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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Setting a baseline for Integrated Landscape Design: Cost and risk assessment in herbaceous feedstock supply chains

The literature on bioenergy has pointed out that financial risks and limited performance benchmarks are leading barriers to wide-spread industry expansion. The purpose of this paper is to document a baseline cost and risk assessment from the perspective of biorefinery management for two herbaceous feedstocks, switchgrass and corn stover, in an application of Integrated Landscape Design (ILD). Integrating energy crops into row crops has attracted considerable attention leading to a variety of ways to implement ILD. However, variation in production conditions, different methods of implementing unit operations, and technology performance variability are among many factors leading to financial risk facing biorefineries from feedstock supply based on ILD. Utilizing data from field-based trials on switchgrass and corn stover in Virginia and Iowa in the United States, we estimate feedstock logistics cost and characterize it by a distribution of potential outcomes. The results show that, across fields included in the study, logistics cost for switchgrass vary from 73 per Mg to 171 per Mg. For corn stover, the inter-field variation in cost ranges from 83 per Mg to 105 per Mg. Results for intra-field uncertainty show that the cost for switchgrass ranges from 58 per Mg to 74 per Mg, and for corn stover they range from 50 per Mg to 65 per Mg. Furthermore, these results inform a baseline from which improvements in ILD can be compared, and provide biorefinery management with a starting point for cost and risk estimates of feedstock from ILD.

09 BIOMASS FUELS↗
DOE OSTI2016

Succinic acid production from lignocellulosic hydrolysate by Basfia succiniciproducens

The production of chemicals alongside fuels will be essential to enhance the feasibility of lignocellulosic biorefineries. Succinic acid (SA), a naturally occurring C4-diacid, is a primary intermediate of the tricarboxylic acid cycle and a promising building block chemical that has received significant industrial attention. Basfia succiniciproducens is a relatively unexplored SA-producing bacterium with advantageous features such as broad substrate utilization, genetic tractability, and facultative anaerobic metabolism. Here B. succiniciproducens is evaluated in high xylose-content hydrolysates from corn stover and different synthetic media in batch fermentation. SA titers in hydrolysate at an initial sugar concentration of 60 g/L reached up to 30 g/L, with metabolic yields of 0.69 g/g, and an overall productivity of 0.43 g/L/h. These results demonstrate that B. succiniciproducens may be an attractive platform organism for bio-SA production from biomass hydrolysates.

09 BIOMASS FUELS↗
DOE OSTI2019

Decarbonizing agriculture through the conversion of animal manure to dietary protein and ammonia fertilizer

The decarbonization of agriculture faces many challenges and has received a level of attention insufficient to abate the worst effects of climate change and ensure a sustainable bioeconomy. Agricultural emissions are caused both by fossil-intensive fertilizer use and land-use change, which in turn are driven in part by increasing demand for dietary protein. To address this challenge, we present a synergistic system in which organic waste-derived biogas (a mixture of methane and carbon dioxide) is converted to dietary protein and ammonia fertilizer. This system produces low-carbon fertilizer inputs alongside high-quality protein, addressing the primary drivers of agricultural emissions. If the proposed system were implemented across the United States utilizing readily available organic waste from municipal wastewater, landfills, animal manure, and commercial operations, we estimate 30% of dietary protein intake and 127% of ammonia usage could be displaced while reducing land use, water consumption, and greenhouse gas emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Butyric acid: Applications and recent advances in its bioproduction

Butyric acid is an important C4 organic acid with broad applications. It is currently produced by chemosynthesis from petroleum-based feedstocks. However, the fermentative production of butyric acid from renewable feedstocks has received growing attention because of consumer demand for green products and natural ingredients in foods, pharmaceuticals, animal feed supplements, and cosmetics. In this review, strategies for improving microbial butyric acid production, including strain engineering and novel fermentation process development are discussed and compared regarding product yield, titer, purity and productivity. Future perspectives on strain and process improvements for butyric acid production are also discussed.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Ten questions concerning agent-based modeling of occupant behavior for energy and environmental performance of buildings

We report the complexity of occupant behavior is one of the major contributors to uncertainty in building performance simulation. Agent-based modeling (ABM), a computational simulation technique, has gained attention in the occupant modeling field due to its capability and flexibility to capture the heterogeneity and dynamics of human behavior and the emergent effects. While multiple efforts in the past decade have demonstrated the usefulness of the ABM approach for simulating occupants and their impacts on building performance, several crucial matters in the ABM research still remain unexplored. This paper presents ten questions that highlight the most important issues regarding ABM research and applications for occupant behavior in the context of building performance simulation. The questions and answers aim to provide insights into current and future ABM research, and more importantly to inspire new significant questions from young researchers in the field. This research is part of the IEA EBC Annex 79 project, occupant-centric building design and operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Analysis of predicted mean vote-based model predictive control for residential HVAC systems

