The amidoximated polyacrylonitrile （PAN） fiber Fe complexeswere prepared and used as the heterogeneous Fenton catalysts for thedegradation of28 anionicwater soluble azodyes inwater under visible irradiation. The mul...The amidoximated polyacrylonitrile （PAN） fiber Fe complexeswere prepared and used as the heterogeneous Fenton catalysts for thedegradation of28 anionicwater soluble azodyes inwater under visible irradiation. The multiple linear regression （MLR） methodwas employed todevelop the quantitative structure property relationship （QSPR） model equations for thedecoloration and mineralization of azodyes. Moreover, the predictive ability of the QSPR model equationswas assessed using Leave-one-out （LOO） and cross-validation （CV） methods. Additionally, the effect of Fe content of catalyst and the sodium chloride inwater on QSPR model equationswere also investigated. The results indicated that the heterogeneous photo-Fentondegradation of the azodyeswithdifferent structureswas conducted in the presence of the amidoximated PAN fiber Fe complex. The QSPR model equations for thedyedecoloration and mineralizationwere successfullydeveloped using MLR technique. MW/S （molecularweightdivided by the number of sulphonate groups） and N N=N （the number of azo linkage） are considered as the most importantdetermining factor for thedyedegradation and mineralization, and there is a significant negative correlation between MW/S or N N=N anddegradation percentage or total organic carbon （TOC） removal. Moreover, LOO and CV analysis suggested that the obtained QSPR model equations have the better prediction ability. The variation in Fe content of catalyst and the addition of sodium chloridedid not alter the nature of the QSPR model equations.展开更多
Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecul...Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices （Х）, modified molecular connectivity indices （ ^mХ^v ） and valance molecular connectivity indices （ ^mХ^v ）, with ^mХ^v calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression （MLR） method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.展开更多
While heteroatom doping serves as a powerful strategy for devising novel polycyclic aromatic hydrocarbons(PAHs), the further fine-tuning of optoelectronic properties via the precisely altering of doping patterns remai...While heteroatom doping serves as a powerful strategy for devising novel polycyclic aromatic hydrocarbons(PAHs), the further fine-tuning of optoelectronic properties via the precisely altering of doping patterns remains a challenge. Herein, by changing the doping positions of heteroatoms in a diindenopyrene skeleton, we report two isomeric boron, sulfur-embedded PAHs, named Anti-B_(2)S_(2) and Syn-B_(2)S_(2), as electron transporting semiconductors. Detailed structure-property relationship studies revealed that the varied heteroatom positions not only change their physicochemical properties, but also largely affect their solid-state packing modes and Lewis base-triggered photophysical responses. With their low-lying frontier molecular orbital levels, n-type characteristics with electron mobilities up to 1.5 × 10^(-3)cm^(2)V^(-1)s^(-1)were achieved in solution-processed organic field-effect transistors. Our work revealed the critical role of controlling heteroatom doping patterns for designing advanced PAHs.展开更多
A series of hydroxyl-terminated polyethylenes(HTPE)bearing various functional side groups(e.g.carboxyl,ester and butane groups)were synthesized by the combination of ring opening metathesis polymerization(ROMP)and vis...A series of hydroxyl-terminated polyethylenes(HTPE)bearing various functional side groups(e.g.carboxyl,ester and butane groups)were synthesized by the combination of ring opening metathesis polymerization(ROMP)and visible light photocatalytic thiol-ene reaction.The products are named as a,w-dihydroxyl-polyllpropionyloxythio)methinetrimethylene](HTPECarboxy),a,w dihydroxy-poly(methylpropionatethio)methinetrimethylene](HTPEeser)and a,wdihydroxyl-poly[(butylthio)methinetrimethylene](HTPEbutane)respectively.The investigation of ROMP indicated that the molecular weight of resultant hydroxy-terminated polybutadiene(HTPB)can be tailored by varying the feed ratios of monomer to chain transfer agent(CTA).The exploration of the photocatalytic thiol-ene reaction between HTPB precursor and methyl-3-mercaptopropionate revealed that blue light as well as oxygen accelerated the reaction.1H-NMR and 13C-NMR results verified all the double bonds in HTPB can be modified,and the main chain of resultant polymer can be considered as polyethylene.Subsequently,relationship between the structure of side groups and the thermal properties of functional PEs was studied.And the results suggested that the Tg was in the order of HTPEbuane<HTPEester<HTPEarboxy+.Greater interaction between side groups resulted in higher Tg.Moreover,all the functional PE samples exhibited poor thermostability as compared to HTPB.Finally,the promising applications for functional PEs were explored.HTPEcarboxy1 can be utilized as a smart material with pH-responsive properties due to its pH-dependent ionization of carboxyl side groups.HTPEbutane can be employed as a macro-initiator for building the triblock copolymer due to the presence of active hydroxyl end groups.HTPEester can serve as a plasticizer for PVC which can enhance the ductilityt of PVC without obviously sacrificing strength.展开更多
Materials and chemical scientists have tirelessly pursued the vision of creating atomically tailored materials.The promise of atomic precision in material synthesis lies in the potential to precisely control every asp...Materials and chemical scientists have tirelessly pursued the vision of creating atomically tailored materials.The promise of atomic precision in material synthesis lies in the potential to precisely control every aspect of a material's structure,thereby opening up opportunities for discovering and tuning novel physical properties[1].However,achieving atomically precise assemblies in practice remains a formidable challenge,largely due to the difficulty of controlling nucleation and growth processes at the most fundamental scale.展开更多
