Laser powder bed fusion(LPBF)makes it possible for biodegradable zinc(Zn)to be used to produce customized orthopedic implants.In this research,we investigate the impact of laser power and scanning speed on the develop...Laser powder bed fusion(LPBF)makes it possible for biodegradable zinc(Zn)to be used to produce customized orthopedic implants.In this research,we investigate the impact of laser power and scanning speed on the development of surface quality,relative densification,and texture during LPBF of Zn implants.Increasing laser power was able to decrease melt viscosity and surface tension,which improved the metallurgical bonding between adjacent tracks.Uneven and twisted tracks also became continuous and straight.Scanning speed could controlmolten-pool temperature to restrain grain natural orientation,achieving various crystal orientations and a weakened texture.Importantly,it further avoided the thermal expansion and contraction caused by excessive energy storage and accumulation in the matrix,thus reducing the generation of high-dislocation density.As a result,by selecting a reasonable laser power and scanning speed,the LPBF parts exhibited a flat surface morphology and a high density over 99.5%.Their average hardness,mechanical strength,and elongation reached 50.2 HV,127.8 MPa,and 7.6%,respectively.Additionally,the parts displayed a moderate degradation rate and excellent osteogenic properties.All these results provide a basis for selecting process parameters to optimize the comprehensive properties of LPBF-processed Zn parts for biodegradable applications.展开更多
Biomedical magnesium(Mg)alloys have garnered significant attention because of their unique biodegradability,favorable biocompatibility,and suitable mechanical properties.The incorporation of rare earth(RE)elements,wit...Biomedical magnesium(Mg)alloys have garnered significant attention because of their unique biodegradability,favorable biocompatibility,and suitable mechanical properties.The incorporation of rare earth(RE)elements,with their distinct physical and chemical properties,has greatly contributed to enhancing the mechanical performance,degradation behavior,and biological performance of biomedical Mg alloys.Currently,a series of RE-Mg alloys are being designed and investigated for orthopedic implants and cardiovascular stents,achieving substantial and encouraging research progress.In this work,a comprehensive summary of the state-of-the-art in biomedical RE-Mg alloys is provided.The physiological effects and design standards of RE elements in biomedical Mg alloys are discussed.Particularly,the degradation behavior and mechanical properties,including their underlying action are studied in-depth.Furthermore,the preparation techniques and current application status of RE-Mg alloys are reviewed.Finally,we address the ongoing challenges and propose future prospects to guide the development of high-performance biomedical Mg-RE alloys.展开更多
There is growing interest in applying machine learning techniques in the field of materials science.However,the interpretation and knowledge extracted from machine learning models is a major concern,particularly as fo...There is growing interest in applying machine learning techniques in the field of materials science.However,the interpretation and knowledge extracted from machine learning models is a major concern,particularly as formulating an explicit model that provides insight into physics is the goal of learning.In the present study,we propose a framework that utilizes the filtering ability of feature engineering,in conjunction with symbolic regression to extract explicit,quantitative expressions for the band gap energy from materials data.We propose enhancements to genetic programming with dimensional consistency and artificial constraints to improve the search efficiency of symbolic regression.We show how two descriptors attributed to volumetric and electronic factors,from 32 possible candidates,explicitly express the band gap energy of Na Cl-type compounds.Our approach provides a basis to capture underlying physical relationships between materials descriptors and target properties.展开更多
Laser three-dimensional(3D)printing offers significant advantages in integrating the shape and function of regen-erative tissues through biomimetic manufacturing.However,its effectiveness is limited by the lack of spe...Laser three-dimensional(3D)printing offers significant advantages in integrating the shape and function of regen-erative tissues through biomimetic manufacturing.However,its effectiveness is limited by the lack of specialized biopolymer powders-while solvent methods that use residual solvents produce powders with poor biocompati-bility,mechanical methods result in irregularly shaped crystals.In this study,a biopolymer powder spheroidiza-tion and shaping technology,which utilizes the evolution of irregular powders into spheres with minimal surface free energy in the molten state,is proposed based on the thermodynamic principle of minimum energy.Initially,the motion trajectory and temperature field of the poly(L-lactic acid)(PLLA)powder during spheroidization were quantitatively assessed and optimized using Stokes’law and Fourier’s principle.Subsequently,the cohesive forces and aggregation kinetics of the polymer chains were calculated using molecular dynamics.Finally,based on these calculations,a phase-field model was constructed to simulate the evolution of the spheroidization rate and deduce the optimal parameters for the process.This precise approach enhances PLLA spheroidization control for laser 3D printing,improves part densification and surface quality,and offers a clean and efficient path for preparing high-quality PLLA spheroidized powder for laser 3D printing.展开更多
In this study,we propose a novel joint training model for named entity recognition(NER)that combines BERT,BiLSTM,CRF,and a reading comprehension(RC)mechanism.Traditional BERT-BiLSTM-CRF models often struggle with inac...In this study,we propose a novel joint training model for named entity recognition(NER)that combines BERT,BiLSTM,CRF,and a reading comprehension(RC)mechanism.Traditional BERT-BiLSTM-CRF models often struggle with inaccu-rate boundary detection and excessive fragmentation of named entities due to their lack of specialized vocabulary.Our model addresses these issues by integrating an RC mechanism,which helps refine fragmented results by enabling the model to more precisely identify entity boundaries without relying on an expert-annotated dictionary.Additionally,segmentation issues are further mitigated through a segmented combined voting-and positive-sample-coverage technique.We applied this model to develop a database for mesoporous bioactive glass(MBG).Furthermore,a classifier was developed to automatically detect the presence of pertinent information within paragraphs.For this study,200 articles were searched using MBG-related keywords,and the data were split into a training set and a test set in a 9:1 ratio.A total of 492 paragraphs were automatically extracted for training,and 50 paragraphs were extracted for testing the model.The results demonstrate that our joint training model achieves an accuracy of 92.8%in named entity recognition,which is 4.3%higher than the 88.5%accuracy of the traditional BERT-BiLSTM-CRF model.展开更多
A highly sensitive electrochemical aptasensor was developed for the detection of kanamycin,employing a DNA signal amplification strategy that combines RecJf exonuclease-assisted target recycling with a hybridization c...A highly sensitive electrochemical aptasensor was developed for the detection of kanamycin,employing a DNA signal amplification strategy that combines RecJf exonuclease-assisted target recycling with a hybridization chain reaction(HCR).The sensing platform is constructed by covalently immobilizing double-stranded DNA(dsDNA)comprised of a kanamycin-specific aptamer and its thiol-modified complementary strand(SH-CDNA)onto a gold electrode.In the presence of kanamycin and RecJf exonuclease,the aptamer selectively binds to kanamycin,dissociating from the dsDNA complex.The RecJf exonuclease then cleaves the aptamer,releasing kanamycin and initiating a cycle of repetitive binding and release.The residual SH-CDNA on the electrode triggers an HCR between two types of ferrocene-labeled hairpin DNA,forming an elongated stable dsDNA nanostructure.This results in an amplified electrochemical signal proportional to the logarithm of kanamycin concentration over a range of 0.01–10 nmol/L,with a remarkable detection limit of 1.8 pmol/L.The aptasensor's performance was validated by analyzing spiked kanamycin in pharmaceutical eye drops and milk samples,yielding recovery rates between 95.6%and 104.8%and relative standard deviations from 1.4%to 4.2%.With its exceptional selectivity and sensitivity,this aptasensor offers a compelling alternative to traditional HPLC for rapid on-site detection of kanamycin.Capitalizing on the specificity of aptamers,the sensor design presented herein serves as a valuable blueprint for engineering detectors of other molecules,with significant implications for analytical chemistry and food safety monitoring.展开更多
Human T-cell lymphophilic virus type 1(HTLV-1),the known retrovirus causing cancer in humans,is closely associated with adult T-cell leukemia/lymphoma and HTLV-1 associated myelopathy/tropical spastic paraparesis.Due ...Human T-cell lymphophilic virus type 1(HTLV-1),the known retrovirus causing cancer in humans,is closely associated with adult T-cell leukemia/lymphoma and HTLV-1 associated myelopathy/tropical spastic paraparesis.Due to its ability to evade the host's defense mechanisms,early tracking of HTLV-1 becomes crucial.In this study,we integrateλ-Exonuclease(λ-Exo)-assisted target recycling with a terminal deoxynucleotidyl transferase(TdT)-mediated template-free DNA extension process to develop an electrochemical analysis platform for the specific and sensitive detection of HTLV-1 DNA.During theλ-Exo-assisted target recycling,HTLV-1 DNA is recognized by hairpin DNA(Hp-DNA),forming double-stranded DNA(dsDNA)through DNA hybridization.The dsDNA,featuring blunt 5'terminal phosphorylation,is cleaved byλ-Exo,generating abundant short output sequence(sDNA).HTLV-1 DNA is released,initiating a cyclic hybridization-cleavage process.Subsequently,thiol-labelled capture DNA(CP-DNA)assembled on gold electrode surface captures a substantial amount of the generated sDNA,forming CP-DNA-sDNA nanostructures.When TdT and dNTPs are present on the electrode surface,the 3'-OH terminal of sDNA extends to generate long single-stranded DNA(ssDNA)structure.Methylene blue(MB)is selected as the electrochemical signal molecule.MB not only binds with ssDNA but also interacts specifically with dsDNA,resulting in a significantly enhanced electrochemical signal on modified electrode surface.The detection limit of HTLV-1 DNA is as low as 19 amol/L(S/N=3)when the two signal amplification strategies are combined.The analysis platform exhibits excellent analytical performance and holds promise as a novel tool for the early tracing and diagnosis of HTLV-1 DNA.展开更多
Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narr...Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.展开更多
External electric field and interlayer twist introduce diverse changes in their confined electronic states of bilayer graphene quantum dots. Using a quantum-dot model, the band gaps of twisted bilayer graphene in fini...External electric field and interlayer twist introduce diverse changes in their confined electronic states of bilayer graphene quantum dots. Using a quantum-dot model, the band gaps of twisted bilayer graphene in finite sizes of about 1.4–2.4 nm with varying twist angles are studied in the presence of an electrostatic field perpendicular to the flakes by means of first-principles calculations. The size-dependent gaps are widened by the interlayer twist, but narrowed by the applied field. Their coupling, however, results in an enhanced Stark response in the twisted structures of which the field-induced band-gap variations are about 3–4 times as large as that of the corresponding untwisted structures under the same field strength. The exceptional Stark shifts come from the field-induced asynchronous shifts in their occupied and virtual energy levels, which are further enhanced by strong interlayer coupling at specific twist angles. Moreover, the shift of band gaps with the field strength follows the quadratic Stark response with large second-order shifting coefficients. The enhanced and tunable Stark shift suggests a gateway to the band engineering of bilayer graphene quantum dots by tuning their sizes, twist angles and their coupling with applied field.展开更多
Machine learning models for exploring structure-property relation for hydroxyapatite nanoparticles(HANPs)are still lacking.A multiscale multisource dataset is presented,including both experimental data(TEM/SEM,XRD/cry...Machine learning models for exploring structure-property relation for hydroxyapatite nanoparticles(HANPs)are still lacking.A multiscale multisource dataset is presented,including both experimental data(TEM/SEM,XRD/crystallinity,ROS,anti-tumor effects,and zeta potential)and computation results(containing 41,976 data samples with up to 9768 atoms)of nanoparticles with different sizes and morphologies at density functional theory(DFT),semi-empirical DFTB,and force field,respectively.Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance.To avoid the pre-determination of features,we also developed a predictive deep learning model within the framework of graph convolution neural network with good generalizability.Energies with DFT accuracy are achievable for largesized nanoparticles from the learned correlations and scale functions for mapping different theoretical levels and particle sizes.The simulated XRD spectra and crystallinity values are in good agreement with experiments.展开更多
The piano sonata Dawn is a significant work from the middle period of the renowned composer Beethoven,composed in 1804.Beethoven’s creation of numerous remarkable musical compositions has been indispensable to the de...The piano sonata Dawn is a significant work from the middle period of the renowned composer Beethoven,composed in 1804.Beethoven’s creation of numerous remarkable musical compositions has been indispensable to the development of modern music art worldwide,and as one of his renowned piano pieces,Dawn holds great value for study in terms of its musical content and performance approach.From the perspective of Beethoven’s compositional journey,Dawn plays a transitional role within his evolving creative thought,embodying both Classical and Romantic musical elements.This study focuses on Dawn,offering an in-depth analysis of the piece’s compositional background,musical content,and performance interpretation.Through this exploration,the study aims to elucidate the artistic techniques employed in the work and reveal the profound emotional and intellectual depth Beethoven embedded within its melodies and tonal color.It is hoped that this research will enrich the foundational theories in China’s modern music arts studies and provide guidance for performance practice in piano music.展开更多
基金The National Natural Science Foundation of China(Nos.51935014,52165043,52105352,and 82072084)Jiangxi Provincial Natural Science Foundation of China(No.20212BAB214026)+1 种基金The Project of State Key Laboratory of High Performance Complex ManufacturingThe Project of Science and Technology of Jiangxi Provincial Education Department(No.GJJ210835).
