电化学微/纳米加工技术

通讯联系人，E-mail: dpzhan@ xmu． edu． cn介绍电化学微/纳米加工技术,特别是厦门大学电化学微/纳米加工课题组建立起来的约束刻蚀剂层技术,旨在让广大师生了解这一特种加工技术,共同促进我国电化学微/纳米加工技术的研究及产业化进程。国家自然科学基金(No.91023006,91023047,91023043);中央高校基本科研业务费专项资金(No.2010121022

Chemo-mechanical study on the kerogen molecular reconstruction and cleavage by machine learning

能源是人类社会发展的物质基础，能源安全是国家安全的命脉所在，是实现国家可持续发展和民族复兴的重要保障，当前我国能源对外依存度极高，时刻可能成为被人卡住的&ldquo;最细的脖子&rdquo;。我国已探明的页岩气和页岩油储量分别居世界第一、第三，但是由于我国页岩油气藏在地质学上相对较为年轻，整体处于中低成熟度，不适合直接开采，需要开发原位催熟技术促进发育。干酪根是石油和天然气的母质，也是地球上最丰富的有机质存在形式，深入理解干酪根结构和熟化机理是指导页岩油气原位催熟与油气藏储量评估的基础。干酪根分子结构模型是从分子层面自下而上地研究干酪根化学-力学性质的基石，但在分子模型重构领域存在&ldquo;组合爆炸&rdquo;问题，分子重构难度随着分子量的增大指数增长。干酪根具有起源复杂，分子量大，官能团种类多样的特性，组合爆炸问题尤为突出，导致传统干酪根分子重构方法需要专业人员综合分析多种实验数据，并反复试错逼近真实分子结构，需要花费大量的人力物力，但效率极低，严重制约了对干酪根化学-力学性质的研究。因此，亟待开发新的干酪根分子高通量重构方法，并实现对干酪根分子化学-力学性质的智能化预测。 本学位论文根据上述背景，针对干酪根分子结构模型重构中的组合爆炸和干酪根化学-力学性质智能化预测难题，在力能学指导下，围绕干酪根分子模型智能化重构与干酪根裂解化学-力学机理两大关键科学问题，采用机器学习结合分子动力学模拟、核磁谱模拟计算以及实验分析的方法开展研究。 建设干酪根分子的机器学习数据库。经过标注的海量样本数据是实现机器学习方法的前提条件，但是尚无可满足机器学习高通量重构未知分子模型的数据库，针对此问题，本文通过实验和模拟建设了包含超过 200 万组分子样本的机器学习数据库，其中收录了分子结构、量子轨道构成、热解时序以及拉伸模拟应力-应变曲线等干酪根各项力学、化学特征信息，并且设计了 1H 和 13C 核磁谱的一维、二维离散化重构方法，为机器学习方法高通量重构干酪根分子模型和干酪根化学-力学性质研究奠定了坚实基础。 干酪根分子组分、类型和成熟度的机器学习智能化预测。由于干酪根分子模型的复杂度极高，本文设计了从原子到分子层层递进的策略。首先通过机器学习方法，智能化预测了干酪根分子的骨架组分，并实现了对干酪根类型和成熟度指数的高精度分析。验证了通过机器学习方法结合实验数据智能化获取干酪根分子结构信息的可行性，可在不需要人为干预的情况下直接给出分析结果，极大降低干酪根样品的分析和测试成本。 干酪根分子模型的机器学习法智能化重构。为提高干酪根分子模型的预测性能，解决单一谱图特征对机器学习模型训练效率低下的问题，设计了组合核磁谱特征重构方案和与其相匹配的机器学习神经网络模型，实现了多种谱图输入，使得机器学习模型可同时对多种谱图综合分析，获得了显著高于单一谱图分析的预测性能，实现了对干酪根分子模型的高通量重构，有助于缩短对干酪根的熟化机理和力学性能的研究周期。 干酪根分子裂解化学-力学性质分析及预测。通过密度泛函理论和分子动力学等分子模拟方法，从官能团层面研究不同 sp2/sp3 原子占比干酪根分子模型的高温裂解和拉伸裂解机理，分析了干酪根受热裂解和拉伸裂解的主导机制。并针对干酪根热解行为分析困难的问题，设计机器学习方案，实现了对干酪根热解点位的智能化预测，可为页岩油气藏的生烃潜能评估提供指导。 本论文结合机器学习设计了干酪根分子模型智能化高通量重构方案，并从官能团尺度揭示了干酪根裂解化学-力学行为主导机理，实现对干酪根类型、成熟度及热解位点的智能化预测。为促进非常规油气原位熟化技术的开发提供了更为经济高效的新方法。</p