The theoretical linear solvation energy relationship(TLSER) approach was adopted to predict the aqueous solubility and n -octanol/water partition coefficient of three groups of environmentally important chemicals-poly...The theoretical linear solvation energy relationship(TLSER) approach was adopted to predict the aqueous solubility and n -octanol/water partition coefficient of three groups of environmentally important chemicals-polychlorinated biphenyls(PCBs), polychlorinated dibenzodioxins and dibenzofurans(PCDDs and PCDFs). For each compound, five quantum parameters were calculated using AM1 semiempirical molecular orbital methods and used as structure descriptors: average molecular polarizability(α), energy of the lowest unoccupied molecular orbit( E _ LUMO ), energy of the highest occupied molecular orbit( E _ HOMO ), the most positive charge on a hydrogen atom( q _+), and the most negative atomic partial charge( q _-) in the solute molecule. Then standard independent variables in TLSER equation was extracted and two series of quantitative equations between these quantum parameters and aqueous solubility and n -octanol/water partition coefficient were obtained by stepwise multiple linear regression(MLR) method. The developed equations have both quite high accuracy and explicit meanings. And the cross-validation test illustrated the good predictive power and stability of the established models. The results showed that TLSER could be used as a promising approach in the estimation of partition and solubility properties of macromolecular chemicals, such as persistent organic pollutants.展开更多
The demand for efficient and environmentally-benign electrocatalysts that help availably harness the renewable energy resources is growing rapidly. In recent years, increasing insights into the design of water electro...The demand for efficient and environmentally-benign electrocatalysts that help availably harness the renewable energy resources is growing rapidly. In recent years, increasing insights into the design of water electrolysers, fuel cells, and metal–air batteries emerge in response to the need for developing sustainable energy carriers, in which the oxygen evolution reaction and the oxygen reduction reaction play key roles. However, both reactions suffer from sluggish kinetics that restricts the reactivity. Therefore, it is vital to probe into the structure of the catalysts to exploit high-performance bifunctional oxygen electrocatalysts. Spinel-type catalysts are a class of materials with advantages of versatility, low toxicity, low expense, high abundance, flexible ion arrangement, and multivalence structure. In this review, we afford a basic overview of spinel-type materials and then introduce the relevant theoretical principles for electrocatalytic activity, following that we shed light on the structure–property relationship strategies for spinel-type catalysts including electronic structure, microstructure, phase and composition regulation,and coupling with electrically conductive supports. We elaborate the relationship between structure and property, in order to provide some insights into the design of spinel-type bifunctional oxygen electrocatalysts.展开更多
Since the two seminal papers were published independently in 2004, high-entropy-alloys(HEAs) have been applied to structural and functional materials due to the enhanced mechanical properties, thermal stability, and e...Since the two seminal papers were published independently in 2004, high-entropy-alloys(HEAs) have been applied to structural and functional materials due to the enhanced mechanical properties, thermal stability, and electrical conductivity. In recent years, HEA nanoparticles(HEA-NPs) were paid much attention to in the field of catalysis for the promoted catalytic activity. Furthermore, the various ratios among the metal components and tunable bulk and surface structures enable HEAs have big room to enhance the catalytic performance. Especially, noble-metal-based HEAs displayed significantly improved performance in electrocatalysis, where the ‘core effects’ were employed to explain the superior catalytic activity. However, it is insufficient to understand the essential mechanism or further guide the design of electrocatalysts. Structure–property relationship should be disclosed for the catalysis on HEA-NPs to accelerate the process of seeking high effective and efficient electrocatalysts. Therefore, we summarized the recent advances of noble-metal-based HEA-NPs applied to electrocatalysis, such as hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, methanol oxidation reaction, ethanol oxidation reaction, formic acid oxidation reaction, hydrogen oxidation reaction, carbon dioxide reduction reaction and nitrogen reduction reaction. For each electrocatalytic reaction, the reaction mechanism and catalyst structure were presented, and then the structure–property relationship was elaborated. The review begins with the development, concept, four ‘core effect’ and synthesis methods of HEAs. Next,the electrocatalytic reactions on noble-metal-based HEA-NPs are summarized and discussed independently. Lastly, the main views and difficulties pertaining to structure–property relationship for HEAs are discussed.展开更多
Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials a...Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.展开更多
New descriptors were constructed and structures of some oxygen-containing organic compounds were parameterized. The multiple linear regression（MLR） and partial least squares regression（PLS） methods were employed t...New descriptors were constructed and structures of some oxygen-containing organic compounds were parameterized. The multiple linear regression（MLR） and partial least squares regression（PLS） methods were employed to build two relationship models between the structures and octanol/water partition coefficients（LogP） of the compounds. The modeling correlation coefficients（R） were 0.976 and 0.922, and the ＂leave one out＂ cross validation correlation coefficients（R（CV）） were 0.973 and 0.909, respectively. The results showed that the structural descriptors could well characterize the molecular structures of the compounds; the stability and predictive power of the models were good.展开更多
The two-dimensional (2D) structure of layered transition metal dichalcogenides (TMDs) provides unusual physical properties [1,2]and chemical reactivity [3,4], which can be influenced by defects such as dislocations [5...The two-dimensional (2D) structure of layered transition metal dichalcogenides (TMDs) provides unusual physical properties [1,2]and chemical reactivity [3,4], which can be influenced by defects such as dislocations [5,6]. For example, dislocations can act as nucleation sites for the onset of deformation when subjected to stress [7].展开更多
