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Ion concentration polarization causes a nearly pore-length-independent conductance of nanopores† 离子浓度极化导致纳米孔隙的电导率几乎与孔隙长度无关
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-08-08 DOI: 10.1039/D4FD00148F
DaVante Cain, Ethan Cao, Ivan Vlassiouk, Tilman E. Schäffer and Zuzanna S. Siwy

There has been a great amount of interest in nanopores as the basis for sensors and templates for preparation of biomimetic channels as well as model systems to understand transport properties at the nanoscale. The presence of surface charges on the pore walls has been shown to induce ion selectivity as well as enhance ionic conductance compared to uncharged pores. Here, using three-dimensional continuum modeling, we examine the role of the length of charged nanopores as well as applied voltage for controlling ion selectivity and ionic conductance of single nanopores and small nanopore arrays. First, we present conditions where the ion current and ion selectivity of nanopores with homogeneous surface charges remain unchanged, even if the pore length decreases by a factor of 6. This length-independent conductance is explained through the effect of ion concentration polarization (ICP), which modifies local ionic concentrations, not only at the pore entrances but also in the pore in a voltage-dependent manner. We describe how voltage controls the ion selectivity of nanopores with different lengths and present the conditions when charged nanopores conduct less current than uncharged pores of the same geometrical characteristics. The manuscript provides different measures of the extent of the depletion zone induced by ICP in single pores and nanopore arrays, including systems with ionic diodes. The modeling shown here will help design selective nanopores for a variety of applications where single nanopores and nanopore arrays are used.

纳米孔作为传感器的基础、制备仿生物通道的模板以及了解纳米尺度传输特性的模型系统,一直备受关注。与不带电的孔相比,孔壁表面电荷的存在已被证明可诱导离子选择性并增强离子传导性。在此,我们利用三维连续建模研究了带电纳米孔的长度以及外加电压在控制单个纳米孔和小型纳米孔阵列的离子选择性和离子传导性方面的作用。首先,我们介绍了具有均匀表面电荷的纳米孔的离子电流和离子选择性保持不变的条件,即使孔的长度减少了 6 倍。离子浓度极化(ICP)不仅改变了孔入口处的局部离子浓度,还以电压依赖的方式改变了孔内的离子浓度,从而解释了这种与长度无关的传导性。我们描述了电压如何控制不同长度纳米孔的离子选择性,并介绍了带电纳米孔比相同几何特性的不带电孔传导更少电流的条件。手稿对单孔和纳米孔阵列(包括带有离子二极管的系统)中由 ICP 引起的耗竭区范围提供了不同的测量方法。这里展示的模型将有助于设计选择性纳米孔,用于单个纳米孔和纳米孔阵列的各种应用。
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引用次数: 0
Scattering of larger molecules – part 1: general discussion 较大分子的散射--第 1 部分:一般性讨论。
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-08-06 DOI: 10.1039/D4FD90019G
Dmitri Babikov, Nadia Balucani, Astrid Bergeat, Mark Brouard, David W. Chandler, Matthew L. Costen, Michal Fárník, Hua Guo, Tibor Győri, Dwayne Heard, David Heathcote, Nils Hertl, Pablo G. Jambrina, Nathanael M. Kidwell, O. A. Krohn, Viet Le Duc, Jérôme Loreau, Stuart R. Mackenzie, Max McCrea, Kenneth G. McKendrick, Jennifer Meyer, Daniel R. Moon, Amy S. Mullin, Gilbert S. Nathanson, Daniel M. Neumark, Kang-Kuen Ni, Martin J. Paterson, Eva Pluhařová, Patrick Robertson, Christopher Reilly, George C. Schatz, Chris Sparling, Arthur G. Suits, Peter D. Watson, Roland Wester, Stefan Willitsch and Alec M. Wodtke
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引用次数: 0
Beyond theory-driven discovery: introducing hot random search and datum-derived structures 超越理论驱动的发现:引入热随机搜索和基准衍生结构
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-08-06 DOI: 10.1039/D4FD00134F
Chris J. Pickard

