基于科教融合的综合化学实验教学设计:银纳米材料的制备及催化性能

开展了研究型综合化学实验教学,将基础教学与科研训练有机结合,以和谐共进的方式加强人才培养效果,有助于推进教育教学改革进程。本实验利用简单的无机化学反应制备银纳米粒子,通过添加不同溴/银比例可调控银纳米胶体溶液颜色,将表面等离子体共振特性引入实验教学,提高了实验的趣味性。实验利用经典的对硝基苯酚催化反应模型验证银纳米粒子的化学活性,结果表明银纳米粒子催化效果良好,可近似为一级反应动力学过程。利用实验室常见的玻璃仪器搭建简易污染物降解反应器,使用自制载银滤纸实现了10次以上的循环降解,降解率可达90%。

Judicious training pattern for superior molecular reorganization energy prediction model

Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.

The Optimization of Ultrasonic Extraction Process of Total Flavonoids from Rhodiola Crenulata by Response Surface Method

[Objectives]To study the optimal extraction process of total flavonoids in Rhodiola rosea L.,and further facilitate the development and utilization of Rhodiola rosea L. [Methods]With Rhodiola crenulata as raw material,ethanol as extractant,ultrasonic extraction as the extraction method,the single factor method was first used for preliminary investigation of effect of ethanol volume fraction,solid-liquid ratio,extraction temperature and extraction time on the flavonoids extraction rate,and then the Box-Behnken response surface method was used to optimize the extraction process of total flavonoids in R. crenulata. [Results] The optimal extraction conditions: ethanol concentration of 72%;solid-liquid ratio of 1∶ 43; extraction temperature of 66℃; extraction time of 50 min. Under these conditions,the extraction rate of total flavonoids from R. crenulata was 2. 591%. [Conclusions] The results showed that the method of response surface was reasonable and feasible for the optimization of ultrasonic extraction of total flavonoids from R. crenulata.

π-Conjugated nitroxide radicals for resistive switching devices

Organic molecules have been deeply explored for logic electronic circuits to satisfy the explosive growth of data in modern society,due to their low cost,flexibly tuned structures,solution processability,high-density and fast data storage[1,2].Currently,various organic materials,such as donor-acceptor complexes,organic-inorganic hybrids,biomacromolecules and radicals with unpaired electron,have been developed for memory.Among these materials,radical species have aroused particular attention because they are very sensitive to electron transfer reactions,that is they can be easily,rapidly and reversibly transformed between radical and ion states having different conducting ability[3-5].

表面自由能对二维结晶的影响

二维分子晶体是一类具有优异光电性能的新兴材料.然而,由于人们对决定二维结晶的因素知之甚少,二维分子晶体的生长极具挑战.我们研究了有机半导体的二维结晶和表面自由能之间的关系,提出了利用表面自由能各向异性因子来衡量二维结晶趋势的设想.以两个BTBT衍生物为例,我们发现具有大的g因子的分子有不受外界条件干扰的二维结晶趋势.这项工作为合理设计可二维结晶的有机半导体提供了指导.

Cocrystal engineering:towards high-performance near-infrared organic phototransistors based on donor-acceptor charge transfer cocrystals

Near-infrared organic phototransistors have wide application prospects in many fields.The active materials with the high mobility and near-infrared response are critical to building high-performance near-infrared organic phototransistors,which are scarce at present.Herein,a new charge transfer cocrystal using 5,7-dihydroindolo[2,3-b]carbazole(5,7-ICZ)as the donor and 2,2’-(benzo[1,2-b:4,5-b’]dithiophene-4,8-diylidene)dimalononitrile(DTTCNQ)as the acceptor is properly designed and prepared in a stoichiometric ratio(D:A=1:1),which not only displays a high electron mobility of 0.15 cm^(2)V^(-1)s^(-1) and very low dark current,but also can serve as the active layer materials in the region of near-infrared detection due to the narrowed band gap and good charge transport properties.A high photosensitivity of 1.8×10^(4),the ultrahigh photoresponsivity of 2,923 A W-1and the high detectivity of 4.26×10^(11)Jones of the organic near-infrared phototransistors are obtained.