The new topological indices A x1 A x3 suggested in our laboratories were applied to the study of structure property relationships between color reagents and their color reactions with yttrium. The ...The new topological indices A x1 A x3 suggested in our laboratories were applied to the study of structure property relationships between color reagents and their color reactions with yttrium. The topological indices of twenty asymmetrical phosphone bisazo derivatives of chromotropic acid were calculated. The work shows that QSPR can be used as a novel aid to predict the molar absorptivities of color reactions and in the long term to be helpful tool in color reagent design. Multiple regression analysis and neural network were employed simultaneously in this study. The results demonstrated the feasibility and the effectiveness of the method.展开更多
Revealing structural isomerization in metal clusters would bridge a huge structural gap between small molecular isomerization and solid–solid phase transformation.However,genuine structural isomerism in metal cluster...Revealing structural isomerization in metal clusters would bridge a huge structural gap between small molecular isomerization and solid–solid phase transformation.However,genuine structural isomerism in metal clusters is still rare.In this work,we report the first example of structural isomerismin Cu clusters.By utilizing the coordination flexibility of alkyne to enable the migration of partial Cu atoms in Cu metal cores,two Cu_(15)cluster complexes(Cu_(15)-a and Cu_(15)-c)possessing identical composition but different metal core structures have been successfully isolated.Interestingly,although the structure of Cu_(15)-a can be retained in CH_(2)C_(l2)solution below 27°C,it will gradually change to give an intermediate state,Cu_(15)-b,as the temperature rises(at about 31°C)before it eventually transforms into Cu_(15)-c(at 40∼65°C).Significantly,atomically precise Cu_(15)-b clearly provides footprints for tracing the thermal migration process of Cu atoms during the thermal transformation from Cu_(15)-a to Cu_(15)-c.In addition,Cu_(15)-a and Cu_(15)-c exhibit diverse crystallization-induced emission enhancement phenomena.Crystalline Cu_(15)-c displays redshifted photoluminescence(820 nm)compared with Cu_(15)-a(726 nm)due to the shorter mean Cu···Cu distance in Cu_(15)-c.Notwithstanding,crystalline Cu_(15)-a exhibits much more intense photoluminescence at room temperature than that in Cu_(15)-c,which might be attributed to the stronger intermolecular C–H⋯πinteractions in Cu_(15)-a.These results indicate that cluster isomerism provides valuable opportunities for insight into the structure–property relationships and understanding the complex evolution of phase transformation in nanometallic solids.展开更多
Covalent organic frameworks have emerged as a hot spot in the field of photocatalysis due to their excellent structural tunability,high specific surface area,high porosity,and good chemical stability.Specifically,they...Covalent organic frameworks have emerged as a hot spot in the field of photocatalysis due to their excellent structural tunability,high specific surface area,high porosity,and good chemical stability.Specifically,they exhibit distinctive optoelectronic features by integrating different molecular building blocks with appropriate links,constructing an π-conjugated system,or introducing electron donor–acceptor units into the conjugated framework.The reasonably adjusted band structure yields excellent photocatalytic activity of covalent organic framework materials.展开更多
A new paradigm driven by artificial intelligence(AI)and machine learning(ML)is significantly accelerating the iterative pace of polymer materials research.Traditional experimental approaches to polymer discovery have ...A new paradigm driven by artificial intelligence(AI)and machine learning(ML)is significantly accelerating the iterative pace of polymer materials research.Traditional experimental approaches to polymer discovery have long relied on trial and error,requiring extensive time and resources while offering limited access to the vast chemical design space.In contrast,ML-assisted strategies provide a transformative framework for efficiently navigating this complex landscape.This paper focuses specifically on polymer design at the molecular level.By integrating data-driven methodologies,researchers can extract structure−property relationships,predict polymer properties,and optimize molecular architectures with unprecedented speed.ML-driven polymer design follows a structured approach:(1)database construction,(2)structural representation and feature engineering,(3)development of ML-based property prediction models,(4)virtual screening of potential candidates,and(5)validation through experiments and/or numerical simulations.This workflow faces two central challenges.First is the limited availability of high-quality polymer datasets,particularly for advanced materials with specialized properties.Second is the generation of virtual polymer structures.Unlike small-molecule drug discovery,where vast libraries of candidate compounds exist,polymer chemistry lacks an equivalent repository of hypothetical structures.Recent efforts have leveraged rule-based polymerization reactions and generative models to create large-scale databases of hypothetical polymers,significantly expanding the design space.Additionally,the diversity of polymer structures,the broad range of their properties,and the limited availability of training samples add complexity to developing accurate predictive models.Addressing these challenges requires innovative ML techniques,such as transfer learning,multitask learning,and generative models,to extract meaningful insights from sparse data and improve prediction reliability.This data-driven approach has enabled the discovery of novel,high-performance polymers for applications in aerospace,electronics,energy storage,and biomedical engineering.Despite these advancements,several hurdles remain.The interpretability of ML models,particularly deep neural networks,is a pressing concern.While black-box models can achieve remarkable predictive accuracy,understanding their decision-making processes remains challenging.Explainable AI methods are increasingly being explored to provide insights into feature importance,model uncertainty,and the underlying chemistry driving polymer properties.Additionally,the synthesizability and processability of ML-generated candidates must be carefully considered to ensure practical experimental validation and real-world application.In this paper,we review recent progress in ML-assisted molecular design of polymer materials,focusing on database development,feature representation,predictive modeling,and virtual polymer generation.We highlight emerging methodologies,including transformer-based language models,physics-informed neural networks,and closed-loop discovery frameworks,which collectively enhance the efficiency and accuracy of polymer informatics.Finally,we discuss the future outlook of ML-driven polymer research,emphasizing the need for collaborative efforts between data scientists,chemists,and engineers to refine predictive models,integrate experimental validation,and accelerate the development of next-generation polymeric materials.By leveraging the synergy between computational modeling and experimental insights,ML-assisted design is poised to revolutionize polymer discovery,enabling the rapid development of sustainable,high-performance materials tailored for diverse applications.展开更多
