土壤有机酸与磷素相互作用的研究

土壤有机酸包括低分子量有机酸和腐殖酸,它们构成土壤有机组分中性质较活跃的部分。特别是低分子量有机酸,在土壤形成、养分和污染物的转化过程中起着十分重要的作用,受到土壤、植物营养和环境科学工作者的高度重视。本文着重从它们与磷素相互作用的角度,阐述有关研究进展。主要内容包括:(1)磷营养与植物根系分泌有机酸的特征;(2)有机酸对土壤吸附磷的影响;(3)有机酸与磷的解吸、释放与转化。旨在促进研究工作的深入,并为合理利用磷素,保护生态环境提供科学依据。     

低分子有机酸对土壤中重金属的解吸及影响因素

研究了柠檬酸、草酸、酒石酸和苹果酸对矿区土壤中重金属Pb、Cd、Cu和Zn的解吸行为,并探讨了介质pH值对其解吸土中重金属的影响。振荡解吸试验结果表明四种低分子有机酸对供试污染土壤中Pb、Cd、Cu和Zn都具有一定的解吸能力。由于土壤中重金属有效态含量较低,各重金属的解吸率都不高。在对Pb和Cd的解吸中,各低分子有机酸能力大小顺序为柠檬酸>酒石酸≈苹果酸>草酸;Cu的解吸顺序为柠檬酸>草酸>酒石酸≈苹果酸;Zn的解吸顺序为酒石酸>柠檬酸≈苹果酸>草酸。低分子有机酸随浓度的增加,其解吸能力提高。低分子有机酸对重金属的解吸量随pH值的降低而增加。     

发酵鸡粪的高温蛋白分解菌的筛选

采用含有酪素的细菌培养基进行稀释分离,从自然鸡粪堆肥中筛选出16株分解蛋白能力较强的菌株,透明圈直径(d)最大达到66.5mm;通过酶活性的测定,得到9个菌株其中性蛋白酶活力较高,并且在50℃能良好生长;将9个高温菌株分别回接灭菌鸡粪,筛选得到有5个菌株能在鸡粪中定殖,50℃恒温发酵10天,活菌数达到109个g-1以上;接种JS8菌株进行鸡粪发酵,从温度、含水量及pH值指标上,均明显好于鸡粪自然发酵,说明人工添加外源菌群有利于缩短发酵时间、减少发酵过程中养分损失,具有广阔的应用前景。     

暗棕壤中低分子有机酸的吸附与生物降解研究

低分子有机酸在森林生态系统中普遍存在,在根际和多种成土过程中发挥重要作用,但有机酸发挥作用的能力和效率与其在土壤中的吸附与降解等行为关系密切。本文研究了草酸、柠檬酸和苹果酸等低分子有机酸在森林暗棕壤A1层和B层的吸附及生物降解行为,结果表明,低分子有机酸加入到暗棕壤后迅速被土壤固相颗粒所吸附,其中对草酸的吸附远强于柠檬酸和苹果酸,三种低分子有机酸在暗棕壤A1层的吸附率强于B层;草酸、柠檬酸和苹果酸加入到森林暗棕壤后还能迅速被微生物降解,其中对草酸的降解远低于柠檬酸和苹果酸,暗棕壤对草酸较强的吸附作用可能是其生物降解率较低的主要原因,且三种低分子有机酸在A1层暗棕壤中的生物降解率高于B层。     

低分子量有机酸对砖红壤表面电荷的影响

本文研究了4种低分子量有机酸对砖红壤表面电荷性质的影响,结果表明存在于体系中的有机酸增加了表面负电荷量,同时减少表面正电荷的量。在低有机酸加入量下,有机酸对正电荷的影响程度比其对负电荷的影响程度大得多。4种酸对土壤表面电荷影响的大小顺序为:柠檬酸>苹果酸>草酸>醋酸。有机酸对表面电荷的影响随体系pH而变化,它们对正电荷的影响程序随pH的增加而减小,而对负电荷的的影响呈相反的变化趋势。有机酸主要通过其阴离子在土壤表面的吸附来影响土壤的表面电荷性质。     