Oxygen doping modulating thermal-activated charge transport of reduced graphene oxide for high performance temperature sensors

Graphene and its derivatives have sparked intense research interest in wearable temperature sensing due to their excellent electric properties,mechanical flexibility,and good biocompatibility.Despite these ad-vantages,the weak temperature dependence of charge transport makes them difficult to achieve a highly sensitive temperature response,which is one of the remaining bottlenecks in the progress towards practi-cal applications.Unfortunately,detailed knowledge about the key factors of the charge transport temper-ature dependence in this material that determines the critical performance of electrical sensors is very limited up to now.Here,we reveal that oxygen absorption on the ultrathin reduced graphene oxide(RGO)films(~3 nm)can significantly increase their conductance activation energy over 200%and thus greatly improve the temperature dependence of thermal-activated charge transport.Further investigations sug-gest that oxygen introduces the deep acceptor states,distributed at an energy level~0.175ev from the valence-band maximum,which allows a highly temperature-dependent impurity ionization process and the resulting vast holes release in a wide temperature range.Remarkably,our temperature sensors based on oxygen-doped ultrathin RGO films show a high sensitivity with temperature conductive coefficient of 14.58%K^(-1),which is one order of magnitude higher than the reported CNT or graphene-based devices.Moreover,the ultrathin thickness and high thermal conductivity of RGo film allow an ultrafast response time of~86ms,which represents the best level of temperature sensors based on soft materials.Profit-ing from these advantages,our sensors show good capacity to identify the slight temperature difference of human body,monitor respiratory rate,and detect the environmental temperature.This work not only represents substantial performance advances in temperature sensing,but also provides a new approach to modulate the charge transport temperature dependence,which could be benefited to both device design and fundamental research.

MXene-MoS_(2)nanocomposites via chemical vapor deposition with enhanced electrocatalytic activity for hydrogen evolution

As a new paradigm of material science,two-dimensional(2D)heterostructured composites have attracted extensive interests because of combining the collective advantages and collaborative characteristics of individual building blocks.Molybdenum disulfide(MoS_(2))has demonstrated great promise as a low-cost substitute to platinum-based catalysts for electrochemical hydrogen production.However,the broad adoption of MoS_(2)is hindered by its limited number of active sites and low inherent electrical conductivity.One of the promising methods to further activate MoS_(2)is coupling engineering.Here,we demonstrate for the first time the synthesis of 2D MXene-MoS_(2)nanocomposites through chemical vapor deposition(CVD)approach,thus leading to precise design in structure type and orientation.The computational results show that nanocomposites have metallic properties.Owing to their unique 2D/2D structure,MXene-MoS_(2)nanocomposites exhibit more active catalytic sites,resulting in higher electrochemical performance,as inherited from parent excellent characteristics,and a much lower overpotential of~69 mV at a current density of 10 mA·cm^(-2) is achieved.This work paves the way to employ CVD method by coupling engineering to construct 2D nanocomposites for energy storage applications.

Perspective on machine learning in energy material discovery

To realize the sustainable development of society,advanced materials for energy storage and conversion are urgently needed.For a long time,the development of new materials relies heavily on tedious trial and error experiments,which have long cycles and high costs,far from modern requirements for advanced materials.With the rapid development of supercomputers and the wide application of density functional theory,high-precision first principle theoretical calculation has been widely used in the process of material design.

Deep insight into the charge transfer interactions in 1,2,4,5-tetracyanobenzene-phenazine cocrystal

A new charge transfer cocrystal of 1,2,4,5-tetracyanobenzene(TCNB)-phenazine(PTC)was prepared by solvent evaporation method.The donor and acceptor molecules of cocrystal are stacked face to face with a mixed-stacking,implying a strong charge transfer(CT)interactions in the cocrystal system.The spectroscopic studies,single-crystal X-ray diffraction structure,density functional theory(DFT)and Hirschfield surfaces calculations are carried out to explore the relationship between structure and properties of cocrystal system,which show that the intermolecular interactions in PTC are stronger than those of single components,leading to the stability and photophysical behaviors of cocrystal different from their constitute units.This study will be helpful for the design and preparation of multifunctional cocrystal materials.

Novel machine learning framework for thermal conductivity prediction by crystal graph convolution embedded ensemble

With the advent of the big data era,artificial intelligence technology has penetrated and deeply affected our daily life.In addition,data-based machine learning algorithms have been applied to physics,chemistry,material science,and other basic science fields.However,the scarcity of data sets is known as the main obstacle to its development.Mining effective information from the limited data samples and building an appropriate machine learning algorithms framework are the major breakthroughs.For solid materials,the intrinsic properties are closely related to their atomic composition and relative positions,namely crystal structures.Here,inspired by the emerging of graph convolution neural network and material crystal graph,we proposed an integrated algorithms framework embedded crystal graph to train and predict the lattice thermal conductivities of crystal materials.This machine learning algorithms framework showed superior learning and generalization ability.In addition,not only in predicting thermal conductivities,but our framework also has great performance in predicting other phonon or electron-related properties.This strategy provided a new approach in the design of machine learning framework,which indicated the great potential for the application of machine learning in material science.