Data-driven methods have transformed the prospects of the computational chemical sciences, with machine-learned interatomic potentials (MLIPs) speeding up calculations by several orders of magnitude. I reflect on theory-driven, as opposed to data-driven, discovery based on ab initio random structure searching (AIRSS), and then introduce two new methods that exploit machine-learning acceleration. I show how long high-throughput anneals, between direct structural relaxation, enabled by ephemeral data-derived potentials (EDDPs), can be incorporated into AIRSS to bias the sampling of challenging systems towards low-energy configurations. Hot AIRSS (hot-AIRSS) preserves the parallel advantage of random search, while allowing much more complex systems to be tackled. This is demonstrated through searches for complex boron structures in large unit cells. I then show how low-energy carbon structures can be directly generated from a single, experimentally determined, diamond structure. An extension to the generation of random sensible structures, candidates are stochastically generated and then optimised to minimise the difference between the EDDP environment vector and that of the reference diamond structure. The distance-based cost function is captured in an actively learned EDDP. Graphite, small nanotubes and caged, fullerene-like, structures emerge from searches using this potential, along with a rich variety of tetrahedral framework structures. Using the same approach, the pyrope, Mg3Al2(SiO4)3, garnet structure is recovered from a low-energy AIRSS structure generated in a smaller unit cell with a different chemical composition. The relationship of this approach to modern diffusion-model-based generative methods is discussed.

数据驱动方法改变了计算化学科学的前景,机器学习原子间势(MLIP)将计算速度提高了几个数量级。与数据驱动相比,我对理论驱动的发现进行了反思,并介绍了两种利用机器学习加速的新方法。我展示了如何通过短暂数据衍生电位(EDDPs)在直接结构弛豫之间进行长时间高通量退火,并将其纳入 AIRSS,从而将具有挑战性的系统取样偏向于低能配置。热 AIRSS(hot-AIRSS)保留了随机搜索的并行优势,同时允许处理更复杂的系统。我将通过搜索大单元中的复杂硼结构来证明这一点。然后,我展示了如何从实验确定的单一金刚石结构直接生成低能碳结构。作为随机合理结构生成的延伸,候选结构是随机生成的,然后进行优化,以最小化 EDDP 环境向量与参考金刚石结构环境向量之间的差异。基于距离的成本函数被捕捉到主动学习的 EDDP 中。通过使用这种势能进行搜索,出现了石墨、小型纳米管和笼状富勒烯结构,以及种类丰富的四面体框架结构。利用同样的方法,从一个化学成分不同的较小单元格中产生的低能量 AIRSS 结构中恢复了石榴石结构 Mg3Al2(SiO4)3。讨论了这种方法与基于现代扩散模型的生成方法之间的关系。
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引用次数: 0
Re-evaluating retrosynthesis algorithms with Syntheseus† 用 Syntheseus 重新评估逆合成算法
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-08-05 DOI: 10.1039/D4FD00093E
Krzysztof Maziarz, Austin Tripp, Guoqing Liu, Megan Stanley, Shufang Xie, Piotr Gaiński, Philipp Seidl and Marwin H. S. Segler

Automated synthesis planning has recently re-emerged as a research area at the intersection of chemistry and machine learning. Despite the appearance of steady progress, we argue that imperfect benchmarks and inconsistent comparisons mask systematic shortcomings of existing techniques, and unnecessarily hamper progress. To remedy this, we present a synthesis planning library with an extensive benchmarking framework, called SYNTHESEUS, which promotes best practice by default, enabling consistent meaningful evaluation of single-step and multi-step synthesis planning algorithms. We demonstrate the capabilities of SYNTHESEUS by re-evaluating several previous retrosynthesis algorithms, and find that the ranking of state-of-the-art models changes in controlled evaluation experiments. We end with guidance for future works in this area, and call on the community to engage in the discussion on how to improve benchmarks for synthesis planning.

自动合成规划最近再次成为化学与机器学习交叉领域的研究热点。尽管看起来取得了稳步进展,但我们认为,不完善的基准和不一致的比较掩盖了现有技术的系统性缺陷,不必要地阻碍了进展。为了弥补这一缺陷,我们提出了一个具有广泛基准测试框架的合成规划库,名为 Syntheseus,它在默认情况下提倡最佳实践,能够对单步和多步合成规划算法进行一致而有意义的评估。我们通过重新评估之前的几种逆合成算法来证明 Syntheseus 的能力,并发现在受控评估实验中,最先进模型的排名发生了变化。最后,我们为这一领域的未来工作提供了指导,并呼吁社会各界参与讨论如何改进合成规划的基准。
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引用次数: 0
Modelling ligand exchange in metal complexes with machine learning potentials† 用机器学习势能模拟金属复合物中的配体交换
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-08-03 DOI: 10.1039/D4FD00140K
Veronika Juraskova, Gers Tusha, Hanwen Zhang, Lars V. Schäfer and Fernanda Duarte

Metal ions are irreplaceable in many areas of chemistry, including (bio)catalysis, self-assembly and charge transfer processes. Yet, modelling their structural and dynamic properties in diverse chemical environments remains challenging for both force fields and ab initio methods. Here, we introduce a strategy to train machine learning potentials (MLPs) using MACE, an equivariant message-passing neural network, for metal–ligand complexes in explicit solvents. We explore the structure and ligand exchange dynamics of Mg2+ in water and Pd2+ in acetonitrile as two illustrative model systems. The trained potentials accurately reproduce equilibrium structures of the complexes in solution, including different coordination numbers and geometries. Furthermore, the MLPs can model structural changes between metal ions and ligands in the first coordination shell, and reproduce the free energy barriers for the corresponding ligand exchange. The strategy presented here provides a computationally efficient approach to model metal ions in solution, paving the way for modelling larger and more diverse metal complexes relevant to biomolecules and supramolecular assemblies.

金属离子在(生物)催化、自组装和电荷转移过程等许多化学领域都具有不可替代的作用。然而,在不同的化学环境中模拟金属离子的结构和动态特性,对于力场和自洽方法来说仍然具有挑战性。在此,我们介绍了一种利用等变信息传递神经网络 MACE 训练显式溶剂中金属配体复合物的机器学习势(MLP)的策略。我们探索了 Mg2+ 在水中和 Pd2+ 在乙腈中的结构和配体交换动力学,以此作为两个示例模型系统。经过训练的电位能准确再现复合物在溶液中的平衡结构,包括不同的配位数和几何形状。此外,MLP 还能模拟金属离子和配体在第一配位层中的结构变化,并再现相应配体交换的自由能障。本文介绍的策略提供了一种计算高效的方法来模拟溶液中的金属离子,为模拟与生物大分子和超分子组装体相关的更大型、更多样化的金属配合物铺平了道路。
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引用次数: 0
Sequence determinants of protein phase separation and recognition by protein phase-separated condensates through molecular dynamics and active learning† 通过分子动力学和主动学习研究蛋白质相分离和蛋白质相分离凝聚物识别的序列决定因素
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-08-03 DOI: 10.1039/D4FD00099D
Arya Changiarath, Aayush Arya, Vasileios A. Xenidis, Jan Padeken and Lukas S. Stelzl

Elucidating how protein sequence determines the properties of disordered proteins and their phase-separated condensates is a great challenge in computational chemistry, biology, and biophysics. Quantitative molecular dynamics simulations and derived free energy values can in principle capture how a sequence encodes the chemical and biological properties of a protein. These calculations are, however, computationally demanding, even after reducing the representation by coarse-graining; exploring the large spaces of potentially relevant sequences remains a formidable task. We employ an “active learning” scheme introduced by Yang et al. (bioRxiv, 2022, https://doi.org/10.1101/2022.08.05.502972) to reduce the number of labelled examples needed from simulations, where a neural network-based model suggests the most useful examples for the next training cycle. Applying this Bayesian optimisation framework, we determine properties of protein sequences with coarse-grained molecular dynamics, which enables the network to establish sequence–property relationships for disordered proteins and their self-interactions and their interactions in phase-separated condensates. We show how iterative training with second virial coefficients derived from the simulations of disordered protein sequences leads to a rapid improvement in predicting peptide self-interactions. We employ this Bayesian approach to efficiently search for new sequences that bind to condensates of the disordered C-terminal domain (CTD) of RNA Polymerase II, by simulating molecular recognition of peptides to phase-separated condensates in coarse-grained molecular dynamics. By searching for protein sequences which prefer to self-interact rather than interact with another protein sequence we are able to shape the morphology of protein condensates and design multiphasic protein condensates.

阐明蛋白质序列如何决定无序蛋白质及其相分离凝聚物的特性,是计算化学、生物学和生物物理学的一大挑战。定量分子动力学模拟和推导出的自由能值原则上可以捕捉序列如何编码蛋白质的化学和生物特性。然而,这些计算对计算要求很高,即使在通过粗粒化减少表征之后也是如此;探索潜在相关序列的巨大空间仍然是一项艰巨的任务。我们采用了杨等人提出的 "主动学习 "方案(bioRxiv 2022.08.05.502972)来减少模拟所需的标记示例数量,其中基于神经网络的模型为下一个训练周期提出了最有用的示例。通过应用这种贝叶斯优化框架,我们用粗粒度分子动力学确定了蛋白质序列的属性,从而使网络能够建立无序蛋白质的序列属性关系及其在相分离凝聚体中的自我相互作用和相互作用。我们展示了如何利用从无序蛋白质序列模拟中得出的第二病毒系数进行迭代训练,从而快速提高肽自相互作用的预测能力。我们采用这种贝叶斯方法,通过在粗粒度分子动力学中模拟分子识别肽与相分离凝聚物的过程,有效地搜索与 RNA 聚合酶 II 的无序 C 端结构域 (CTD) 凝聚物结合的新序列。通过寻找更倾向于自我相互作用而不是与另一个蛋白质序列相互作用的蛋白质序列,我们能够塑造蛋白质凝聚物的形态并设计多相蛋白质凝聚物。
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引用次数: 0
Molecular sandwich-based DNAzyme catalytic reaction towards transducing efficient nanopore electrical detection of antigen proteins† 基于分子夹心 DNA 酶催化反应的抗原蛋白高效纳米孔电检测技术
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-08-02 DOI: 10.1039/D4FD00146J
Lebing Wang, Shuo Zhou, Yunjiao Wang, Yan Wang, Jing Li, Xiaohan Chen, Daming Zhou, Liyuan Liang, Bohua Yin, Youwen Zhang and Liang Wang

Despite significant advances in nanopore nucleic acid sequencing and sensing, protein detection remains challenging due to the inherent complexity of protein molecular properties (i.e., net charges, polarity, molecular conformation & dimension) and sophisticated environmental parameters (i.e., biofluids), resulting in unsatisfactory electrical signal resolution for protein detection such as poor accessibility, selectivity and sensitivity. The selection of an appropriate electroanalytical approach is strongly desired which should be capable of offering easily detectable and readable signals regarding proteins particularly depending on the practical application. Herein, a molecular sandwich-based cooperative DNAzyme catalytic reaction nanopore detecting approach was designed. Specifically, this approach uses Mg2+ catalyzed DNAzyme (10–23) toward nucleic acids digestion for efficient antigen protein examination. The proposed strategy operates by initial formation of a molecular sandwich containing capture antibody–antigen–detection antibody for efficient entrapment of target proteins (herein taking the HIV p24 antigen for example) and immobilization on magnetic beads surfaces. After that, the DNAzyme was linked to the detection antibody via a biotin–streptavidin interaction. In the presence of Mg2+, the DNAzyme catalytic reaction was triggered to digest nucleic acid substrates and release unique cleavage fragments as reporters capable of transducing more easily detectable nucleic acids as a substitute for the complicated and hard to yield protein signals, in a nanopore. Notably, experimental validation confirms the detecting stability and sensitivity for the target antigen referenced with other antigen proteins, meanwhile it demonstrates a detection efficacy in a human serum environment at very low concentration (LoD ∼1.24 pM). This cooperative DNAzyme nanopore electroanalytical approach denotes an advance in protein examination, and may benefit in vitro testing of proteinic biomarkers for disease diagnosis and prognosis assessment.

尽管在纳米孔核酸测序和传感方面取得了重大进展,但由于蛋白质固有的分子特性(如净电荷、极性、分子构象和ampamp;尺寸)和复杂的环境参数(如生物流体)的复杂性,蛋白质检测仍面临挑战,导致蛋白质检测的电信号分辨率不理想,如可及性、选择性和灵敏度差。因此,选择一种适当的电分析方法是非常必要的,这种方法应能提供易于检测和读取的蛋白质信号,特别是在实际应用中。在此,我们设计了一种基于 DNA 酶催化反应的分子三明治式纳米孔检测方法。特别是,这种方法易于使用 Mg2+ 催化的 DNA 酶(10-23)对核酸进行消化,从而实现高效的抗原蛋白检测。它在拟议策略中的适用性是,首先形成一个分子夹心层,其中包含捕获抗体-抗原-检测抗体,以有效捕获目标蛋白(此处以 HIV p24 抗原为例),并固定在磁珠表面。然后,DNA 酶通过生物素-链霉亲和素相互作用与检测抗体相连。在 Mg2+ 的存在下,DNA 酶的催化反应被触发,消化核酸底物,释放出独特的裂解片段作为报告物,能够在纳米孔中转导更容易检测的核酸,以替代复杂和难以产生的蛋白质信号。值得注意的是,实验验证证实了目标抗原与其他抗原蛋白的检测稳定性和灵敏度,同时证明了在人体血清环境中极低浓度(LoD ~1.24 pM)的检测功效。这种 DNA 酶协同纳米孔电分析方法标志着蛋白质检测技术的进步,可能有利于体外检测蛋白质生物标志物,以进行疾病诊断和预后评估。
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引用次数: 0
Electrochemiluminescence microscopy for the investigation of peptide interactions within planar lipid membranes† 电化学发光显微镜用于研究平面脂膜中肽的相互作用
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-07-31 DOI: 10.1039/D4FD00137K
Kaoru Hiramoto, Kosuke Ino, Ibuki Takahashi, Ayumi Hirano-Iwata and Hitoshi Shiku

Understanding the interactions between lipid membranes and peptides is crucial for controlling bacterial and viral infections, and developing effective drugs. In this study, we proposed the use of electrochemiluminescence (ECL) microscopy in a solution of [Ru(bpy)3]2+ and tri-n-propylamine to monitor alterations in the lipid membranes due to peptide action. A planar artificial lipid membrane served as a model platform, and its surface was observed using ECL microscopy during exposure to melittin, a representative membrane lytic peptide. Upon exposure to melittin, the light-emitting process of the [Ru(bpy)3]2+/tri-n-propylamine system through the lipid membrane exhibited complex changes, suggesting that stepwise peptide actions can be monitored through the system. Furthermore, wide-field imaging with ECL microscopy provided an effective means of elucidating the membrane surface at the submicron level and revealing heterogeneous changes upon exposure to melittin. This complemented the spatiotemporal information that could not be obtained using conventional electrochemical measurements.

了解脂膜与肽之间的相互作用对于控制细菌和病毒感染以及开发有效药物至关重要。在本研究中,我们提出了在[Ru(bpy)3]2+和三正丙胺溶液中使用电化学发光(ECL)显微镜来监测多肽作用引起的脂膜变化。以平面人工脂膜为模型平台,使用 ECL 显微镜观察其表面在暴露于具有代表性的膜溶解肽 melittin 时的变化。暴露于美乐汀时,[Ru(py)3]2+/三正丙胺体系透过脂膜的发光过程表现出复杂的变化,表明可以通过该体系监测肽的分步作用。此外,利用 ECL 显微镜进行宽视场成像提供了一种有效的方法,可在亚微米水平上阐明膜表面的情况,并揭示暴露于美利汀后的异质性变化。这补充了传统电化学测量无法获得的时空信息
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引用次数: 0
Restoring translational symmetry in periodic all-orbital dynamical mean-field theory simulations 在周期性全轨道动态均场理论模拟中恢复平移对称性。
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-07-30 DOI: 10.1039/D4FD00068D
Jiachen Li and Tianyu Zhu

Dynamical mean-field theory (DMFT) and its cluster extensions provide an efficient Green’s function formalism to simulate spectral properties of periodic systems at the quantum many-body level. However, traditional cluster DMFT breaks translational invariance in solid-state materials, and the best strategy to capture non-local correlation effects within cluster DMFT remains elusive. In this work, we investigate the use of overlapping atom-centered impurity fragments in recently-developed ab initio all-orbital DMFT, where all local orbitals within the impurity are treated with high-level quantum chemistry impurity solvers. We demonstrate how the translational symmetry of the lattice self-energy can be restored by designing symmetry-adapted embedding problems, which results in an improved description of spectral functions in two-dimensional boron nitride monolayers and graphene at the levels of many-body perturbation theory (GW) and coupled-cluster theory. Furthermore, we study the convergence of self-energy and density of states as the embedding size is systematically expanded in one-shot and self-consistent DMFT calculations.

动态均场理论(DMFT)及其簇扩展提供了一种高效的格林函数形式,可在量子多体水平上模拟周期系统的光谱特性。然而,传统的簇均场理论打破了固态材料的平移不变性,而在簇均场理论中捕捉非局部相关效应的最佳策略仍然难以捉摸。在这项工作中,我们研究了在最近开发的 ab initio 全轨道 DMFT 中使用重叠原子中心杂质片段的问题,其中杂质内的所有局部轨道都用高级量子化学杂质求解器处理。我们展示了如何通过设计对称适配嵌入问题来恢复晶格自能的平移对称性,从而在多体扰动理论(GW)和耦合簇理论的水平上改进了对二维氮化硼单层和石墨烯中光谱函数的描述。此外,我们还研究了在单次和自洽 DMFT 计算中,随着嵌入尺寸的系统性扩大,自能和状态密度的收敛性。
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引用次数: 0
Enzyme-modified Pt nanoelectrodes for glutamate detection† 用于谷氨酸检测的酶修饰铂纳米电极
IF 3.4 3区 化学 Q2 Chemistry Pub Date : 2024-07-30 DOI: 10.1039/D4FD00138A
Peibo Xu, Henry David Jetmore, Ran Chen and Mei Shen

We present here a glutamate oxidase (GluOx)-modified platinum (Pt) nanoelectrode with a planar geometry for glutamate detection. The Pt nanoelectrode was characterized using electrochemistry and scanning electron microscopy (SEM). The radius of the Pt nanoelectrode measured using SEM is ∼210 nm. GluOx-modified Pt nanoelectrodes were generated by dip coating GluOx on the Pt nanoelectrode in a solution of 0.9% (wt%) bovine serum albumin (BSA), 0.126% (wt%) glutaraldehyde, and 100 U mL−1 GluOx. An increase in current was observed at +0.7 V vs. Ag/AgCl/1 M KCl with adding increasing concentrations of glutamate. Two-sample t-test results showed that there is a significant difference for current at +0.7 V between the blank and the added lowest glutamate concentration, as well as between adjacent glutamate concentrations, confirming that the increase in current is related to the increased glutamate concentration. The experimental current–concentration curve of glutamate detection fitted well to the theoretical Michaelis–Menten curve. At the low concentration range (50 μM to 200 μM), a linear relationship between the current and glutamate concentration was observed. The Michaelis–Menten constants of Imax and Km were calculated to be 1.093 pA and 0.227 mM, respectively. Biosensor efficiency (the ratio of glutamate sensitivity to H2O2 sensitivity) is calculated to be 57.9%. Enzact (Imax/H2O2 sensitivity, an indicator of the amount of enzyme loaded on the electrode) of the GluOx-modified Pt nanoelectrode is 0.243 mM. We further compared the sensitivity of a GluOx-modified Pt nanoelectrode with a GluOx-modified carbon fiber microelectrode (7 μm diameter and a sensing length of ∼350 μm). Glutamate detection on the GluOx-modified carbon fiber microelectrode fitted well to a Michaelis–Menten like response. Based on the fitting, the GluOx-modified carbon fiber microelectrode exhibited an Imax of 0.689 nA and a Km of 301.2 μM towards glutamate detection. The best linear range of glutamate detection on the GluOx-modified carbon fiber microelectrode is from 50 μM to 150 μM glutamate. The GluOx-modified carbon fiber microelectrode exhibited a higher potential requirement for glutamate detection compared to the GluOx-modified Pt nanoelectrode.

我们在此介绍一种具有平面几何形状的谷氨酸氧化酶(GluOx)修饰铂(Pt)纳米电极,用于谷氨酸检测。我们利用电化学和扫描电子显微镜(SEM)对铂纳米电极进行了表征。利用扫描电子显微镜测量的铂纳米电极半径约为 210 纳米。在 0.9% (wt%) 牛血清白蛋白 (BSA)、0.126% (wt%) 戊二醛和 100 U/mL GluOx 的溶液中,通过在铂纳米电极上浸涂 GluOx 生成了 GluOx 修饰的铂纳米电极。随着谷氨酸浓度的增加,在 +0.7 V 与 Ag/AgCl/1M KCl 的对比中观察到电流增加。双样本 t 检验结果表明,在 +0.7 V 处,空白与添加的最低谷氨酸浓度之间以及相邻谷氨酸浓度之间的电流差异显著,这证实了电流的增加与谷氨酸浓度的增加有关。谷氨酸检测的实验电流-浓度曲线与理论 Michaelis-Menten 曲线非常吻合。在低浓度范围内(50 μM 至 200 μM),电流与谷氨酸浓度之间呈线性关系。经计算,Imax 和 Km 的 Michaelis-Menten 常数分别为 1.093 pA 和 0.227 mM。生物传感器效率(谷氨酸敏感度与 H2O2 敏感度之比)计算值为 57.9%。GluOx 改性铂纳米电极的 Enzact(Imax /H2O2 灵敏度,电极上负载酶量的指标)为 0.243 mM。我们进一步比较了 GluOx 改性铂纳米电极与 GluOx 改性碳纤维微电极(直径 7 微米,传感长度约 350 微米)的灵敏度。GluOx 改性碳纤维微电极上的谷氨酸检测与类似 Michaelis-Menten 的反应非常吻合。根据拟合结果,GluOx 改性碳纤维微电极在谷氨酸检测方面的 Imax 为 0.689 nA,Km 为 301.2 μM。GluOx 改性碳纤维微电极检测谷氨酸的最佳线性范围为 50 μM 至 150 μM。与 GluOx 改性铂纳米电极相比,GluOx 改性碳纤维微电极检测谷氨酸的电位要求更高。
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Faraday Discussions
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