Model Predictive Control (MPC) is an advanced process control method that has attracted much attention in building heating, ventilation, and air conditioning (HVAC) systems. Here, this paper analyzes the optimal precooling performance in residential buildings using MPC with two different comfort indices, namely, temperature and predicted mean vote (PMV). It first formulates, for each comfort index, an optimization problem that accounts for different factors, such as weather, home thermal condition, prediction horizon, time-of use (TOU) utility rate, and rated cooling capacity. The problem is then solved, resulting in an MPC strategy that determines the HVAC on/off control signal and minimizes energy cost over a receding time horizon while maintaining thermal comfort. The energy performance difference between temperature-based and PMV-based MPC strategies is subsequently investigated, especially in light of the interior wall surface temperature and under different combinations of the factors. Extensive simulation results demonstrated that the proposed MPC strategies are adaptive and their performances depend primarily on weather, home thermal condition, and prediction horizon, while the impact of TOU utility rate and rated cooling capacity is relatively small. Because the PMV-based MPC strategy can take advantage of the lower interior wall surface temperature due to precooling, it resulted in 8–45% cost savings for the scenarios investigated and an average increase of 0.042–0.113 in the absolute value of the PMV index compared to the temperature-based MPC strategy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Preface to the Special Issue on TOUGH Symposium 2015

The TOUGH Symposium 2015 was held in Berkeley, California, September 28-30, 2015. The TOUGH family of codes, developed at the Energy Geosciences Division of Lawrence Berkeley National Laboratory (LBNL), is a suite of computer programs for the simulation of multiphase and multicomponent fluid and heat flows in porous and fractured media with applications in many geosciences fields, such as geothermal reservoir engineering, nuclear waste disposal, geological carbon sequestration, oil and gas reservoirs, gas hydrate research, vadose zone hydrology and environmental remediation. Since the first release in the 1980s, many modifications and enhancements have been continuously made to TOUGH and its various descendants (iTOUGH2, TOUGH+, TOUGH-MP, TOUGHREACT, TOUGH+HYDRATE, TMVOC...), at LBNL and elsewhere. Today, these codes are used worldwide in academia, government organizations and private companies in problems involving coupled hydrological, thermal, biogeochemical and geomechanical processes. The Symposia, organized every 2-3 years, bring together developers and users for an open exchange on recent code enhancements and applications. In 2015, the Symposium was attended by one hundred participants, representing thirty-four nationalities. This Special Issue in Computers & Geosciences gathers extended versions of selected Symposium proceedings related to (i) recent enhancements to the TOUGH family of codes and (ii) coupled flow and geomechanics processes modeling. In this special issue, a paper by Jung et al. provides a new base version of TOUGH, TOUGH3. This latest version addresses the computational robustness and efficiency needed for the increasing complexity of the simulated processes and the growing size of model domains. Moreover, it consolidates the serial and parallel versions into a single code, hence, inconsistencies between different versions are less likely to occur, and code maintenance and development become more efficient. Finsterle et al. present a review of the capabilities of iTOUGH2 and illustrate the workflow of an integrated simulation-optimization framework. Liu et al. present an approach to reduce the computational cost of performing Bayesian inverse modeling and demonstrate their approach using a synthetic experiment simulated with TOUGH2. Regarding the mesh generation process, the paper by Doughty presents a generalization of the grid-generation algorithm embedded in TOUGH2 (MESHMAKER) to create one-column grids with fractal dimension, useful for representing flow through fractures or highly heterogeneous media with fractal geometry, and also for inverse methods. Sentís and Gable present a module to interface the open source code Lagrit with TOUGH2, that assists in the processes of mesh generation and model setup, and that can be particularly useful for complex geometries and large models. Bonduà et al. present a pre-processing tool (VORO2MESH) that features a full 3D Voronoi approach for structured and unstructured grids, and an enhanced post-processor (TOUGH2Viewer) for the visualization of 3D Voronoi grids. The literature review shows that coupled fluid flow and geomechanics has gained increasing attention in the last years in many geosciences fields. In this context, several codes of the TOUGH family have been successfully coupled with geomechanics software. The paper by Rutqvist presents a review of some of these TOUGH-based geomechanics models, of which the TOUGH-FLAC simulator – presented for the first time in the TOUGH Symposium 2003 – is well-known worldwide and has been used in a large variety of applications, encompassing the application domains of the TOUGH codes. Blanco-Martín et al. present an extension of TOUGH-FLAC to the finite strain framework, motivated by the existence of large strains associated with rock salt deformation in the context of heat-generating nuclear waste disposal. Following a paper published in the Special Issue of the TOUGH Symposium 2012 in this journal, Kim et al. present the current status of the TOUGH-RSBN simulator for coupled hydro-mechanical modeling of hydraulic fracturing using a discrete fracture network approach, and present some validation and demonstration cases. Rinaldi and Nespoli present TOUGH-SEED, an injection-induced seismicity modeling approach using TOUGH2 and a geomechanics stochastic model, and apply it to the Basel EGS project. Finally, Rinaldi et al. use the PEST protocol implemented in iTOUGH2, combined with TOUGH-FLAC as the forward model, to perform inverse modeling for the In Salah CO2 demonstration site in Algeria. Using pore pressures and InSAR displacements as observations, this approach allows for a multi-physics joint inversion and takes advantage of the inverse capabilities of iTOUGH2 (parameter estimation, sensitivity analysis, uncertainty estimation …), applied to coupled fluid flow and geomechanics. On behalf of the members of the TOUGH Symposium 2015 Organizing Committee, I hope that this collection of papers will provide the readers of Computers & Geosciences with a recent view of the capabilities of the TOUGH family of codes, as well as with current modeling research domains in the geosciences. Last but not least, I would like to express my gratitude to all the reviewers who kindly contributed to this Special Issue, as well as the Journal staff who have made this publication possible.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Ambient occlusion – A powerful algorithm to segment shell and skeletal intrapores in computed tomography data

