生物医用高通量研究中的微液滴阵列

为快速、高效处理大量实验及其数据,进一步加快材料研发过程,高通量筛选成为了一种越来越重要的实验手段,被广泛应用于众多领域,以提升实验效率。而高通量实验平台是开展高通量实验的基础条件。现有高通量实验平台,如微孔板,在处理珍贵样品和试剂时,仍存在消耗量较大、实验通量较低等问题,仍有待进一步优化。而微液滴阵列作为一种新兴的微型化和集成化高通量平台,具有试剂及样品消耗少、反应时间短、高度集成化、可操作性强等优势,在生物医用领域得到了广泛的研究和应用。本文总结归纳了微液滴阵列的制备方法,将其划分为表面化学驱动和物理形貌辅助两大类,并对不同制备方法的优缺点进行了简要的分析;随后从2D细胞筛选、3D细胞培养、单细胞分析及全机体筛选四个方向对其在生物医用高通量研究中的应用进行了简要介绍,最后总结分析了微液滴阵列在应用过程中存在的问题和未来的发展方向。     

生物质吸附剂的制备表征及其对水中Cu~(2+)吸附性能的研究

介绍一个集物理化学、分析化学于一体的综合性实验——生物质吸附剂对水体中Cu~(2+)吸附性能研究。首先制备出生物质吸附剂,并运用红外光谱仪分析其结构;考察了pH、时间、温度等条件对吸附剂吸附Cu~(2+)的影响,研究其吸附机理。该实验涉及物理化学的表面吸附原理和分析化学中分析方法的运用、大型仪器的使用,以及数据处理方法,既锻炼了学生的动手操作能力,又提高了学生对实验数据的处理分析能力。     

制备方法对Pd/Ce-Zr-Mn-Al-O材料催化性能的影响研究

通过溶胶凝胶法、浸渍法和共沉淀法制备含贵金属Pd的Ce-Zr-Mn-Al-O固溶体材料,并对新鲜和老化样件材料的BET、孔容、孔径、TPR等表征研究,结果表明:采用溶胶凝胶法制备的材料在新鲜和老化条件下具有良好的储氧能力(OSC)和表征特性,将不同制备方法所得的材料制成催化剂样件在发动机台架进行连续空燃比和连续温度实验,台架数据结果表明采用溶胶凝胶法制备的催化剂在处理HC和NOx时表现出优良的特性,催化剂对总碳氢化合物(THC)和NOx的初始转化能力和偏稀条件下NOx的处理具有较强的适应能力,该方法制备的催化剂应用到CNG发动机上也取得了良好的效果。     

离子色谱法测定污水中阴、阳离子的前处理

研究了离子色谱技术在污水中常见阴、阳离子监测方面的应用。根据大量实验数据提出了水样前处理方法，确定了色谱条件。污水样品经前处理后，阴离子测定结果的相对标准偏差为0.71%-4.4%，回收率为94．6％－108.6%。     

Excel在基础无机化学实验数据处理中的应用

介绍了一种处理基础无机化学实验数据的方法,即用Microsoft Office系列中的Excel软件自动完成数据的分析和图表的处理.以"化学反应速率"及"碘化铅溶度积常数的测定"的实验内容为例,通过实验测得的反应速率和c(I-) 标准曲线吸光度及PbI2饱和溶液的制备.用Excel进行数据分析、公式计算和图表处理,找出了线形拟合关系,确定了反应级数,计算出了速率方程、速率常数、活化能和溶度积常数,以完整详实地实验记录将处理的结果完整地展示出来.方法具有方便快捷、精确度高、操作简便等优点.减少人工处理数据带来的误差.是本科生学习处理专业实验数据的基本方法之一.     

硫酸亚铁铵实验不同结晶条件的探讨

硫酸亚铁铵的制备是大学基础化学实验的一个经典实验,结晶条件对目标产物的结构和形态影响较大,研究了在不同结晶条件下制备了硫酸亚铁铵,并对其进行了单晶结构测试和解析,探讨了硫酸亚铁铵的最佳结晶条件。     

硅胶制备的趣味化设计及其原理分析

硅胶制备实验的难点在于控制不好溶液的酸度和温度这2个关键条件。本研究经过实验探索发现了最佳制备方法,不仅极大程度地提高了硅胶制备实验的成功率,而且还增加了趣味性。     

累托石-壳聚糖吸附剂对Cd~(2+)的吸附作用研究

利用累托石负载壳聚糖制备一种复合吸附剂,并对其结构进行了表征.研究了复合吸附剂对Cd2+的吸附,应用正交试验确定了最佳吸附条件:当pH值为7,吸附时间为30min,投加量为0.3g和温度为20℃时,Cd2+的吸附率达99%以上,处理后的水符合国家污水综合排放标准(GB8978-1996)中的一级标准.通过对实验数据运用相关数学模型拟合,复合吸附剂对Cd2+的吸附符合Langmuir和Freundlich吸附等温式.     