Machine learning(ML),material genome,and big data approaches are highly overlapped in their strategies,algorithms,and models.They can target various definitions,distributions,and correlations of concerned physical par...Machine learning(ML),material genome,and big data approaches are highly overlapped in their strategies,algorithms,and models.They can target various definitions,distributions,and correlations of concerned physical parameters in given polymer systems,and have expanding applications as a new paradigm indispensable to conventional ones.Their inherent advantages in building quantitative multivariate correlations have largely enhanced the capability of scientific understanding and discoveries,thus facilitating mechanism exploration,target prediction,high-throughput screening,optimization,and rational and inverse designs.This article summarizes representative progress in the recent two decades focusing on the design,preparation,application,and sustainable development of polymer materials based on the exploration of key physical parameters in the composition-process-structure-property-performance relationship.The integration of both data-driven and physical insights through ML approaches to deepen fundamental understanding and discover novel polymer materials is categorically presented.Despite the construction and application of robust ML models,strategies and algorithms to deal with variant tasks in polymer science are still in rapid growth.The challenges and prospects are then presented.We believe that the innovation in polymer materials will thrive along the development of ML approaches,from efficient design to sustainable applications.展开更多
Zirconium-based metal-organic frameworks(Zr-MOFs)have been explored for applications including but not limited to water adsorption,gas storage and separation,heterogeneous catalysis,and chemical sensing.Zr-MOFs serve ...Zirconium-based metal-organic frameworks(Zr-MOFs)have been explored for applications including but not limited to water adsorption,gas storage and separation,heterogeneous catalysis,and chemical sensing.Zr-MOFs serve as a major class of functional MOFs thanks to their high thermal,chemical and hydrolytic stability,large surface area,and tunable structures with the versatile connectivity.In this work,we highlight the design and synthesis of zirconium-based MOFs as well as their applications.Specifically,we demonstrate how reticular chemistry can direct the rational design and synthesis of functional ZrMOFs and describe their structure–property relationship.In addition,we feature synthetic strategies,including isoreticular expansion,linker functionalization,node functionalization,and defect engineering,as toolkits to construct tailored material for specific applications.展开更多
Covalent organic frameworks(COFs)have emerged as promising electrode materials for rechargeable metal-ion batteries and have gained much attention in recent years due to their high specific surface area,inherent poros...Covalent organic frameworks(COFs)have emerged as promising electrode materials for rechargeable metal-ion batteries and have gained much attention in recent years due to their high specific surface area,inherent porosity,tunable molecular structure,robust framework,abundant active sites.Moreover,compared with inorganic materials and small organic molecules,COFs have the advantages of multi-electron transfer,short pathways,high cycling stability.Although great progress on COF-based electrodes has been made,the corresponding electrochemical performance is still far from satisfactory for practical applications.In this review,we first summarize the fundamental background of COFs,including the species of COFs(different active covalent bonds)and typical synthesis methods of COFs.Then,the key challenges and the latest research progress of COF-based cathodes and anodes for metal-ion batteries are reviewed,including Li-ion batteries,Na-ion batteries,K-ion batteries,Zn-ion batteries,et al.Moreover,the effective strategies to enhance electrochemical performance of COF-based electrodes are presented.Finally,this review also covers the typical superiorities of COFs used in energy devices,as well as providing some perspectives and outlooks in this field.We hope this review can provide fundamental guidance for the development of COFbased electrodes for metal-ion batteries in the further research.展开更多
基金supported by the Research Program of Application Foundation and Advanced Technology from the Tianjin Municipal Science and Technology Committee(No.11JCZDJ24600)the Natural Science Foundationof China(No.20773093)
文摘The amidoximated polyacrylonitrile （PAN） fiber Fe complexeswere prepared and used as the heterogeneous Fenton catalysts for thedegradation of28 anionicwater soluble azodyes inwater under visible irradiation. The multiple linear regression （MLR） methodwas employed todevelop the quantitative structure property relationship （QSPR） model equations for thedecoloration and mineralization of azodyes. Moreover, the predictive ability of the QSPR model equationswas assessed using Leave-one-out （LOO） and cross-validation （CV） methods. Additionally, the effect of Fe content of catalyst and the sodium chloride inwater on QSPR model equationswere also investigated. The results indicated that the heterogeneous photo-Fentondegradation of the azodyeswithdifferent structureswas conducted in the presence of the amidoximated PAN fiber Fe complex. The QSPR model equations for thedyedecoloration and mineralizationwere successfullydeveloped using MLR technique. MW/S （molecularweightdivided by the number of sulphonate groups） and N N=N （the number of azo linkage） are considered as the most importantdetermining factor for thedyedegradation and mineralization, and there is a significant negative correlation between MW/S or N N=N anddegradation percentage or total organic carbon （TOC） removal. Moreover, LOO and CV analysis suggested that the obtained QSPR model equations have the better prediction ability. The variation in Fe content of catalyst and the addition of sodium chloridedid not alter the nature of the QSPR model equations.