During the last decades, X-ray (micro-)computed tomography has gained increasing attention for the description of porous skeletal and shell structures of various organism groups. However, their quantitative analysis is often hampered by the difficulty to discriminate cavities and pores within the object from the surrounding region.Herein, we test the ambient occlusion (AO) algorithm and newly implemented optimisations for the segmentation of cavities (implemented in the software Amira). The segmentation accuracy is evaluated as a function of (i) changes in the ray length input variable, and (ii) the usage of AO (scalar) field and other AO-derived (scalar) fields. The results clearly indicate that the AO field itself outperforms all other AO-derived fields in terms of segmentation accuracy and robustness against variations in the ray length input variable. The newly implemented optimisations improved the AO field-based segmentation only slightly, while the segmentations based on the AO-derived fields improved considerably.Additionally, we evaluated the potential of the AO field and AO-derived fields for the separation and classification of cavities as well as skeletal structures by comparing them with commonly used distance-map-based segmentations. For this, we tested the zooid separation within a bryozoan colony, the stereom classification of an ophiuroid tooth, the separation of bioerosion traces within a marble block and the calice (central cavity)-pore separation within a dendrophyllid coral. The obtained results clearly indicate that the ideal input field depends on the three-dimensional morphology of the object of interest The segmentations based on the AO-derived fields often provided cavity separations and skeleton classifications that at were superior to or impossible to obtain with commonly used distance-map-based segmentations. The combined usage of various AO-derived fields by supervisd or unsupervised segmentation algorithms might provide a promising target for future research to further improve the results for this kind of high-end data segmentation and classification. Furthermore, the application of the developed segmentation algorithm is not restricted to X-ray (micro-)computed tomographic data but may potentially be useful for the segmentation of 3D volume data from other sources.

Computed tomography, Micro-computed tomography, Po↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
DOE OSTI2020

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Modeling the thermodynamics of the FeTi hydrogenation under para-equilibrium: An ab-initio and experimental study

We report FeTi-based hydrides have recently re-attracted attention as stationary hydrogen storage materials due to favorable reversibility, good sorption kinetics and relatively low costs compared to alternative intermetallic hydrides. Employing the OpenCalphad software, the thermodynamics of the (FeTi) 1-x H x (0 ≤ x ≤ 1) system were assessed as a key basis for modeling hydrogenation of FeTi-based alloys. New thermodynamic data were acquired from our experimental pressure-composition-isotherm (PCI) curves, as well as first-principles calculations utilizing density functional theory (DFT). The thermodynamic phase models were carefully selected based on critical analysis of literature information and ab-initio investigations. Key thermodynamic properties such as dissociation pressure, formation enthalpies and phase diagrams were calculated in good agreement to our performed experiments and literature-reported data. This work provides an initial perspective, which can be extended to account for higher-order thermodynamic assessments and subsequently enables the design of novel FeTi-based hydrides. In addition, the assessed thermodynamic data can serve as key inputs for kinetic models and hydride microstructure simulations.

