try to hybridize various ensemble learning methods to see if it will improve the performance of just-in-time defect prediction. In particular, we focus on one way to do this by hybridizing bagging and stacking together and leave other possibly hybridization strategies for future work. MethodIn this paper, we propose a two-layer ensemble learning approach TLEL which leverages decision tree and ensemble learning to improve the performance of just-in-time defect prediction. In the inner layer, we combine decision tree and bagging to build a Random Forest model. In the outer layer, we use random under-sampling to train many different Random Forest models and use stacking to ensemble them once more. ResultsTo evaluate the performance of TLEL, we use two metrics, i.e., cost effectiveness and F1-score. We perform experiments on the datasets from six large open source projects, i.e., Bugzilla, Columba, JDT, Platform, Mozilla, and PostgreSQL, containing a total of 137,417 changes. Also, we compare our approach with three baselines, i.e., Deeper, the approach proposed by us [6], DNC, the approach proposed by Wang et al. [2], and MKEL, the approach proposed by Wang et al. [3]. The experimental results show that on average across the six datasets, TLEL could discover over 70% of the bugs by reviewing only 20% of the lines of code, as compared with about 50% for the baselines. In addition, the F1-scores TLEL can achieve are substantially and statistically significantly higher than those of three baselines across the six datasets. ConclusionTLEL can achieve a substantial and statistically significant improvement over the state-of-the-art methods, i.e., Deeper, DNC and MKEL. Moreover, TLEL could discover over 70% of the bugs by reviewing only 20% of the lines of code.

Boosting the terahertz nonlinearity of graphene by orientation disorder

The conical band structure is the cornerstone of graphene’s ultra-broadband optical conductivity. For practical use of graphene in nonlinear photonics, however, substantial increases of the light–matter interaction strength will be required while preserving the promising features of monolayers, as the interaction of light with a single atomic layer is limited due to the extremely short interaction length and low density of state, particularly for the long-wavelength region. Here, we report that this demand can be fulfilled by random stacking of high-quality large-area monolayer graphene up to a requested number of layers, which leads to the electronic interaction between layers being effectively switched off due to turbostratic disorder. The nonlinear characteristics of randomly stacked multilayer graphene (RSMG), which originates from a thermo-modulational feedback mechanism through ultrafast free-carrier heating and temperature-dependent carrier-phonon collisions, show clear improvements in the terahertz (THz) regime with increasing layer numbers, whereas as-grown multilayer graphene (AGMG) exhibits limited behaviors due to strong interlayer coupling. This controllable nonlinearity enhancement provides an ideal prerequisite for developing efficient graphene-based THz photonic devices.

Hexagonal Close-Packed $$^4\hbox {He}$$ 4 He as Crystalline Multilayered Polytype: An Alternative for ‘Supersolid’ or ‘Glassy-Like’ Phase

We apply the model of a crystalline polytype built of close-packed 2D monoatomic basal planes with triangular lattice to interpret the anomalous thermodynamical and mechanical properties of solid hexagonal close-packed (HCP) \(^4\hbox {He}\). The polytype is a 3D stack of the basal planes, and its structure can be built from the simplest periodic packing (HCP, FCC, 4H, 5H, 6R, \(\ldots \) etc.) up to random stacking fault system (RSFS) totally aperiodic in only c-direction perpendicular to the basal planes. RSFS is a crystal without microscopic translation symmetry along c-axis, i.e., entirely disordered in only one spatial direction. Despite of packing disorder, c-direction remains the crystallographic axis of third order at arbitrary sequence of the 2D plates in the whole stack. In a long-wave limit the HCP polytype can be treated as 3D anisotropic continuum, as a result its phonon spectrum and Helmholtz free energy have been calculated. The temperature dependence of the phonon pressure is calculated theoretically and compared with experimental data. A quantitative agreement between the theory and the experiment is achieved. Mechanical properties of \(^4\hbox {He}\) crystals in the framework of the polytype model are briefly discussed.

Sprayable superhydrophobic nano-chains coating with continuous self-jumping of dew and melting frost

Spontaneous movement of condensed matter provides a new insight to efficiently improve condensation heat transfer on superhydrophobic surface. However, very few reports have shown the jumping behaviors on the sprayable superhydrophobic coatings. Here, we developed a sprayable silica nano-porous coating assembled by fluorinated nano-chains to survey the condensates’ dynamics. The dewdrops were continuously removed by self- and/or trigger-propelling motion due to abundant nano-pores from random multilayer stacking of nano-chains. In comparison, the dewdrops just could be slipped under the gravity effect on lack of nano-pores coatings stacked by silica nano-spheres and nano-aggregates. More interestingly, the spontaneous jumping effect also occurred on micro-scale frost crystals under the defrosting process on nano-chains coating surfaces. Different from self-jumping of dewdrops motion, the propelling force of frost crystals were provided by a sudden increase of the pressure under the frost crystal.