文摘Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices （Х）, modified molecular connectivity indices （ ^mХ^v ） and valance molecular connectivity indices （ ^mХ^v ）, with ^mХ^v calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression （MLR） method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.
基金the National Natural Science Foundation of China (Nos.22375059, 22005133, 51922039 and52273174)Shenzhen Science and Technology Program (No.RCJC20200714114434015)+1 种基金Science and Technology Innovation Program of Hunan Province (No.2020RC5033)National Key Research and Development Program of China (No.2020YFC1807302) for financial support。
文摘While heteroatom doping serves as a powerful strategy for devising novel polycyclic aromatic hydrocarbons(PAHs), the further fine-tuning of optoelectronic properties via the precisely altering of doping patterns remains a challenge. Herein, by changing the doping positions of heteroatoms in a diindenopyrene skeleton, we report two isomeric boron, sulfur-embedded PAHs, named Anti-B_(2)S_(2) and Syn-B_(2)S_(2), as electron transporting semiconductors. Detailed structure-property relationship studies revealed that the varied heteroatom positions not only change their physicochemical properties, but also largely affect their solid-state packing modes and Lewis base-triggered photophysical responses. With their low-lying frontier molecular orbital levels, n-type characteristics with electron mobilities up to 1.5 × 10^(-3)cm^(2)V^(-1)s^(-1)were achieved in solution-processed organic field-effect transistors. Our work revealed the critical role of controlling heteroatom doping patterns for designing advanced PAHs.
基金the financial support from the National Natural Science Foundation of China(Nos.51803111,31670596 and 11904220)the Natural Science Foundation of Shaanxi province(Nos.2019JQ-786 and 2020GY-232).
文摘A series of hydroxyl-terminated polyethylenes(HTPE)bearing various functional side groups(e.g.carboxyl,ester and butane groups)were synthesized by the combination of ring opening metathesis polymerization(ROMP)and visible light photocatalytic thiol-ene reaction.The products are named as a,w-dihydroxyl-polyllpropionyloxythio)methinetrimethylene](HTPECarboxy),a,w dihydroxy-poly(methylpropionatethio)methinetrimethylene](HTPEeser)and a,wdihydroxyl-poly[(butylthio)methinetrimethylene](HTPEbutane)respectively.The investigation of ROMP indicated that the molecular weight of resultant hydroxy-terminated polybutadiene(HTPB)can be tailored by varying the feed ratios of monomer to chain transfer agent(CTA).The exploration of the photocatalytic thiol-ene reaction between HTPB precursor and methyl-3-mercaptopropionate revealed that blue light as well as oxygen accelerated the reaction.1H-NMR and 13C-NMR results verified all the double bonds in HTPB can be modified,and the main chain of resultant polymer can be considered as polyethylene.Subsequently,relationship between the structure of side groups and the thermal properties of functional PEs was studied.And the results suggested that the Tg was in the order of HTPEbuane<HTPEester<HTPEarboxy+.Greater interaction between side groups resulted in higher Tg.Moreover,all the functional PE samples exhibited poor thermostability as compared to HTPB.Finally,the promising applications for functional PEs were explored.HTPEcarboxy1 can be utilized as a smart material with pH-responsive properties due to its pH-dependent ionization of carboxyl side groups.HTPEbutane can be employed as a macro-initiator for building the triblock copolymer due to the presence of active hydroxyl end groups.HTPEester can serve as a plasticizer for PVC which can enhance the ductilityt of PVC without obviously sacrificing strength.
文摘Materials and chemical scientists have tirelessly pursued the vision of creating atomically tailored materials.The promise of atomic precision in material synthesis lies in the potential to precisely control every aspect of a material's structure,thereby opening up opportunities for discovering and tuning novel physical properties[1].However,achieving atomically precise assemblies in practice remains a formidable challenge,largely due to the difficulty of controlling nucleation and growth processes at the most fundamental scale.
基金TheNationalKeyBasicResearchFoundationofChina (No .G1 9990 4 571 1 )
文摘The theoretical linear solvation energy relationship(TLSER) approach was adopted to predict the aqueous solubility and n -octanol/water partition coefficient of three groups of environmentally important chemicals-polychlorinated biphenyls(PCBs), polychlorinated dibenzodioxins and dibenzofurans(PCDDs and PCDFs). For each compound, five quantum parameters were calculated using AM1 semiempirical molecular orbital methods and used as structure descriptors: average molecular polarizability(α), energy of the lowest unoccupied molecular orbit( E _ LUMO ), energy of the highest occupied molecular orbit( E _ HOMO ), the most positive charge on a hydrogen atom( q _+), and the most negative atomic partial charge( q _-) in the solute molecule. Then standard independent variables in TLSER equation was extracted and two series of quantitative equations between these quantum parameters and aqueous solubility and n -octanol/water partition coefficient were obtained by stepwise multiple linear regression(MLR) method. The developed equations have both quite high accuracy and explicit meanings. And the cross-validation test illustrated the good predictive power and stability of the established models. The results showed that TLSER could be used as a promising approach in the estimation of partition and solubility properties of macromolecular chemicals, such as persistent organic pollutants.
基金supported by the Natural Scientific Foundation of China (21825501)National Key Research and Development Program (2016YFA0202500 and 2016YFA0200102)+1 种基金Australian Research Council (DP160103107, FT170100224)Tsinghua University Initiative Scientific Research Program。
文摘The demand for efficient and environmentally-benign electrocatalysts that help availably harness the renewable energy resources is growing rapidly. In recent years, increasing insights into the design of water electrolysers, fuel cells, and metal–air batteries emerge in response to the need for developing sustainable energy carriers, in which the oxygen evolution reaction and the oxygen reduction reaction play key roles. However, both reactions suffer from sluggish kinetics that restricts the reactivity. Therefore, it is vital to probe into the structure of the catalysts to exploit high-performance bifunctional oxygen electrocatalysts. Spinel-type catalysts are a class of materials with advantages of versatility, low toxicity, low expense, high abundance, flexible ion arrangement, and multivalence structure. In this review, we afford a basic overview of spinel-type materials and then introduce the relevant theoretical principles for electrocatalytic activity, following that we shed light on the structure–property relationship strategies for spinel-type catalysts including electronic structure, microstructure, phase and composition regulation,and coupling with electrically conductive supports. We elaborate the relationship between structure and property, in order to provide some insights into the design of spinel-type bifunctional oxygen electrocatalysts.
基金supported by the National Natural Science Foundation of China (21676100, 22008076)the Guangdong Natural Science Foundation (2017A030312005)。
文摘Since the two seminal papers were published independently in 2004, high-entropy-alloys(HEAs) have been applied to structural and functional materials due to the enhanced mechanical properties, thermal stability, and electrical conductivity. In recent years, HEA nanoparticles(HEA-NPs) were paid much attention to in the field of catalysis for the promoted catalytic activity. Furthermore, the various ratios among the metal components and tunable bulk and surface structures enable HEAs have big room to enhance the catalytic performance. Especially, noble-metal-based HEAs displayed significantly improved performance in electrocatalysis, where the ‘core effects’ were employed to explain the superior catalytic activity. However, it is insufficient to understand the essential mechanism or further guide the design of electrocatalysts. Structure–property relationship should be disclosed for the catalysis on HEA-NPs to accelerate the process of seeking high effective and efficient electrocatalysts. Therefore, we summarized the recent advances of noble-metal-based HEA-NPs applied to electrocatalysis, such as hydrogen evolution reaction, oxygen evolution reaction, oxygen reduction reaction, methanol oxidation reaction, ethanol oxidation reaction, formic acid oxidation reaction, hydrogen oxidation reaction, carbon dioxide reduction reaction and nitrogen reduction reaction. For each electrocatalytic reaction, the reaction mechanism and catalyst structure were presented, and then the structure–property relationship was elaborated. The review begins with the development, concept, four ‘core effect’ and synthesis methods of HEAs. Next,the electrocatalytic reactions on noble-metal-based HEA-NPs are summarized and discussed independently. Lastly, the main views and difficulties pertaining to structure–property relationship for HEAs are discussed.
基金Project support by the National Natural Science Foundation of China(Grant Nos.11674237 and 51602211)the National Key Research and Development Program of China(Grant No.2016YFB0700700)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD),ChinaChina Post-doctoral Foundation(Grant No.7131705619).
文摘Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.
基金supported by the Youth Foundation of Education Bureau,Sichuan Province(13ZB0003)
文摘New descriptors were constructed and structures of some oxygen-containing organic compounds were parameterized. The multiple linear regression（MLR） and partial least squares regression（PLS） methods were employed to build two relationship models between the structures and octanol/water partition coefficients（LogP） of the compounds. The modeling correlation coefficients（R） were 0.976 and 0.922, and the ＂leave one out＂ cross validation correlation coefficients（R（CV）） were 0.973 and 0.909, respectively. The results showed that the structural descriptors could well characterize the molecular structures of the compounds; the stability and predictive power of the models were good.