08 HYDROGEN↗
DOE OSTI2022

Thermodynamic modeling of the Pd–Zn system with uncertainty quantification and its implication to tailor catalysts

Pd–Zn intermetallic catalysts show encouraging combinations of activity and selectivity on well-defined active site ensembles. Thermodynamic description of the Pd–Zn system, delineating phase boundaries and enumerating site occupancies within intermediate alloy phases, is essential to determining the ensembles of Pd–Zn atoms as a function of composition and temperature. Combining the present extensive first-principles calculations based on density functional theory (DFT) and available experimental data, the Pd–Zn system was remodeled using the CALculation of PHAse Diagrams (CALPHAD) approach. High throughput modeling tools with uncertainty quantification, i.e., ESPEI and PyCalphad, were incorporated in the phase analysis. Here, the site occupancies across the γ phase composition region were given special attention. A four-sublattice model was used for the γ phase owing to its four Wyckoff positions, i.e., the outer tetrahedral (OT) site 8c, the inner tetrahedral (IT) site 8c, the octahedral (OH) site 12e, and the cuboctahedral (CO) site 24g. The site fractions of Pd and Zn calculated from the present thermodynamic model show the occupancy preference of Pd in the OT and OH sublattices in agreement with experimental observations. The force constants obtained from DFT-based phonon calculations further supports the tendency of Pd occupying the OH sublattice compared with the IT and CO sublattice. The catalytic ensembles changing from Pd monomers (Pd 1 ) to trimers (Pd 3 ) on the surface of γ phase are attributed to the increase of Pd occupancy in the OH sublattice.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Spatial resolution of different discretizations over long-time for the Dirac equation with small potentials

In this report we compare the long-time error bounds and spatial resolution of finite difference methods with different spatial discretizations for the Dirac equation with small electromagnetic potentials characterized by $\varepsilon \in (0, 1]$ a dimensionless parameter. We begin with the simple and widely used finite difference time domain (FDTD) methods, and establish rigorous error bounds of them, which are valid up to the time at $O(1/\varepsilon)$. In the error estimates, we pay particular attention to how the errors depend explicitly on the mesh size $h$ and time step $\tau$ as well as the small parameter $\varepsilon$. Based on the results, in order to obtain "correct" numerical solutions up to the time at $O(1/\varepsilon)$, the $\varepsilon$-scalability (or meshing strategy requirement) of the FDTD methods should be taken as $h = O(\varepsilon^{1/2})$ and $\tau = O(\varepsilon^{1/2})$. To improve the spatial resolution capacity, we apply the Fourier spectral method to discretize the Dirac equation in space. Error bounds of the resulting finite difference Fourier pseudospectral (FDFP) methods show that they exhibit uniform spatial errors in the long-time regime, which are optimal in space as suggested by the Shannon's sampling theorem. Extensive numerical results are reported to confirm the error bounds and demonstrate that they are sharp.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

An investigation of the multi-mode Richtmyer-Meshkov instability at a gas/HE interface using Pagosa

Here, in this work, we present a hydrocode Pagosa and explore the Richtmyer-Meshkov Instability (RMI) at an air/high explosive (HE) interface for the first time that is important but has not received much attention yet in the high explosive safety field. Thus, the presented Pagosa can be expected to predict the whole deflagration-to-detonation transition (DDT) process in future. In Pagosa, spatial discretization is implemented on cubic staggered grids by computing different variables at the vertex and the cell center, respectively, a special operator-splitting technique is employed to reduce the computational cost, and an artificial viscosity is added to handle the discontinuous shock waves in our simulations. To quantitatively evaluate the capability of Pagosa to solve these kinds of instabilities, the single mode Rayleigh-Taylor instability (RTI) and the multimode RMI at an air/SF 6 interface are first performed, respectively. The Pagosa results are compared with the related numerical solutions in the existing references and the experimental result. Moreover, a theoretical derivation of growth of RTI is also provided based on our numerical method. Subsequently, we explore the multi-mode RMI at an air/HE interface as well as the effects of several factors using Pagosa. Numerical results show that Pagosa is a powerful toolset to generate the right structures and the amplitude of RTI and RMI at an air/SF 6 interface. The solid HE can be penetrated by a strong shock wave and forms RMI deformations. The RMI at an air/HE interface behaves very different than at an air/SF 6 interface, periodic, decreased oscillation is observed due to material character, and is very sensitive to the initial simulation settings, that is, a tiny change in physical quantities will lead to a remarkable RMI structure, which is also observed in a shock bubble interaction. The findings in this work are significant, and will present a new insight for the high explosive field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