文摘Laser powder bed fusion(LPBF)makes it possible for biodegradable zinc(Zn)to be used to produce customized orthopedic implants.In this research,we investigate the impact of laser power and scanning speed on the development of surface quality,relative densification,and texture during LPBF of Zn implants.Increasing laser power was able to decrease melt viscosity and surface tension,which improved the metallurgical bonding between adjacent tracks.Uneven and twisted tracks also became continuous and straight.Scanning speed could controlmolten-pool temperature to restrain grain natural orientation,achieving various crystal orientations and a weakened texture.Importantly,it further avoided the thermal expansion and contraction caused by excessive energy storage and accumulation in the matrix,thus reducing the generation of high-dislocation density.As a result,by selecting a reasonable laser power and scanning speed,the LPBF parts exhibited a flat surface morphology and a high density over 99.5%.Their average hardness,mechanical strength,and elongation reached 50.2 HV,127.8 MPa,and 7.6%,respectively.Additionally,the parts displayed a moderate degradation rate and excellent osteogenic properties.All these results provide a basis for selecting process parameters to optimize the comprehensive properties of LPBF-processed Zn parts for biodegradable applications.
基金supported by National Key Research and Development Program of China[2023YFB4605800]National Natural Science Foundation of China[51935014,52165043]+3 种基金JiangXi Provincial Natural Science Foundation of China[20224ACB204013,20224ACB214008]Jiangxi Provincial Cultivation Program for Academic and Technical Leaders of Major Subjects[20225BCJ23008]Anhui Provincial Natural Science Foundation[2308085ME171]The University Synergy Innovation Program of Anhui Province[GXXT-2023-025,GXXT-2023-026].
文摘Biomedical magnesium(Mg)alloys have garnered significant attention because of their unique biodegradability,favorable biocompatibility,and suitable mechanical properties.The incorporation of rare earth(RE)elements,with their distinct physical and chemical properties,has greatly contributed to enhancing the mechanical performance,degradation behavior,and biological performance of biomedical Mg alloys.Currently,a series of RE-Mg alloys are being designed and investigated for orthopedic implants and cardiovascular stents,achieving substantial and encouraging research progress.In this work,a comprehensive summary of the state-of-the-art in biomedical RE-Mg alloys is provided.The physiological effects and design standards of RE elements in biomedical Mg alloys are discussed.Particularly,the degradation behavior and mechanical properties,including their underlying action are studied in-depth.Furthermore,the preparation techniques and current application status of RE-Mg alloys are reviewed.Finally,we address the ongoing challenges and propose future prospects to guide the development of high-performance biomedical Mg-RE alloys.
基金financially supported by the National Key Research and Development Program of China(No.2016YFB0700500)the Guangdong Province Key Area R&D Program(No.2019B010940001)。
文摘There is growing interest in applying machine learning techniques in the field of materials science.However,the interpretation and knowledge extracted from machine learning models is a major concern,particularly as formulating an explicit model that provides insight into physics is the goal of learning.In the present study,we propose a framework that utilizes the filtering ability of feature engineering,in conjunction with symbolic regression to extract explicit,quantitative expressions for the band gap energy from materials data.We propose enhancements to genetic programming with dimensional consistency and artificial constraints to improve the search efficiency of symbolic regression.We show how two descriptors attributed to volumetric and electronic factors,from 32 possible candidates,explicitly express the band gap energy of Na Cl-type compounds.Our approach provides a basis to capture underlying physical relationships between materials descriptors and target properties.
基金supported by National Key Research and Development Program of China(Grant No.2023YFB4605800)Natural Science Foundation of China(Grant Nos.51935014,52365046,52105352,82072084)+1 种基金JiangXi Provincial Natural Science Foundation of China(Grant No.20224ACB204013)Schig-Qinling Program(Grant No.2022360702014891).
