High photosensitivity in cis-polyphenylacetylene films irradiated with 60Co γ-ray

This paper reports that ⁶⁰Co γ-ray irradiation can convert cis-polyphenylacetylene (cis-PPA) films prepared with rare-earth coordination catalysts to highly photosensitive materials. The dependence of the photosensitivity on irradiation dose, preparation methods, and microstructure of the PPA films has been investigated by means of a potential discharge technique. The photosensitivity was enhanced with increasing irradiation dose. The critical dose to produce a light response was 5 × 10³ Gy. The maximum surface potential discharge rate was 618 V/s, and the dark decay was approximately 2 V/s for cis-PPA films irradiated with ⁶⁰Co γ-ray (dose: 2 × 10⁵ Gy). The cis-transoidal-PPA and an electrophotographic photoreceptor device incorporating cis-PPA showed a higher irradiation effect. The structure and properties of ⁶⁰Co γ-ray irradiated rare-earth PPA films are similar to the unirradiated films.     

The Design for Automation of Pellet-Forming Process in HL-2A Pellet Fueling System

In the ever fusion experiments in SWIP, pellet forming process was carried out through adjusting relative devices by staff member in site, which will make every pellet-forming process slight distinction and will result in pellet difference in shape, size and intensity. In the intervals of HL-2A discharges, staff member have to go site to accomplish the pellet-forming process, this wastes human power and increase the potential danger. So it is necessary to develop a remote control system to perform the pellet-forming process. The control system needs have the features of real-time, reliability and be easy to operate and maintain.     

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules

A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (−1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model''s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

Prediction of bond dissociation energies for charged molecules with a graph neural network enabled by global molecular features and reaction difference features between products and reactants.     

声光可调-近红外光谱技术分析烟草主要化学成分

建立了声光可调-近红外光谱方法(AOTF-NIR)检测烟草主要化学成分的方法。应用AOTF-NIR光谱仪测定了不同地区、不同等级烟草样品的近红外光谱,用Unscrambler(定量分析软件将光谱与对应的化学成分值相关联,建立了烟草中总糖、还原糖、总烟碱和钾的回归模型。用这些模型对未知样品进行了预测。总糖、还原糖、总烟碱和钾模型预测的平均相对标准偏差分别为2.71%、3.13%、4.04%和6.42%。     

Application of Mathematical Programming to a Large-scale Agricultural Production and Distribution System

This paper addresses an actual production planning problem for a large seed corn production company. Various scenarios are included in different mathematical programming models in order to help the management make production decisions. A linear programming package and a mixed-integer programming package are combined by a designed heuristic program to solve the problem. The solutions obtained and an accompanying sensitivity analysis provide the management with insight into the system's operation and potentials of cost savings.     

Effect of Intercalated LiF on Interface Contact Characteristics of MoS2 Field Effect Transistor: A First-Principles Study

Based on the first-principles calculation, the effect of intercalated LiF on the contact characteristics of the interface between Au electrode and MoS₂ layer is studied. It is found that adding LiF film can change the contact type between metal electrode Au and MoS₂ layer from Schottky contact to ohmic contact, which is accompanied by interfacial charge transfer from LiF layer to MoS₂ layer and the downward movement of d (d_xy and d_z2) orbital of Mo atom and p (p_x and p_y) orbital of S atom to Fermi level. And the interlayer spacing between LiF layer and Au electrode has a great impact on the interface contact characteristics. The electric field effect and stress effect of interface contact of Au, LiF and MoS₂ (Au/LiF/MoS₂)is more obvious than that of interface contact of Au and MoS₂ (Au/MoS₂). Au/LiF/MoS₂ shows ohmic contact with the interlayer spacing between Au layer and LiF layer less than 3.05 Å and with the electric field less than 0.15 VÅ⁻¹, respectively, while Au/MoS₂ still shows N-type Schottky contact. These findings are helpful to control the contact resistance and have guiding significance for high performance MoS₂ field effect transistor and other electronic components.     

直接衍生离子液体富集高效液相色谱分析水基胶中脂肪族醛酮

建立了以2,4-二硝基苯肼(DNPH)直接衍生,1-丁基-3-甲基咪唑六氟磷酸盐(［BMIM］PF6)萃取富集,高效液相色谱(HPLC)分析水基胶中痕量脂肪族醛酮的方法。分散的水基胶乳液用80 mg/L DNPH衍生化试剂(含0.44 mol/L磷酸)于40 ℃衍生18 min。取离心后的上层衍生液,加入0.5 mL ［BMIM］PF6于30 ℃萃取富集,离子液体相过滤后进行HPLC分析。采用Dionex Acclaim Explosives E2色谱柱(250 mm×4.6 mm, 5 μm),以水-乙腈为流动相在流速1.2 mL/min进行梯度洗脱,色谱柱温度为35 ℃,检测波长为365 nm。结果表明,8种脂肪族醛酮的检出限为0.022～0.221 mg/kg,定量限为0.073～0.738 mg/kg,相对标准偏差为3.5%～7.3%,回收率为84.0%～102.5%。与溶剂萃取法相比,该法具有检出限和定量限低、稳定性高、测定更准确的优势。     

橄榄苦甙的NMR数据解析

对橄榄苦甙进行了¹H和¹³C MNR检测, 通过DEPT和¹H-¹H COSY、HMQC、HMBC、NO ESY等2D NMR对其¹H和¹³C NMR数据进行了全归属和较详细的解析, 并指出其NMR数据特征.     

呼吸尘埃中游离晶态α－石英的X射线衍射外标法测定

本实验用Ｘ射线衍射外标法测定了极微量（０．１～５ｍｇ）呼吸尘埃中游离晶态α－ＳｉＯ＿２的含量。其精确度（相对偏差）小于１０％，达到常规微量（５～１０ｍｇ）样品的分析精确度。     

改进的部分子演化模型及其对EMC效应和核Drell－Yan过程的解释

用解析方法证明了部分子演化模型可为双重Ｘ重新标度模型提供物理基础，进而利用修正的Ａｌｔｅｒａｌｌｉ－Ｐａｒｉｓｉ方程描述小ｘ区域的核遮蔽和反遮蔽效应，以改进部分子演化模型，使我们不必引入核遮蔽因子便可统一地描述ＥＭＣ效应、核遮蔽和反遮蔽效应．最后，利用改进的部分子演化模型相当好地解释了核Ｄｒｅｌｌ－Ｙａｎ过程的实验数据．