低分子量有机酸对不同合成磷源的释磷效应

采用化学浸提方法,研究了5种低分子量有机酸(草酸、柠檬酸、酒石酸、苹果酸、乙酸)对不同合成磷源的释磷效应。结果表明:供试有机酸(低浓度乙酸除外)均能促进不同合成磷源(DCP、OCP、FA、Fe-P、A l-P)中磷素的释放;其释磷效果与有机酸的种类和浓度有关,强弱顺序依次为柠檬酸、草酸、酒石酸、苹果酸、乙酸,有机酸浓度越高其释磷效果越好;有机酸与氟磷灰石(FA)反应后,溶液pH值升高。低浓度条件下pH值变化较大,而高浓度条件下pH值变化较小。     

聚合物红外光谱多媒体课件的开发与应用

采用多媒体制作软件Authorware5.0开发了聚合物红外光谱多媒体教学课件，包括红外谱的基本原理，仪器与操作，定性定量分析和练习。引入动画效果讲解原理，采用视频播放演示实验操作。并对应用情况和需改进的地方进行了总结。     

不同类型卷烟中非挥发性有机酸及高级脂肪酸的分析

选取了国内外包括中式烤烟型、中式混合型、英式烤烟型、美式混合型和日式混合型在内的不同类型的93个卷烟样品,测定其非挥发性有机酸及高级脂肪酸含量。讨论了卷烟中有机酸含量的分布特点;结合各类型烟叶的有机酸含量,采用方差分析方法探讨了有机酸在不同类型卷烟中的含量差异及原因:采用相关分析方法探讨了各有机酸物质间的关系。结果表明,各类型卷烟有机酸中,苹果酸含量最高,占有机酸总量的62%。国内外各类型卷烟的有机酸含量存在较大差异。对同一类型卷烟,各高级脂肪酸含量呈显著正相关,高级脂肪酸与草酸、柠檬酸含量呈显著负相关。     

铁系催化剂的鉴定与分析

应用因素分析法于铁系催化剂体系的紫外光谱,对六种样品进行了成份的鉴定。其二种成份为乙酰丙酮和α,α’—联吡啶,并测定了它们的组成。实验值与予计值颇为一致。     

改进的鸡群算法并用于多分类器系数优化

针对鸡群算法因雄鸡粒子易陷入局部最优而无法取得全局最优问题,提出了基于杂交的改进的鸡群算法。即在雌鸡粒子更新后加入杂交机制,使雌鸡粒子加速离开局部最优点;同时通过更新机制将性能优越的雌鸡粒子设定为雄鸡粒子来避免雄鸡粒子陷入局部最优,并将改进的鸡群算法用于多分类器系数的优化。仿真实验结果表明,改进的鸡群算法不易陷入局部最优,且用该算法优化的多分类器其错误率降低,训练时间缩短。     

Thermodynamic assessments of binary phase diagrams in organic and polymeric systems

The Sanchez–Lacombe (SL) model and the Flory–Huggins model were used for the calculation of binary phase diagrams in organic and polymer systems, respectively. The thermodynamic parameters of the liquid and gas phases in acetone–carbon disulfide (CS₂), butane–heptane, cyclohexane–aniline systems, and liquid phases in polystyrene–polybutadiene and polystyrene–bisphenol A poly-carbonate systems were optimized, based on the experimental data. The calculated results with various pressures are in good agreement with the experimental data. It is hoped that this method will be widely applied in the prediction of binary phase diagrams in organic and polymer systems.     

有机物质种类对污染土壤铜形态及活性的影响

本文试验研究了几种有机物质对冶炼厂附近污染土壤中铜形态及其活性的影响.结果表明，有机物质添加后对土壤铜化学形态与活性的影响进程较为迅速，半个月内就有明显的表现不同类型有机物质都对土壤铜的化学活性有控制作用，但控制效果因有机物质种类而异，且与有机物质在土壤中腐解转化有关.     

土壤矿物钾活化途径

针对我国钾肥资源短缺问题,分析了土壤钾资源的开发潜力,概述了土壤矿物钾的存在状态及其释放特点,综述了硅酸盐细菌、富钾植物和有机酸活化土壤矿物钾的能力与机理,并展望了活化土壤矿物钾的三种途径在生产中的应用前景。     

A theoretical exploration of the effect of fluorine and cyano substitutions in diketopyrrolopyrrole-based polymer donor for organic solar cells