文摘Laser three-dimensional(3D)printing offers significant advantages in integrating the shape and function of regen-erative tissues through biomimetic manufacturing.However,its effectiveness is limited by the lack of specialized biopolymer powders-while solvent methods that use residual solvents produce powders with poor biocompati-bility,mechanical methods result in irregularly shaped crystals.In this study,a biopolymer powder spheroidiza-tion and shaping technology,which utilizes the evolution of irregular powders into spheres with minimal surface free energy in the molten state,is proposed based on the thermodynamic principle of minimum energy.Initially,the motion trajectory and temperature field of the poly(L-lactic acid)(PLLA)powder during spheroidization were quantitatively assessed and optimized using Stokes’law and Fourier’s principle.Subsequently,the cohesive forces and aggregation kinetics of the polymer chains were calculated using molecular dynamics.Finally,based on these calculations,a phase-field model was constructed to simulate the evolution of the spheroidization rate and deduce the optimal parameters for the process.This precise approach enhances PLLA spheroidization control for laser 3D printing,improves part densification and surface quality,and offers a clean and efficient path for preparing high-quality PLLA spheroidized powder for laser 3D printing.
基金sponsored by the National Key Research and Development Program of China(2021YFB3802105 and 2021YFB3802102).
文摘In this study,we propose a novel joint training model for named entity recognition(NER)that combines BERT,BiLSTM,CRF,and a reading comprehension(RC)mechanism.Traditional BERT-BiLSTM-CRF models often struggle with inaccu-rate boundary detection and excessive fragmentation of named entities due to their lack of specialized vocabulary.Our model addresses these issues by integrating an RC mechanism,which helps refine fragmented results by enabling the model to more precisely identify entity boundaries without relying on an expert-annotated dictionary.Additionally,segmentation issues are further mitigated through a segmented combined voting-and positive-sample-coverage technique.We applied this model to develop a database for mesoporous bioactive glass(MBG).Furthermore,a classifier was developed to automatically detect the presence of pertinent information within paragraphs.For this study,200 articles were searched using MBG-related keywords,and the data were split into a training set and a test set in a 9:1 ratio.A total of 492 paragraphs were automatically extracted for training,and 50 paragraphs were extracted for testing the model.The results demonstrate that our joint training model achieves an accuracy of 92.8%in named entity recognition,which is 4.3%higher than the 88.5%accuracy of the traditional BERT-BiLSTM-CRF model.
基金financially supported by Central Government Guided Local Science and Technology Development Fund Project(guikeZY22096017)Natural Science Foundation of Guangxi Province(2024GXNSFDA010036)National Natural Science Foundation of China(22164014,U23A2089)。
文摘A highly sensitive electrochemical aptasensor was developed for the detection of kanamycin,employing a DNA signal amplification strategy that combines RecJf exonuclease-assisted target recycling with a hybridization chain reaction(HCR).The sensing platform is constructed by covalently immobilizing double-stranded DNA(dsDNA)comprised of a kanamycin-specific aptamer and its thiol-modified complementary strand(SH-CDNA)onto a gold electrode.In the presence of kanamycin and RecJf exonuclease,the aptamer selectively binds to kanamycin,dissociating from the dsDNA complex.The RecJf exonuclease then cleaves the aptamer,releasing kanamycin and initiating a cycle of repetitive binding and release.The residual SH-CDNA on the electrode triggers an HCR between two types of ferrocene-labeled hairpin DNA,forming an elongated stable dsDNA nanostructure.This results in an amplified electrochemical signal proportional to the logarithm of kanamycin concentration over a range of 0.01–10 nmol/L,with a remarkable detection limit of 1.8 pmol/L.The aptasensor's performance was validated by analyzing spiked kanamycin in pharmaceutical eye drops and milk samples,yielding recovery rates between 95.6%and 104.8%and relative standard deviations from 1.4%to 4.2%.With its exceptional selectivity and sensitivity,this aptasensor offers a compelling alternative to traditional HPLC for rapid on-site detection of kanamycin.Capitalizing on the specificity of aptamers,the sensor design presented herein serves as a valuable blueprint for engineering detectors of other molecules,with significant implications for analytical chemistry and food safety monitoring.
基金financially supported by Central Leading Local Science and Technology Development Fund Project(guikeZY22096017)Natural Science Foundation of Guangxi Province(2024GXNSFDA010036)National Natural Science Foundation of China(22164014,U23A2089).