An implicit-in-time DPG formulation of the 1D1V Vlasov-Poisson equations

Efficient solution of the Vlasov equation, which can be up to six-dimensional, is key to the simulation of many difficult problems in plasma physics. The discontinuous Petrov-Galerkin (DPG) finite element methodology provides a framework for the development of stable (in the sense of Ladyzhenskaya–Babuška–Brezzi conditions) finite element formulations, with built-in mechanisms for adaptivity. While DPG has been studied extensively in the context of steady-state problems and to a lesser extent with space-time discretizations of transient problems, relatively little attention has been paid to time-marching approaches. In the present work, we study a first application of time-marching DPG to the Vlasov equation, using backward Euler for a Vlasov-Poisson discretization. We demonstrate adaptive mesh refinement for two problems: the two-stream instability problem, and a cold diode problem. Furthermore, we believe the present work is novel both in its application of unstructured adaptive mesh refinement (as opposed to block-structured adaptivity, which has been studied previously) in the context of Vlasov-Poisson, as well as in its application of DPG to the Vlasov-Poisson system. We also discuss extensive additions to the Camellia library in support of both the present formulation as well as extensions to higher dimensions, Maxwell equations, and space-time formulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Effect of radiation exposure on survival after first solid cancer diagnosis in A-bomb survivors

Comparison of the estimated effect of atomic bomb radiation exposure on solid cancer incidence and solid cancer mortality in the RERF Life Span Study (LSS) reveals a difference in the magnitude and shape of the excess relative risk dose response. A possible contributing factor to this difference is pre-diagnosis radiation effect on post-diagnosis survival. Pre-diagnosis radiation exposure theoretically could influence post-diagnosis survival by affecting the genetic makeup and possibly aggressiveness of cancer, or by compromising tolerance for aggressive treatment for cancer. We analyze the radiation effect on post-diagnosis survival in 20,463 LSS subjects diagnosed with first-primary solid cancer between 1958 and 2009 with particular attention to whether death was caused by the first-primary cancer, other cancer, or non-cancer diseases. From multivariable Cox regression analysis of cause-specific survival, the excess hazard at 1 Gy (EH 1Gy ) for death from the first primary cancer was not significantly different from zero – p = 0.23, EH 1Gy = 0.038 (95 % CI: –0.023, 0.104). Death from other cancer and death from non-cancer diseases both were significantly associated with radiation dose: other cancer EH 1Gy = 0.38 (95 % CI: 0.24, 0.53); non-cancer EH 1Gy = 0.24 (95 % CI: 0.13, 0.36), both p < 0.001. There is no detectable large effect of pre-diagnosis radiation exposure on post-diagnosis death from the first primary cancer in A-bomb survivors. A direct effect of pre-diagnosis radiation exposure on cancer prognosis is ruled out as an explanation for the difference in incidence and mortality dose response in A-bomb survivors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Molecular imaging to guide systemic cancer therapy: Illustrative examples of PET imaging cancer biomarkers

Molecular imaging agents have the ability to non-invasively visualize, characterize, and quantify the molecular biology of disease. Recent advances in nuclear probe development, particularly in PET radiotracers, have generated many new imaging agents with precise molecular targets. With such specificity, PET probes may be utilized as biomarkers to objectively interrogate and evaluate pathology. Whereas the current indications for PET imaging are predominately confined to staging and restaging of malignancy, the utility of PET greatly expands when utilized as a biomarker, the topic of this review. As an imaging biomarker, PET may be used to (1) measure target expression to select subsets of patients who would most benefit from targeted therapy; (2) measure early treatment response to predict therapeutic efficacy; and (3) relate tumor response to survival. This work will discuss the application of radiotracers to targeted cancer therapy. Particular attention is given to new radiotracers evaluated in recently completed clinical trials and those with current or potential clinical utility. The diverse roles of PET in clinical trails for drug development are also examined.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Controlling spin-orbit coupling strength of bulk transition metal dichalcogenide semiconductors

Transition metal dichalcogenide (TMD) semiconductors are attracting much attention in research regarding device physics based on their unique properties that can be utilized in spintronics and valleytronics. Although current studies concentrate on the monolayer form due to the explicitly broken inversion symmetry and the direct band gap, bulk materials also hold the capability of carrying spin and valley current. In this study, we report the methodology to continuously control the spin-orbit coupling (SOC) strength of bulk TMDs Mo 1-x W x Se 2 by changing the atomic ratio between Mo and W. The results show the size of band splitting at the K valley the measure of the coupling strength is linearly proportional to the atomic ratio of Mo and W. Here, our results thus demonstrate how to precisely tune the SOC coupling strength, and the collected information of which can serve as a reference for future applications of bulk TMDs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