A Review on Discrete Element Method Simulation in Laser Powder Bed Fusion Additive Manufacturing

Laser powder bed fusion(LPBF)is a popular metal additive manufacturing technique.Generally,the materials employed for LPBF are discrete and particulate metal matters.Thus,the discontinuous behaviors exhibited by the powder materials cannot be simulated solely using conventional continuum-based computational approaches,such as finite-element or finite-difference methods.The discrete element method(DEM)is a proven numerical method to model discrete matter,such as powder particles,by tracking the motion and temperature of individual particles.Recently,DEM simulation has gained popularity in LPBF studies.However,it has not been widely applied.This study reviews the existing applications of DEM in LPBF processing,such as powder spreading and fusion.A review of the existing literature indicates that DEM is a promising approach in the study of the kinetic and thermal fluid behaviors of powder particles in LPBF additive manufacturing.

Simultaneous studies of pressure effect on charge transport and photophysical properties in organic semiconductors:A theoretical investigation

High-mobility and strong luminescent materials are essential as an important component of organic photodiodes,having received extensive attention in the field of organic optoelectronics.Beyond the conventional chemical synthesis of new molecules,pressure technology,as a flexible and efficient method,can tune the electronic and optical properties reversibly.However,the mechanism in organic materials has not been systematically revealed.Here,we theoretically predicted the pressure-depended luminescence and charge transport properties of high-performance organic optoelectronic semiconductors,2,6-diphenylanthracene(DPA),by first-principle and multi-scale theoretical calculation methods.The dispersion-corrected density functional theory(DFT-D)and hybrid quantum mechanics/molecular mechanics(QM/MM)method were used to get the electronic structures and vibration properties under pressure.Furthermore,the charge transport and luminescence properties were calculated with the quantum tunneling method and thermal vibration correlation function.We found that the pressure could significantly improve the charge transport performance of the DPA single crystal.When the applied pressure increased to 1.86 GPa,the hole mobility could be doubled.At the same time,due to the weak exciton coupling effect and the rigid flat structure,there is neither fluorescence quenching nor obvious emission enhancement phenomenon.The DPA single crystal possesses a slightly higher fluorescence quantum yield~0.47 under pressure.Our work systematically explored the pressure-dependence photoelectric properties and explained the inside mechanism.Also,we proposed that the exte rnal pressure would be an effective way to improve the photoelectric perfo rmance of organic semiconductors.

Cocrystal engineering: Tuning the charge transfer excitons for highly sensitive luminescent switching materials under multiple stimuli

This paper presents a multi-stimuli responsive cocrystal system with luminescent properties that can be dynamically controlled and demonstrates the responsive mechanism of cocrystals under multiple stimuli(acid/alkali vapor, force, and heat). Detailed spectroscopic, computational, and structural studies exhibit that obvious charge transfer interactions occur in loosely mixed-stacking cocrystals. Such interactions can be weakened by acid vapor fuming due to the strong electron-withdrawing effect of acid cations and strengthened under mechanical grinding. Furthermore,the response time of the cocrystal is in the order of seconds,which is much superior to those of most previously reported stimuli-responsive cocrystals. Accordingly, a high-sensitive fluorescence switching is demonstrated under multiple stimuli, providing an effective strategy to develop smart materials.

Thermally-enhanced photo-electric response of an organic semiconductor with low exciton binding energy for simultaneous and distinguishable detection of light and temperature

Simultaneous and distinguishable detection of external stimuli such as light and temperature is of great interest for a variety of scientific and industrial applications.Theoretically,an organic semiconductor with low exciton binding energy,low thermal activation energy and good charge transporting property produces thermally enhanced photo-electric response in organic phototransistors(OPTs),which thus provides an ideal and effective way to realize the simultaneous and distinguishable detection of temperature and light.However,there is no report on such a kind of organic semiconductor until now.Herein,we designed and synthesized a narrow band gap organic small molecule semiconductor 2,5-bis(2-butyloctyl)-3,6-bis(5-(4-(diphenylamino)phenyl)thiophen-2-yl)-2,5-dihydropyrrolo[3,4-c]pyrrole-1,4-dione(DPP-T-TPA)with low exciton binding energy(about 37 meV)and small activation energy(about 61 meV)for distinct thermal-dependence of charge carrier and exciton.The low exciton binding energy enables the semiconductor to exhibit strong thermal dependence of exciton dissociation,which contributes to the thermally-enhanced photo-electric response.Furthermore,the low thermal activation energy produces the weak thermal dependence of charge transport,which avoids the disturbance of thermally-modulated charge transport on photo-electric response.Benefiting from these two features,phototransistors based on DPP-T-TPA show great potential in simultaneous and distinguishable detection of light and temperature,which represents a novel and efficient way for bifunctional detection.