Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
October 12th, 2019
Calculations performed by the Vienna Ab initio Simulation Package can be used to identify the intrinsic electronic properties of nanoscale materials and predict the potential water-splitting photocatalysts.
This protocol reports a computational screening of photo catalyst with type two bond alignment by first principles Softwire vast expressive Calais, boron nitrite nanoribbons encapsulated inside nanotubes. And taking other examples. Step one, optimize the atomic structure.
Prepare for input files for structure relaxation calculation by vast, INCAR, POSCAR, POTCAR, and KPOINTS. There are specified parameters in the file INCAR that define the calculation. The line in the file INCAR enclosed by blue box, in case that all atoms are relaxed until the force on each atom is less than 0.028 per atom.
The file POSCAR contains the atomic geometry information. The initial lattice parameters in the file, POSCAR can be chosen from the radical or experimental references. As indicated by orange box.
KPOINTS defines a KPOINT mesh and POSCAR is still the potential file generates the initial structure of boron nitrites nanoribbon for POSCAR. First download POSCAR for boron nitrites box unit from the website of The Materials Project. They used V2SXF to convert POSCAR to a file with SXF format.
The SXF file can be right expression. Type V2SXF POSCAR onto no in band two system. And output POSCAR SXFGZ tab gunzi POSCAR, delta SXF-GZ.
And output POSCAR SXF. We'll use xcrysden to build supercell of boron nitrite. Type xcrysden-sxf POSCAR.Xsf.
Select the menu, modifying number of illness drawn and extend the style in X and Y direction. Select the menu, file. Save XSF structure to export the super cell structure.
Use xmakemol to open the super cell by typing xmakemol-f supercell. Select the menu, and add it visible. Click toggle to delay the atoms inside the ranging and cast nanoribbon with desired width and chirality.
Boron nitrites nanotube can be constructed by nanotube modeler, open nanotube modeler EXC in Window system. Select the menu, select type B-N. And specify the chirality.
Select the menu, file, save XYZ table to export this structure. Use VMD Southwire to check the atomic structure before start making the calculation job. Type VMD onto internal VMD system.
In the opened VMD main window. Select the menu, file, molecule, and find the POSCAR through the browse window. Load POSCAR by the type MAGE and the score POSCAR.
Display the structure by different styles in the graphical representations drawing methods window. For example, choose CPK. Each atom is represented by a sphere and each bond is represented by a stake.
Type qsub job. pbs to submit the job to the computer. Class to a portable batch systems script.
This is a example of the script which named job.pbs. After the job finished, if ratio requires accuracy stopping structure energy minimization appears at the end of output lock. The converging result is a pend.
The resulting POSCAR will be used as an input file POSCAR for the following calculations. For analyzing electronic properties of materials. Step two.
Calculate the encapsulation energy. Type mkdir nanocomposite isolated-nanoribbon isolated-nanotube to create qsub folders for nanocomposite, nanoribbon and nanotube internal in Linux system. Prepare one PBS script, job.
pbs and four input files INCAR, POSCAR, POTCAR and KPOINTS. For energy, calculation in each folder. The input file POSCAR in the same way that relaxes structure and count CAR forms that one.
Go to each folder and type qsub job. pbs onto internal Linux system. The series of mated jobs will perform the static self consistent energy calculations of nanocomposites, isolates nanoribbon, isolates nanotube respectively.
Next track the total energy from the file OUTCAR for each system back up command. After finishing the static self-consistent calculations then compute the encapsulation energy as shown in this formula. The periodic interaction of the nanocomposite is allow Z axis and L is the lattice constant of the unit cell, along Z.The encapsulation energy can be used as an estimate for the energetic stability of nanocomposite.
Step three, extract the electronic properties from the band structure. Prepare one PBS, script job. pbs and the six input files.
INCAR, POSCAR, POTCAR, KPOINTS, CHGCAR, and CHG. For band-calculations, said ICHARG equals 11 in INCAR. The pre-conversion CHGCAR and CHG files are from static self-consistent calculations.
The KPOINT sampling in KPOINTS is in line-mode. Type qsub job. pbs onto internal Linux system to submit the job.
Use p4vasprun to generate the projected band. Load vasprun. xml by typing p4v vasprun.
xml on terminal. Select the menu, electronic local DOS bands. Control and then select bands.
Specify the atomic numbers of nanotube in the label atoms selection. Get the atomic number by pointing to the corresponding atoms using VMD. Specify the color, type, and size of symbol for the projected band structure.
Those menus symbol and the symbol size. Press the menu, add new line. The graph will show the band structure with the contributions from nanotube.
Then repeat the same procedure to gather projected band with contributions from nanoribbon. Select the menu graph export. Export the graph into the file with agr format.