基金supported by the National Key R&D Program of China[Nos.2018YFB1304902,2016YFA0300804,2016YFA0300903]the National Natural Science Foundation of China[Nos.51672007,11974023,11904372,11704389,U1813211]+3 种基金the Key-Area Research and Development Program of Guang Dong Province[Nos.2018B030327001,2018B010109009]the‘‘2011 Program”Peking-Tsinghua-IOP Collaborative Innovation Center of Quantum Matterthe Beijing Institute of Technology Research Fund Program for Young Scholarsthe Beijing Institute of Technology laboratory research project[No.2019BITSYA03]。
文摘The two-dimensional (2D) structure of layered transition metal dichalcogenides (TMDs) provides unusual physical properties [1,2]and chemical reactivity [3,4], which can be influenced by defects such as dislocations [5,6]. For example, dislocations can act as nucleation sites for the onset of deformation when subjected to stress [7].
文摘The new topological indices A x1 A x3 suggested in our laboratories were applied to the study of structure property relationships between color reagents and their color reactions with yttrium. The topological indices of twenty asymmetrical phosphone bisazo derivatives of chromotropic acid were calculated. The work shows that QSPR can be used as a novel aid to predict the molar absorptivities of color reactions and in the long term to be helpful tool in color reagent design. Multiple regression analysis and neural network were employed simultaneously in this study. The results demonstrated the feasibility and the effectiveness of the method.
基金supported by the National Natural Science Foundation of China(grant nos.22101048,22005054,and 21975044)the Natural Science Foundation of Fujian Province(grant no.2021J01150).
文摘Revealing structural isomerization in metal clusters would bridge a huge structural gap between small molecular isomerization and solid–solid phase transformation.However,genuine structural isomerism in metal clusters is still rare.In this work,we report the first example of structural isomerismin Cu clusters.By utilizing the coordination flexibility of alkyne to enable the migration of partial Cu atoms in Cu metal cores,two Cu_(15)cluster complexes(Cu_(15)-a and Cu_(15)-c)possessing identical composition but different metal core structures have been successfully isolated.Interestingly,although the structure of Cu_(15)-a can be retained in CH_(2)C_(l2)solution below 27°C,it will gradually change to give an intermediate state,Cu_(15)-b,as the temperature rises(at about 31°C)before it eventually transforms into Cu_(15)-c(at 40∼65°C).Significantly,atomically precise Cu_(15)-b clearly provides footprints for tracing the thermal migration process of Cu atoms during the thermal transformation from Cu_(15)-a to Cu_(15)-c.In addition,Cu_(15)-a and Cu_(15)-c exhibit diverse crystallization-induced emission enhancement phenomena.Crystalline Cu_(15)-c displays redshifted photoluminescence(820 nm)compared with Cu_(15)-a(726 nm)due to the shorter mean Cu···Cu distance in Cu_(15)-c.Notwithstanding,crystalline Cu_(15)-a exhibits much more intense photoluminescence at room temperature than that in Cu_(15)-c,which might be attributed to the stronger intermolecular C–H⋯πinteractions in Cu_(15)-a.These results indicate that cluster isomerism provides valuable opportunities for insight into the structure–property relationships and understanding the complex evolution of phase transformation in nanometallic solids.
基金financial support from National Natural Science Foundation of China(52272287,22268003)Yunnan Fundamental Research Projects(202305AF150116,202301AT070027)。
文摘Covalent organic frameworks have emerged as a hot spot in the field of photocatalysis due to their excellent structural tunability,high specific surface area,high porosity,and good chemical stability.Specifically,they exhibit distinctive optoelectronic features by integrating different molecular building blocks with appropriate links,constructing an π-conjugated system,or introducing electron donor–acceptor units into the conjugated framework.The reasonably adjusted band structure yields excellent photocatalytic activity of covalent organic framework materials.
基金support from the Air Force Office of Scientific Research through the Air Force’s Young Investigator Research Program(FA9550-20-1-0183,Program Manager:Dr.Ming-Jen Pan and Capt.Derek Barbee)Air Force Research Laboratory/UES Inc.(FA8650-20-S-5008,PICASSO program)the National Science Foundation(CMMI-2332276,CMMI-2316200,and CAREER-2323108).
文摘A new paradigm driven by artificial intelligence(AI)and machine learning(ML)is significantly accelerating the iterative pace of polymer materials research.Traditional experimental approaches to polymer discovery have long relied on trial and error,requiring extensive time and resources while offering limited access to the vast chemical design space.In contrast,ML-assisted strategies provide a transformative framework for efficiently navigating this complex landscape.This paper focuses specifically on polymer design at the molecular level.By integrating data-driven methodologies,researchers can extract structure−property relationships,predict polymer properties,and optimize molecular architectures with unprecedented speed.ML-driven polymer design follows a structured approach:(1)database construction,(2)structural representation and feature engineering,(3)development of ML-based property prediction models,(4)virtual screening of potential candidates,and(5)validation through experiments and/or numerical simulations.This workflow faces two central challenges.First is the limited availability of high-quality polymer datasets,particularly for advanced materials with specialized properties.Second is the generation of virtual polymer structures.Unlike small-molecule drug discovery,where vast libraries of candidate compounds exist,polymer chemistry lacks an equivalent repository of hypothetical structures.Recent efforts have leveraged rule-based polymerization reactions and generative models to create large-scale databases of hypothetical polymers,significantly expanding the design space.Additionally,the diversity of polymer structures,the broad range of their properties,and the limited availability of training samples add complexity to developing accurate predictive models.Addressing these challenges requires innovative ML techniques,such as transfer learning,multitask learning,and generative models,to extract meaningful insights from sparse data and improve prediction reliability.This data-driven approach has enabled the discovery of novel,high-performance polymers for applications in aerospace,electronics,energy storage,and biomedical engineering.Despite these advancements,several hurdles remain.The interpretability of ML models,particularly deep neural networks,is a pressing concern.While black-box models can achieve remarkable predictive accuracy,understanding their decision-making processes remains challenging.Explainable AI methods are increasingly being explored to provide insights into feature importance,model uncertainty,and the underlying chemistry driving polymer properties.Additionally,the synthesizability and processability of ML-generated candidates must be carefully considered to ensure practical experimental validation and real-world application.In this paper,we review recent progress in ML-assisted molecular design of polymer materials,focusing on database development,feature representation,predictive modeling,and virtual polymer generation.We highlight emerging methodologies,including transformer-based language models,physics-informed neural networks,and closed-loop discovery frameworks,which collectively enhance the efficiency and accuracy of polymer informatics.Finally,we discuss the future outlook of ML-driven polymer research,emphasizing the need for collaborative efforts between data scientists,chemists,and engineers to refine predictive models,integrate experimental validation,and accelerate the development of next-generation polymeric materials.By leveraging the synergy between computational modeling and experimental insights,ML-assisted design is poised to revolutionize polymer discovery,enabling the rapid development of sustainable,high-performance materials tailored for diverse applications.
基金supported by the National Natural Science Foundation of China(Nos.22173094,52303121,and 52293471)National Key Research and Development Program of China(Nos.2022YFB3707303 and 2021YFB3801500)+2 种基金Major Science and Technology Projects for Independent Innovation of China FAW Group Co.Ltd.(No.20220301018GX)Guizhou Provincial Basic Research Program(Nos.BQW[2024]006 and Z2024021)Guizhou University Talents Fund(No.C0048072).
文摘Machine learning(ML),material genome,and big data approaches are highly overlapped in their strategies,algorithms,and models.They can target various definitions,distributions,and correlations of concerned physical parameters in given polymer systems,and have expanding applications as a new paradigm indispensable to conventional ones.Their inherent advantages in building quantitative multivariate correlations have largely enhanced the capability of scientific understanding and discoveries,thus facilitating mechanism exploration,target prediction,high-throughput screening,optimization,and rational and inverse designs.This article summarizes representative progress in the recent two decades focusing on the design,preparation,application,and sustainable development of polymer materials based on the exploration of key physical parameters in the composition-process-structure-property-performance relationship.The integration of both data-driven and physical insights through ML approaches to deepen fundamental understanding and discover novel polymer materials is categorically presented.Despite the construction and application of robust ML models,strategies and algorithms to deal with variant tasks in polymer science are still in rapid growth.The challenges and prospects are then presented.We believe that the innovation in polymer materials will thrive along the development of ML approaches,from efficient design to sustainable applications.
基金support by the National Natural Science Foundation of China (22201247)the startup funding from Zhejiang University。
文摘Zirconium-based metal-organic frameworks(Zr-MOFs)have been explored for applications including but not limited to water adsorption,gas storage and separation,heterogeneous catalysis,and chemical sensing.Zr-MOFs serve as a major class of functional MOFs thanks to their high thermal,chemical and hydrolytic stability,large surface area,and tunable structures with the versatile connectivity.In this work,we highlight the design and synthesis of zirconium-based MOFs as well as their applications.Specifically,we demonstrate how reticular chemistry can direct the rational design and synthesis of functional ZrMOFs and describe their structure–property relationship.In addition,we feature synthetic strategies,including isoreticular expansion,linker functionalization,node functionalization,and defect engineering,as toolkits to construct tailored material for specific applications.
基金financially supported by the National Natural Science Foundation of China(21827813,21921001,22175172,22075283,92161125,and U21A20508)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2020303 and 2021300)。
基金the National Natural Science Foundation of China(No.51872186)Project funded by China Postdoctoral Science Foundation(No.2021M702316)Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110999).
文摘Covalent organic frameworks(COFs)have emerged as promising electrode materials for rechargeable metal-ion batteries and have gained much attention in recent years due to their high specific surface area,inherent porosity,tunable molecular structure,robust framework,abundant active sites.Moreover,compared with inorganic materials and small organic molecules,COFs have the advantages of multi-electron transfer,short pathways,high cycling stability.Although great progress on COF-based electrodes has been made,the corresponding electrochemical performance is still far from satisfactory for practical applications.In this review,we first summarize the fundamental background of COFs,including the species of COFs(different active covalent bonds)and typical synthesis methods of COFs.Then,the key challenges and the latest research progress of COF-based cathodes and anodes for metal-ion batteries are reviewed,including Li-ion batteries,Na-ion batteries,K-ion batteries,Zn-ion batteries,et al.Moreover,the effective strategies to enhance electrochemical performance of COF-based electrodes are presented.Finally,this review also covers the typical superiorities of COFs used in energy devices,as well as providing some perspectives and outlooks in this field.We hope this review can provide fundamental guidance for the development of COFbased electrodes for metal-ion batteries in the further research.