文摘Human T-cell lymphophilic virus type 1(HTLV-1),the known retrovirus causing cancer in humans,is closely associated with adult T-cell leukemia/lymphoma and HTLV-1 associated myelopathy/tropical spastic paraparesis.Due to its ability to evade the host's defense mechanisms,early tracking of HTLV-1 becomes crucial.In this study,we integrateλ-Exonuclease(λ-Exo)-assisted target recycling with a terminal deoxynucleotidyl transferase(TdT)-mediated template-free DNA extension process to develop an electrochemical analysis platform for the specific and sensitive detection of HTLV-1 DNA.During theλ-Exo-assisted target recycling,HTLV-1 DNA is recognized by hairpin DNA(Hp-DNA),forming double-stranded DNA(dsDNA)through DNA hybridization.The dsDNA,featuring blunt 5'terminal phosphorylation,is cleaved byλ-Exo,generating abundant short output sequence(sDNA).HTLV-1 DNA is released,initiating a cyclic hybridization-cleavage process.Subsequently,thiol-labelled capture DNA(CP-DNA)assembled on gold electrode surface captures a substantial amount of the generated sDNA,forming CP-DNA-sDNA nanostructures.When TdT and dNTPs are present on the electrode surface,the 3'-OH terminal of sDNA extends to generate long single-stranded DNA(ssDNA)structure.Methylene blue(MB)is selected as the electrochemical signal molecule.MB not only binds with ssDNA but also interacts specifically with dsDNA,resulting in a significantly enhanced electrochemical signal on modified electrode surface.The detection limit of HTLV-1 DNA is as low as 19 amol/L(S/N=3)when the two signal amplification strategies are combined.The analysis platform exhibits excellent analytical performance and holds promise as a novel tool for the early tracing and diagnosis of HTLV-1 DNA.
基金financial support from the Natural Science Foundation of China(No.21973064 to DX and No.22173064 to MY).
文摘Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.
基金The authors thank the financial support from the National Natural Science Foundation of China (Nos. 21773159 and 11904328).
文摘External electric field and interlayer twist introduce diverse changes in their confined electronic states of bilayer graphene quantum dots. Using a quantum-dot model, the band gaps of twisted bilayer graphene in finite sizes of about 1.4–2.4 nm with varying twist angles are studied in the presence of an electrostatic field perpendicular to the flakes by means of first-principles calculations. The size-dependent gaps are widened by the interlayer twist, but narrowed by the applied field. Their coupling, however, results in an enhanced Stark response in the twisted structures of which the field-induced band-gap variations are about 3–4 times as large as that of the corresponding untwisted structures under the same field strength. The exceptional Stark shifts come from the field-induced asynchronous shifts in their occupied and virtual energy levels, which are further enhanced by strong interlayer coupling at specific twist angles. Moreover, the shift of band gaps with the field strength follows the quadratic Stark response with large second-order shifting coefficients. The enhanced and tunable Stark shift suggests a gateway to the band engineering of bilayer graphene quantum dots by tuning their sizes, twist angles and their coupling with applied field.
基金This work was supported by the National Key Research and Development Program of China(2017YFB0702601)the National Natural Science Foundation of China(grant nos.21873045,22033004).We gratefully acknowledge the High Performance Computing Centre of Nanjing University for providing the IBM Blade cluster system and Nanxin Pharm Co.,Ltd.,Nanjing.
文摘Machine learning models for exploring structure-property relation for hydroxyapatite nanoparticles(HANPs)are still lacking.A multiscale multisource dataset is presented,including both experimental data(TEM/SEM,XRD/crystallinity,ROS,anti-tumor effects,and zeta potential)and computation results(containing 41,976 data samples with up to 9768 atoms)of nanoparticles with different sizes and morphologies at density functional theory(DFT),semi-empirical DFTB,and force field,respectively.Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance.To avoid the pre-determination of features,we also developed a predictive deep learning model within the framework of graph convolution neural network with good generalizability.Energies with DFT accuracy are achievable for largesized nanoparticles from the learned correlations and scale functions for mapping different theoretical levels and particle sizes.The simulated XRD spectra and crystallinity values are in good agreement with experiments.
文摘The piano sonata Dawn is a significant work from the middle period of the renowned composer Beethoven,composed in 1804.Beethoven’s creation of numerous remarkable musical compositions has been indispensable to the development of modern music art worldwide,and as one of his renowned piano pieces,Dawn holds great value for study in terms of its musical content and performance approach.From the perspective of Beethoven’s compositional journey,Dawn plays a transitional role within his evolving creative thought,embodying both Classical and Romantic musical elements.This study focuses on Dawn,offering an in-depth analysis of the piece’s compositional background,musical content,and performance interpretation.Through this exploration,the study aims to elucidate the artistic techniques employed in the work and reveal the profound emotional and intellectual depth Beethoven embedded within its melodies and tonal color.It is hoped that this research will enrich the foundational theories in China’s modern music arts studies and provide guidance for performance practice in piano music.