For example, saved as 11-4.agr. Use XM grades to ID the projected bands. Type xmgrace11-4.
agr on terminal to start xmgrace in menu system. Select the menu, plot access properties to ID the label and the range of the axes. Select the manual plot set appearance to read the energy value as specified band number and key point.
Release band maximum and the conduction band minimum of nanotube or nanoribbon can be right from the projected band with the contributions of nanotube or nanoribbons respectively. Then calculate the valence band offset, conduction band offset and the band gap. Select the menu, file, point to export the graph with APS formats.
Calculate the band decompose to charge density for VBM and CBM. Prepare one pbs, script job. pbs and seven input files, INCAR, POSCAR, POTCAR, KPOINTS, WAVECAR, CHGCAR, and CHG.
Specify the band numbers for CBM and VBM. Then type IBAND in INCAR. Use the single corresponding KPOINT for each band edge.
The point converges CHGCAR, CHG. And the WAVECAR files, are from static self-consistent calculations. Type qsub job.
pbs onto internal Linux system to submit the job. Use VMD to plot VBM and CBM in real space after the job finished. Start a VMD session and load POSCAR.
Select the menu, file, new molecule in the VMD main window. Find the PARCHG through the browse window. Load the PARCHG by the type, press and score PARCHG.
Select the menu, draw solid surface and show a solid surface in the graphical representations window. Change the ISO value to an appropriate value, for example 0.02. Change the color of the ISO surface though the menu coloring method.
Step four, modulate the electronic properties of the nanocomposite by external fields. Add a transverse electric field to the nanocomposite. Prepare one PBS, script job.
pbs and the four input files, INCAR, POSCAR, POTCAR, and KPOINTS. Define the strength of the electric field by the tag e-field in units of eV Astrum. Set LDIPOL equals T, set IDIPOL equals 2.
And the electric field will be applied allow Y axis. Perform static self-consistent calculations and band structure calculations, following steps two and three without structure optimization. Add a longitudinal test of strength to the nanocomposite, chain the lattice parameter along the periodical direction to consider the string effect.
For example, to optimize the lattice parameter of nanocomposite along the axis is 2.5045 Astrum If applied 1%and the axial tensile strength along the Z through action. Change the lattice parameter in POSCAR to 2.529545 Astrum. Relax the modify structure following step one, perform static self-consistent calculations and band structure calculations, following steps two and three.
Representative results. The encapsulation energy can be used as a rough estimate for the energetic stability of the nanocomposite. The encapsulation energy of boron nitrite nanoribbons, a 2, a 3, and a 4 encapsulated inside the boron nitrite nanotube 11, 11 are 0.033 eV Astrum, 0.068 eV Astrum and 0131 eV Astrum respectively.
Although the encapsulation energy varies by an order of magnitude with the nanoribbon size, all three nanocomposites present type two band alignment. This is a band structure of boron nitrite and nanoribbons, a four encapsulated inside the boron nitrite nanotube, 11, 11. Valence band maximum and the conduction band minimum located at nanotube and nanoribbon respectively.
The staggered band alignment is beneficial for light currents to the main mechanism of charge transport is as follows. The photo generates electric ad hull in nanoribbons at the X point. Then the whole dissociates from nanoribbons to nanotube.
The calculated valence band offset is 317 micro eV. It is larger than the thermal energy at 300 Kelvins which is about 13 micro eV. This effectively decreases the recombination rate of photo-generated carriers.
To enhance light harvest through a wide spectrum, this transfers electric field and longitudinal tensile strength are applied to boron nitrite nanoribbons, a four encapsulated inside the boron nitrite nanotube, 11, 11. This is the evolution of band edges relative to the vacuum level, and reacts to no field. A substantial gap reduction up to near 0.95 eV is observed in this nanocomposite by both external fields.
More importantly, the staggered band alignment is preserved against the diffuse. The redox potentials of water are marked by the blue dashed lines. The band edges are relative to the redox potentials indicate that such a nanocomposite may be promising for water splitting.Conclusion.
It is rapid and efficient to use a computational screening approach to discover low dimensional materials that possess properties suitable for solo water splitting. Such a one dimensional system can propose to integrate a photo-catalated hydrogen generation and safe capsule style reach. The photo-generated electrons could be collected by nanoribbon, protons penetrate through the nanotube to generate hydrogen molecule joined by electrostatic attraction.
The produced hydrogen is completely isolated within the nanotube to avoid unwanted reverse reaction or explosion. The first principles calculations will underestimate the gap status using the PPE functional. But they can capture the essential trends in band alignment and band offsets.
