Deposition of Gold Nano-Micro Islands on Electrochemically Reduced Graphene Oxide to Use in Combination with Molecularly Imprinted Polymers
Bibliographic record
Abstract
Molecularly imprinted polymers (MIPs) are known as an alternative for antibodies in immunosensors with high stability and low production cost. MIP approach relies on polymerization in the presence of a template molecule and subsequent template removal. In this process, the template molecule is exactly the same as the target molecule leading to a good selectivity for the biomimetic sensor [1]. The integration of MIPs with nanomaterials enables ultrasensitive sensors for various analytes. Besides, nanomaterials with uniform distribution and suitable morphology provide a wide linear range of detection for the sensor [1]. In particular, the controlled electrodeposition of gold can supply gold nano-micro islands (NMIs) with remarkable electrochemical behavior and high active surface area [2, 3]. However, it is found that the modification of an electrode surface using electrochemically reduced graphene oxide (ERGO), as a facile and fast method, not only increases the surface area but also improves its electrochemical behavior [4]. Herein, we describe a surface modification strategy based on indium tin oxide (ITO) electrode coated with ERGO to increase the surface active sites for nucleation and deposition of NMI. For this aim, we electrochemically reduced graphene oxide (GO) by one cycle in cyclic voltammetry (CV) with a scan rate of 100 mV s-1 in the range of -0.6 to -2 VAg/AgCl. The succeed of the electrochemical reduction was confirmed through ID/IG ratio in Raman spectroscopy and observing the enhanced electrochemical behavior in CV and electrochemical impedance spectroscopy (EIS) tests. The gold layers were deposited at 0 and -0.4 VAg/AgCl in 5, 50 and 100 mM HAuCl4 solutions for 50 and 300 s. The surface area of the resulted structures was also determined by integrating the reduction peak in CV tests in 50 mM H2SO4 solution [2]. The results showed that the gold structure deposited from 100 mM HAuCl4 at 0 VAg/AgCl for 50 s provided a significant surface area (0.47 cm2) on the electrode surface of 1 mm diameter, while this value for gold nanoparticles, as the common form of gold nanostructure in electrochemical biosensing, was determined only 0.063 cm2. This noticeable increase in surface area can result in a wide linear range of detection as well as excellent electrochemical behavior in biosensing applications. Scanning electron microscopy (SEM) of NMI structures deposited on both ITO and ERGO/ITO electrodes indicated a shrub-like structure because of the concentration polarization governed during gold deposition. Transmission scanning electron microscopy (TEM) of the NMI displayed Au (113) as the main crystalline plane. Based on the chronoamperometry plots, the gold deposition mechanism on the surface of both electrodes was according to the instantaneous deposition mode [2]. However, the electrode background surface indicated that the ERGO/ITO was uniformly covered by gold. This was attributed to the higher nucleation sites on the ERGO-modified electrode, which was confirmed by the increase of double-layer formation time (tmax). Accordingly, several nucleation stages on the chronoamperometry plot during NMI deposition on the ERGO/ITO electrode, were related to the formation of gold shrub-like structures with more needle-shaped structures. Finally, electropolymerization of a thin layer of o-phenylenediamine in the presence of Heart-fatty acid binding protein (H-FABP) as the template molecule and its subsequent removal was applied to make a biomimetic sensor. In this study, we demonstrated that the biomimetic developed electrode with high surface area, low production cost, and facile and fast synthesis method is acceptably selective to H-FABP against other cardiac biomarkers and serum proteins. KEYWORDS: Electrodeposition; Electrochemically reduced graphene oxide; Electrochemical sensors; Gold nano-micro islands; Molecularly imprinted polymers. References [1] R. Gui, H. Jin, H. Guo, Z. Wang, Recent advances and future prospects in molecularly imprinted polymers-based electrochemical biosensors, Biosens. Bioelectron. 15 (2018) 56-70. [2] S. Mahshid, A. H. Mepham, S. S. Mahshid, B. BurgessI, T. SaberiSafaei, E. H. Sargent, S. O. Kelley, Mechanistic Control of the Growth of Three-Dimensional Gold Sensors, The Journal of Physical Chemistry C 120 (2016) 21123-21132. [3] M. Jalali, T. Abdel Fatah, S. S. Mahshid, M. Labib, A. S. Perumal, S. Sara Mahshid, A Hierarchical 3D Nanostructured Microfluidic Device for Sensitive Detection of Pathogenic Bacteria, Small 14 (2018) 1801893. [4] A. Sanati, M. Jalali, K. Raeissi, F. Karimzadeh, M. Kharaziha, S. S. Mahshid, S. Mahshid, A review on recent advancements in electrochemical biosensing using carbonaceous nanomaterials, Microchimica Acta 186 (2019) 773.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".