A series of polymer donor materials 1-5 based on diketopyrrolopyrrole and thiophene unit which have been widely used in organic solar cells (OSCs) were investigated based on quantum chemical calculations. The effect of fluorine and cyano substitutions in polymer donor materials was focused on. Based on the investigation on electronic structures and optical properties of the reported molecules 1 and 2 and the analysis on some parameters relevant to charge dissociation ability at donor/acceptor interface constituted by 1 and 2 with PC₆₁BM such as intermolecular charge transfer and recombination, driving force and Coulombic bound energy, we explained why fluorine substitution can improve OPV efficiency through strengthening eletron-withdrawing ability from a theoretical perspective. Then we designed cyano-substituted polymers 3-5 with the aim of obtaining better photovoltaic donor materials. The results reveal that our attempt to design donor materials which can balance large open-circuit voltage (V_oc) and high short-circuit current (J_sc) in OSCs has worked out. It is worth noting that the substitutions of fluorine and cyano groups synergistically reduce energy gap and HOMO energy level of polymers 3 and 4. Moreover, 3/PC₆₁BM and 4/PC₆₁BM heterojunctions show over 10⁷ and 10⁴ times higher than 1/PC₆₁BM on the ratios of intermolecular charge transfer and recombination rates (k_inter-CT/k_inter-CR). Thus, our work here may provide an efficient strategy to design promising donor materials in OPVs and we hope it could be useful in the future experimental synthesis.     

线性自抗扰控制参数b0 辨识及参数整定规律

针对自抗扰控制中参数b0 整定困难的问题, 提出一种新的参数辨识方法, 对线性自抗扰控制器(LADRC) 中的参数b0 进行辨识, 并提出了LADRC参数整定的基本规律. 通过频域分析研究了控制器参数b₀ 和控制器带宽b₀ 的变化对闭环系统扰动抑制能力的影响. 通过分析闭环控制系统的稳定区域研究了控制器的鲁棒性. 仿真结果表明, 根据辨识得到的b0 可以快速整定LADRC的参数, 使LADRC具有较强的鲁棒性.

     

Isoform-specific expression and characterization of 14-3-3 proteins in human glioma tissues discovered by stable isotope labeling with amino acids in cell culture-based proteomic analysis

Human 14-3-3 proteins have isoform-specific expression and functions in different kinds of normal or tumor cells and tissues. However, the expression profiling of 14-3-3 proteins and isoform-specific biological functions are unclear in human glioma so far. In our study, the expression levels and characterization of 14-3-3 isoforms in human glioma tissues were investigated by a sensitive, accurate stable isotope labeling with amino acids in cell culture-based quantitative proteomic strategy. As a result, except unexpressed 14-3-3σ, the other six isoforms, with different expression levels, were existed in glioma tissues and para-cancerous brain tissues (PBTs). 14-3-3β and η were upregulated, whereas 14-3-3ζ was downregulated in glioma tissues compared with that in PBTs. And the other three isoforms 14-3-3ε, θ, and γ had similar expression levels in human glioma tissues and PBTs. Western blot and immunohistochemistry analysis were both consistent with the quantitative proteomic data. The loss of expression of 14-3-3σ was further discovered due to DNA high methylation in its coding region in glioma by methylation-specific PCR analysis. These results indicated that the four isoforms, including 14-3-3β, η, ζ, and σ, may play important roles in tumorigenesis of human glioma, which is probably used as potential biomarkers for diagnosis and targets for treatment of human gliomas in future.     

Discovery and prediction capabilities in metal-based nanomaterials: An overview of the application of machine learning techniques and some recent advances

The application of machine learning (ML) techniques to metal-based nanomaterials has contributed greatly to understanding the interaction of nanoparticles, properties prediction, and new materials discovery. However, the prediction accuracy and efficiency of distinctive ML algorithms differ with different metal-based nanomaterials problems. This, alongside the high dimensionality and nonlinearity of available datasets in metal-based nanomaterials problems, makes it imperative to review recent advances in the implementation of ML techniques for these kinds of problems. In addition to understanding the applicability of different ML algorithms to various kinds of metal-based nanomaterials problems, it is hoped that this work will help facilitate understanding and promote interest in this emerging and less explored area of materials informatics. The scope of this review covers the introduction of metal-based nanomaterials, several techniques used in generating datasets for training ML models, feature engineering techniques used in nanomaterials-machine learning applications, and commonly applied ML algorithms. Then, we present the recent advances in ML applications to metal-based nanomaterials, with emphasis on the procedure and efficiency of algorithms used for such applications. In the concluding section, we identify the most common and efficient algorithms for distinctive property predictions. The common problems encountered in ML applications for metal-based nanoinformatics were mentioned. Finally, we propose suitable solutions and future outlooks for various challenges in metal-based nanoinformatics research.