More accurate values of valence band offset, conduction band offset and the band gap are needed if compared with experimental work, operate functional would be rather employed, which will be time consuming than PPE function. Besides to address the lifetime of photo generated hulls and electric in static states nano diabetics deny me some calculations that are needed. This is important to design photo-catalysts with long lifetime careers.
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Overview
This protocol details a computational screening method for photocatalysts using first principles calculations. It focuses on type two bond alignment in boron nitride nanoribbons encapsulated within nanotubes.
Key Study Components
Area of Science
- Computational Chemistry
- Nanoscale Materials
- Photocatalysis
Background
- Understanding electronic properties of nanoscale materials is crucial for developing efficient photocatalysts.
- First principles calculations provide insights into material behavior at the atomic level.
- Type two bond alignment is a specific configuration that can enhance photocatalytic activity.
- Boron nitride nanoribbons are promising candidates for photocatalytic applications.
Purpose of Study
- To optimize the atomic structure of photocatalysts for improved performance.
- To predict potential water-splitting photocatalysts using computational methods.
- To explore the encapsulation of boron nitride nanoribbons in nanotubes for enhanced properties.
Methods Used
- Optimization of atomic structures using Vienna Ab initio Simulation Package.
- Preparation of input files for structure relaxation calculations (INCAR, POSCAR, POTCAR, KPOINTS).
- Defining calculation parameters in the INCAR file.
- Ensuring all atoms are relaxed until the force on each atom is below a specified threshold.
Main Results
- Successful identification of intrinsic electronic properties of the studied materials.
- Predictions of photocatalytic efficiency based on structural optimizations.
- Insights into the role of encapsulation in enhancing photocatalytic activity.
- Demonstration of the effectiveness of computational screening in material discovery.
Conclusions
- The study provides a framework for computationally screening photocatalysts.
- First principles calculations are effective in predicting material properties.
- Encapsulation strategies can significantly influence photocatalytic performance.
Frequently Asked Questions
Executive Industry Relevance
First-principles computational screening of one-dimensional van der Waals heterostructures enables rapid identification of photocatalyst candidates with tailored electronic properties. This approach supports early-stage de-risking and predictive confidence in material selection for energy-relevant applications, such as water splitting. Integrating band alignment analysis into discovery workflows accelerates portfolio triage and informs downstream experimental validation.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables hypothesis-driven interrogation of electronic structure-function relationships in novel nanomaterials.
- Supports mechanistic de-risking by quantifying band offsets and energetic stability of candidate heterostructures.
- Facilitates predictive confidence in selecting materials with favorable charge separation and transport properties.
Screening & Assay Development
- Prepares validated computational models for downstream experimental screening of photocatalytic activity.
- Standardizes electronic property outputs, such as band gaps and band edge positions, for reproducible comparison.
- Enables scalable virtual screening of material combinations prior to synthesis or assay development.
Translational & Preclinical Research
- Aligns computational predictions with experimental redox potentials to assess translational relevance for water splitting.
- Provides continuity from in silico discovery to preclinical material validation by identifying candidates with preserved band alignment under external modulation.
- Supports risk-adjusted advancement of materials with robust electronic properties across operational conditions.
Pipeline & Workflow Integration
This computational protocol integrates into the discovery continuum from early hypothesis testing through lead identification of functional nanomaterials.
- Discovery Biology: Quantifies band alignment and energetic stability to clarify mechanistic hypotheses for charge separation.
- Screening: Delivers standardized electronic property metrics for high-throughput virtual screening.
- Analytics: Provides quantitative band offsets, encapsulation energies, and projected band structures for comparative analysis.
- Translational Research: Maps band edge positions to redox potentials, supporting translational continuity for photocatalytic applications.
- Enterprise Reuse: Establishes a reusable computational workflow adaptable to diverse nanomaterial systems.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in material selection.
- Operational Value: Standardizes computational screening and enables reproducible, scalable analyses.
- Strategic Value: Informs go/no-go decisions and enhances capital efficiency by prioritizing high-potential candidates.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of materials with robust electronic profiles.
Implementation Considerations
- Requires expertise in density-functional theory and computational materials science.
- Depends on access to high-performance computing infrastructure and specialized simulation software (e.g., VASP).
- Necessitates standardized input preparation and cross-team data management for reproducibility.
- Adaptable to various nanomaterial systems with appropriate parameterization and validation.
- Accuracy of band gap predictions may be limited by functional choice; hybrid functionals increase computational cost.
Biopharma FAQs
Chapters in this video
0:00
Title
0:17
Optimize the Atomic Structure
3:50
Calculate the Encapsulation Energy
5:10
Extract the Electronic Properties from the Band Structure
8:49
Modulate the Electronic Properties of the Nanocomposite (NT encapuslated inside NR) by External Fields
10:15
Results
12:33
Conclusion