Predicting the Molecular Models, Types, and Maturity of Kerogen in Shale Using Machine Learning and Multi-NMR Spectra

Kerogen is the primary hydrocarbon source of shale oil/gas. The kerogen types and maturity are the two most crucial indicators that can reflect the hydrocarbon generation potential of shale o il/gas reservoirs. These indicators and the other mechanochemical properties can be effectively studied in a bottom-up strategy using kerogen molecular models. Thus, the rapid construction of kerogen molecular models is the cornerstone of shale oil/gas exploitation research. Because of the combinatorial explosion problem, there are two inherent disadvantages of traditional methods: being time- and material-consuming and labor-intensive. We propose a new method that combines machine learning with multiple nuclear magnetic resonance spectra to intelligently and with a high throughput predict the kerogen structures, types, and maturity. Neither the manual analysis of experimental spectra nor the enormous trial-and-error process is required in our method. The 650,000 groups of samples are annotated as the sample datasets. Various spectral types can be analyzed comprehensively using the multi-spectral form, and the predictive capability beyond that of the single input form is obtained. The results demonstrate that the average similarity of prediction molecules and the targets is 91.78%. The prediction accuracy of kerogen components, types, and maturity indexes is better than 92.4%, and the coefficients of determination R-2 are all over 0.934. The results exhibit the excellent comprehensive performance and effectiveness of our method. Thus, we anticipate that this work will shorten the research cycle and tremendously reduce costs in constructing kerogen models and predicting kerogen properties

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

The shale revolution has provided abundant shale oil/gas resources for the world, but the efficient, sustainable, and environmentally friendly exploitation of shale oil/gas is still challenging. Kerogen is the primary hydrocarbon source of shale oil/gas. The research on the kerogen chemo-mechanical properties significantly influences the development of shale oil/gas extraction technology. Rapid reconstruction of the kerogen molecular models is the most effective way to study the generation mechanism of shale oil/gas from the bottom-up molecular level. However, due to the combinatorial explosion problem, the reconstruction complexity of kerogen increases sharply because of the kerogen's characteristics of complex origin, large molecular weight, and diverse functional groups. The traditional kerogen molecular reconstruction methods require professionals to comprehensively analyze various experimental information to approximate the actual kerogen molecular models through trial-and-error. So, the traditional methods are time and material-consuming and extremely inefficient. These shortcomings make researchers spend too much strength on the reconstruction of kerogen molecular models and cannot focus on the study of kerogen chemo-mechanical properties. For the past few years, state-of-the-art machine learning (ML) methods have been applied to intelligently reconstruct the kerogen molecular models through high-throughput and predict shale oil/gas production mechanisms. Although the current work is still in the infancy stage, ML methods are believed to be the most promising way to solve the drawbacks of traditional methods and reconstruct kerogen in reliable and large molecular weight. Hence, mechano-energetics is proposed to study the efficient development and utilization of energy based on mechanics and ML. This paper briefly reviews the development history of kerogen molecular model reconstruction methods and the research of ML in the fields of kerogen reconstruction and shale oil/gas exploitation. Some recommendations for further ML-based work are also suggested. We are convinced that the ML methods will accelerate the research of kerogen and promote the significant development of unconventional oil/gas exploitation technologies

微反应器分形流道中物质利用效率优化研究

自然界广泛存在的分形流道能够实现物质的高效率输运和利用,对分形流道的研究在医学、化学以及社会学等诸多领域具有非常重要的意义.本文采用理论分析和数值模拟相结合的方法,从物质最优化利用角度出发,讨论分形流道中各级流道的作用和末端流道发育极限.研究结果表明,随着流道内Péclet数(Pe)不断增大,流道内物质利用率先增大后减小,可分为3种状态:对流限制状态、过渡状态和扩散限制状态.过渡状态对应的物质利用率最高,而扩散限制状态下物质输运速率最大.不同限制状态下,影响物质利用率的因素不同,物质利用率与Pe数分别呈现不同标度关系.通过优化算法获得分形流道和其流场信息,与局部分析结果对比发现,流道从初级向末端发育过程中,流道逐级收窄, Pe数降低,流体状态从扩散限制状态逐步转变为过渡状态.综合上述分析,分形流道中物质输运和利用分别在初级流道和末级流道中完成,各层级分工协作高效率地完成输运和利用的任务,并且过渡状态是流道发育的最优极限.本文的研究结果有助于对分形流道的深入理解,可为页岩油气缝网设计、高效催化反应器设计以及高灵敏传感器设计等应用领域提供理论指导

Defining kerogen maturity from orbital hybridization by machine learning

Kerogen is the primary material for oil and gas. Its maturity is used to determine the potential for hydrocarbon generation. Nowadays, kerogen maturity is mainly measured experimentally and characterized by its chemical composition. The fundamental reason for the change in its chemical composition during the maturation is the breaking and recombination of chemical bonds, manifested by the transformation in atomic hybridization based on quantum mechanics. While traditional methods are time-consuming and labor-intensive, machine learning technique has been introduced to clarify the relationship between hybridization and maturity. A kerogen maturity prediction model based on hybridization is constructed. The average error of the predicted values is only 4.91%, and more than 87% of the test samples have an error of less than 10%. The results demonstrate that the model can accurately predict the maturity of kerogen. As the evolution of kerogen maturity increases the proportion of sp(2) hybridized carbons, the orbital hybridization maturity index (OrbHMI) is proposed. The chemical changes in the thermal evolution and pyrolysis mechanism of kerogen can be explained and understood more essentially by OrbHMI. The results provide a basis for guiding artificial maturation and pave a promising path toward studying the kerogen structure and predicting hydrocarbon generating potential

Predicting the components and types of kerogen in shale by combining machine learning with NMR spectra

This study aims to develop a new method that combines machine learning with nuclear magnetic resonance (NMR) spectra to predict the kemgen components and types. Kerogen is the primary hydrocarbon source of shale oil/gas, and nearly half of the hydrocarbons in shale are adsorbed in kemgen. The adsorption and hydrocarbon generation capacity of kerogen is directly related to its types, molecular components, and structures. Fruitful researches studying kerogen at the molecular level have been conducted. Unfortunately, these methods are complicated, time-consuming, and labor-intensive. Our method has the advantages of high-throughput prediction, high accuracy, and time savings compared with the existing methods. Additionally, this method simplifies the operations from repetitive trial and error. This study proposes a solution to convert non-uniform two-dimensional (2D) graph into a uniform one-dimensional (1D) matrix, which makes 2D graph data available for machine learning models. An automatic labeling platform is constructed that annotated over 22,000 groups of organic matter molecules and their NMR spectra. The results show that the carbon, hydrogen, and oxygen element prediction accuracy reach 96.1%, 94.8%, and 81.7%, respectively. In addition, the accuracy of the three kerogen types is approximately 90% in total. These results reflect the excellent performance of the machine learning method. Therefore, our work provides an automated and intelligent prediction and analysis method, which is a powerful and superior tool in kerogen studies at the molecular level

2003～2015年CERN植物物候观测数据集

中国生态系统研究网络(Chinese Ecosystem Research Network,CERN)植物物候观测数据集是CERN生态站植物物候观测数据综合集成的产物,包含21个生态站2003～2015年660余个物种的物候观测记录。因木本植物和草本植物观测的物候期不同,本数据集被分为木本子集和草本子集。木本子集主要记录了芽开放期、展叶期、开花始期、开花盛期、果实或者种子成熟期、叶秋季变色期和落叶期等物候信息。草本子集则记录了萌动期、开花期、果实或种子成熟期、种子散布期和黄枯期等物候信息。另外,本数据集还包含生态站代码、年份、样地代码、样地名称、样地类别、植物种名、拉丁名等信息。本数据集可以为环境变化、碳循环、植物对环境变化的响应等方面的研究提供数据支持

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel