Snapshot Redox Cycling Voltammetry (Snapshot RCV): Fast Generation-Collection Detection across a Wide Potential Range at a Chip-Based Microelectrode Array
Blake A. Sterling, Miguel Á. Ábrego Tello, Ingrid Fritsch
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引用次数: 0
Abstract
During generation-collection, redox-active species oxidize
(or
reduce) at generator electrodes and electrochemically convert back
at neighboring collector electrodes, yielding dual-current signals
shaped by mass transport, reaction kinetics, and scaled by analyte
concentration. As electrode spacing decreases, diffusion layers overlap,
recycling species in a feedback loop (redox cycling, RC) and amplifying
observed currents. While advantageous, conventional RC techniques
experience challenges: chronoamperometry (CA) offers high temporal
resolution but lacks thermodynamic insight, while cyclic voltammetry
(CV) captures the potential window but is constrained by scan duration
and background current at high speeds. Here, snapshot redox cycling
voltammetry (Snapshot RCV) is introduced, which constructs the profile
of a conventional RC voltammogram without a potential sweep. By simultaneously
biasing individual generator electrodes in a microband array to different
voltages across the potential range while individually holding collector
electrodes at a reversing potential, high-resolution “spatial”
voltammograms are obtained in as little as 1 s, providing steady-state
generation-collection profiles on a chronoamperometric time scale.
This method was evaluated using 0.50 mM Ru(NH3)6Cl3 in 0.10 M KCl on a seven-electrode gold microband
array (width = 4.08 μm, gap = 4.14 μm, length = 97.17 μm). Snapshot
RCV was then performed on other analytes (dopamine (DA), 3,4-dihydroxyphenylalanine
(L-DOPA), 3,4-dihydroxyphenylacetic acid (DOPAC), and ascorbic acid
(AA)) with different electrochemical properties: standard reduction
potentials (E°), electron transfer rate constants (k°),
and following chemical reaction rates. Snapshot RCV captures the voltammetric
shape with a 10%–60% increase in collection efficiency over
Conventional RCV and can isolate inner array electrochemistry from
edge contributions, allowing for postexperimental geometric filtering.
The different collection efficiencies and voltammetric shapes from
redox-active molecules, driven by their thermodynamics, kinetics,
and chemical mechanisms (EC, ECC′), could make Snapshot RCV
a promising technique for qualitative and quantitative analysis while
enabling rapid sensing that is also adaptable to confined spaces.
在生成-收集过程中,氧化还原活性物质在发生器电极上氧化(或还原),并在邻近的集电极上电化学转化,产生由质量传递、反应动力学和分析浓度决定的双电流信号。随着电极间距的减小,扩散层重叠,在反馈回路中循环物质(氧化还原循环,RC)并放大观察到的电流。虽然具有优势,但传统的RC技术面临挑战:计时安培法(CA)提供高时间分辨率,但缺乏热力学洞察力,而循环伏安法(CV)捕获电位窗口,但受高速扫描持续时间和背景电流的限制。本文介绍了快照氧化还原循环伏安法(snapshot RCV),它构建了没有电位扫描的传统RC伏安图。通过同时将微带阵列中的单个发生器电极偏置到电位范围内的不同电压,同时单独将集电极保持在反向电位,可以在短短1秒内获得高分辨率的“空间”伏安图,从而在计时安培时间尺度上提供稳态发电收集剖面。该方法采用0.50 mM Ru(NH3)6Cl3和0.10 M KCl,在宽4.08 μm,间隙4.14 μm,长97.17 μm的七电极金微带阵列上进行。然后对其他具有不同电化学性质的分析物(多巴胺(DA)、3,4-二羟基苯丙氨酸(L-DOPA)、3,4-二羟基苯乙酸(DOPAC)和抗坏血酸(AA))进行SnapshotRCV,这些电化学性质包括:标准还原电位(E°)、电子转移速率常数(k°)和随后的化学反应速率。快照RCV捕获伏安形状,收集效率比传统RCV提高10%-60%,并且可以从边缘贡献中分离内部阵列电化学,允许实验后几何滤波。氧化还原活性分子的不同收集效率和伏安形状,由其热力学、动力学和化学机制(EC、ECC)驱动,可以使Snapshot RCVa成为定性和定量分析的有前途的技术,同时实现快速传感,也适用于密闭空间。
期刊介绍:
ACS Measurement Science Au is an open access journal that publishes experimental computational or theoretical research in all areas of chemical measurement science. Short letters comprehensive articles reviews and perspectives are welcome on topics that report on any phase of analytical operations including sampling measurement and data analysis. This includes:Chemical Reactions and SelectivityChemometrics and Data ProcessingElectrochemistryElemental and Molecular CharacterizationImagingInstrumentationMass SpectrometryMicroscale and Nanoscale systemsOmics (Genomics Proteomics Metabonomics Metabolomics and Bioinformatics)Sensors and Sensing (Biosensors Chemical Sensors Gas Sensors Intracellular Sensors Single-Molecule Sensors Cell Chips Arrays Microfluidic Devices)SeparationsSpectroscopySurface analysisPapers dealing with established methods need to offer a significantly improved original application of the method.