综述(187~191)利用冷冻聚焦离子束制备冷冻含水切片技术问题探讨

冷冻聚焦离子束制备冷冻含水切片为研究细胞内原位结构打开了窗口, 其切片质量直接影响了后续冷冻电子断层扫描成像数据的收集及处理.目前, 这一技术的应用还存在各种各样的问题, 总结了近期文献中的相关解决之道, 以期为制备冷冻含水切片的研究者提供参考.     

医药类专业基于滴定分析实验培养学生误差分析能力的教学设计

为提高医药类专业滴定分析化学实验教学效果,设计了一种新的滴定分析测定药品含量实验的教学模式,将实验设计成不同的条件实验,学生分别完成不同的条件实验后共享实验数据,通过处理实验数据,学生可以对整个实验进行条件分析,在学习滴定分析测定药物含量实验的过程中,对实验原理有更深的理解,提高了学生分析实验关键点和处理实验数据的能力,增强了团队合作意识。     

人工神经网络法用于高效毛细管电泳分离条件优化的研究

把人工神经网络（ＡＮＮ）法应用于高效毛细管电泳（ＨＰＣＥ）分离条件的优化，给出了反向传播（ＢＰ）的ＡＮＮ模型的具体算法。用正交试验法同时考察了缓冲溶液组成、浓度、ｐＨ值和有机添加剂浓度等实验因素对ＨＰＣＥ分离合成色素和防腐剂的影响，采用误差反向传播方法建立了有效的ＡＮＮ预测模型，预测最佳分离条件，获得了满意的分离结果。     

Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning

Computer aided synthesis planning of synthetic pathways with green process conditions has become of increasing importance in organic chemistry, but the large search space inherent in synthesis planning and the difficulty in predicting reaction conditions make it a significant challenge. We introduce a new Monte Carlo Tree Search (MCTS) variant that promotes balance between exploration and exploitation across the synthesis space. Together with a value network trained from reinforcement learning and a solvent-prediction neural network, our algorithm is comparable to the best MCTS variant (PUCT, similar to Google''s Alpha Go) in finding valid synthesis pathways within a fixed searching time, and superior in identifying shorter routes with greener solvents under the same search conditions. In addition, with the same root compound visit count, our algorithm outperforms the PUCT MCTS by 16% in terms of determining successful routes. Overall the success rate is improved by 19.7% compared to the upper confidence bound applied to trees (UCT) MCTS method. Moreover, we improve 71.4% of the routes proposed by the PUCT MCTS variant in pathway length and choices of green solvents. The approach generally enables including Green Chemistry considerations in computer aided synthesis planning with potential applications in process development for fine chemicals or pharmaceuticals.

A new MCTS variant with a reinforcement learning value network and solvent prediction model proposes shorter synthesis routes with greener solvents.     

基于径向基神经网络和遗传算法的槽内式选择性超声电合成甲基苯甲醛

以混合二甲苯为原料, Mn(Ⅲ)为氧化剂, 硫酸溶液为电解质, 采用槽内式超声电合成甲基苯甲醛. 探讨了选择性电合成甲基苯甲醛的可能性, 通过径向基(RBF)神经网络和遗传算法(GA)对选择性电合成甲基苯甲醛3种异构体的比例、 电流效率与混合二甲苯的用量、 硫酸浓度和电流强度的关系建立预测模型, 并运用GA确定模型中RBF神经网络的目标均方误差(Goal)和径向基函数的分布(Spread). 然后根据预测模型, 使用GA对电合成条件进行优化, 分别获得了电合成产物中对位甲基苯甲醛占优、 邻位和对位甲基苯甲醛占优以及电流效率最高时的电合成条件. 当采用上述条件进行实验时, 模型给出的预测结果分别为: 对位甲基苯甲醛占优的质量分数可达90.01%, 邻位和对位甲基苯甲醛占优的质量分数为80.38%, 电流效率达到最高时的邻位、 间位和对位甲基苯甲醛的质量分数分别为16.80%, 8.43%和74.77%; 而与之相对应的实际实验结果分别为90.10%和79.91%, 以及17.20%, 8.49%和74.31%, 二者之间的最大相对误差小于±2.24%, 表明所建立模型的预测值与实测值基本吻合.     

Direct and inverse neural network modeling in free radical polymerization

The first part of this paper reviews of the most important aspects regarding the use of neural networks in the polymerization reaction engineering. Then, direct and inverse neural network modeling of the batch, bulk free radical polymerization of methyl methacrylate is performed. To obtain monomer conversion, number and weight average molecular weights, and mass reaction viscosity, separate neural networks and, a network with multiple outputs were built (direct neural network modeling). The inverse neural network modeling gives the reaction conditions (temperature and initial initiator concentration) that assure certain values of conversion and polymerization degree at the end of the reaction. Each network is a multi-layer perceptron with one or two hidden layers and a different number of hidden neurons. The best topology correlates with the smallest error at the end of the training phase. The possibility of obtaining accurate results is demonstrated with a relatively simple architecture of the networks. Two types of neural network modeling, direct and inverse, represent possible alternatives to classical procedures of modeling and optimization, each producing accurate results and having simple methodologies.     

基于BP神经网络研究儿茶素在ADS-8树脂固定床的吸附过程

以信阳毛尖茶叶浸提液为原料,研究儿茶素在ADS-8树脂固定床的吸附过程及优化吸附条件。试验基于BP神经网络建立吸附模型,通过仿真分析上样浓度、柱径比和流速对穿透时间、穿透体积的影响。模型的模拟值与试验值的相关系数为0.992,模拟值与试验值没有显著差异。最佳工艺条件为上样浓度1.5mg/mL,柱径比17:1,流速3.0mL/min。建立的模型可用于模拟儿茶素在ADS-8树脂固定床的吸附过程,结果为吸附树脂法制备儿茶素提供一定的参考。     

Retention modeling and simultaneous optimization of pH value and gradient steepness in RP‐HPLC using feed‐forward neural networks

A novel approach is proposed for the simultaneous optimization of mobile phase pH and gradient steepness in RP‐HPLC using artificial neural networks. By presetting the initial and final concentration of the organic solvent, a limited number of experiments with different gradient time and pH value of mobile phase are arranged in the two‐dimensional space of mobile phase parameters. The retention behavior of each solute is modeled using an individual artificial neural network. An “early stopping” strategy is adopted to ensure the predicting capability of neural networks. The trained neural networks can be used to predict the retention time of solutes under arbitrary mobile phase conditions in the optimization region. Finally, the optimal separation conditions can be found according to a global resolution function. The effectiveness of this method is validated by optimization of separation conditions for amino acids derivatised by a new fluorescent reagent.     

蛋白质二级结构预测的人工神经网络方法研究

本文比较了五种神经网络方法预测蛋白质二级结构的准确率,并做出初步评价。五种神经网络分别是:误差反传前向网络(BP),径向基函数网络(RBF),广义回归神经网络(GRNN),串并联叠层网络(CF),Elman网络(ELM)。结果显示:GRNN的预测准确率达85.7%,优于其它网络。本文还讨论了训练集样本数及参数的优化对GRNN预测准确率的影响。     

级联神经网络用于单扫描示波极谱信号的测定

将改进小波神经网络与BP神经网络相结合,提出一种新的混级联神经网络结构,用于单扫描示波极谱信号的同时测定。通过对网络结构的优化和网络参数的调整,加快了训练速度,提高了预测的准确度。用该法对邻、间硝基氯苯混合样进行了预测,结果满意。对级联神经网络法与单一BP神经网络法的预测结果进行了比较,表明级联神经网络优于单一BP神经网络。     

Application Des Réseaux de Neurones Avec la Régularisation Bayésienne pour la Modélisation de la Synthèse de l’hydroxyapatite Élaborée à Partir du Carbonate de Calcium et de L’acide Phosphorique

We have used a new, robust model mapping technique—a Bayesian-regularized neural network—to develop a quantitative relationships model for the synthesis of the phosphocalcic hydroxyapatite by precipitation from a calcium carbonate solution and a phosphoric acid solution. This model was preformed by using a set of factors consisting on the pH of reactional medium, the Ca/P molar ratio of the reagents, reaction time, and the initial concentration of calcium. The results show that the method is robust and gives satisfied results. The Levenberg–Marquardt's algorithm implemented in the neural network Matlab's toolbox allowed a drastic improvement of the performance of the model. Very satisfactory results are then obtained by testing the validity by cross-validation technique.

We have also turned our interests to the explanatory capacities of our methodology to explore the relative contribution and/or the contribution profile of the input factors by using Garson weight portioning method.     

Hybrid neural network model of an industrial ethanol fermentation process considering the effect of temperature

In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur.