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Utilization of Inexpensive Carbon-Based Substrates as Platforms for Sensing.

ABSTRACT: Gold (Au) has been widely used as a material for Surface Enhanced Raman Spectroscopy (SERS) due to its plasmonic properties, stability and biocompatibility. Conventionally for SERS application, Au is deposited on a rigid substrate such as glass or silicon. The rigid substrates severely limit analyte collection efficiency as well as portability. Here, flexible substrates like carbon cloth and carbon paper were investigated as potential substrate candidates for SERS application. The flexible substrates were coated with Au nanostructures by electrodeposition. Model analyte, Rhodamine 6G was utilized to demonstrate the capabilities of the flexible SERS substrates. Additionally, the pesticide paraoxon was also detected on the flexible SERS substrates as well as on a real sample like the apple fruit.

SUBMITTER: Tran M

PROVIDER: S-EPMC6111970 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Utilization of Inexpensive Carbon-Based Substrates as Platforms for Sensing.

Tran Minh M Fallatah Ahmad A Whale Alison A Padalkar Sonal S

Sensors (Basel, Switzerland) 20180727 8

Gold (Au) has been widely used as a material for Surface Enhanced Raman Spectroscopy (SERS) due to its plasmonic properties, stability and biocompatibility. Conventionally for SERS application, Au is deposited on a rigid substrate such as glass or silicon. The rigid substrates severely limit analyte collection efficiency as well as portability. Here, flexible substrates like carbon cloth and carbon paper were investigated as potential substrate candidates for SERS application. The flexible subst ...[more]

PMID: 30060494

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Project description:A new approach to the electronic instrumentation for extracting data from resonator-based sensing devices (e.g., microelectromechanical, piezoelectric, electrochemical, and acoustic) is suggested and demonstrated here. Traditionally, oscillator-based circuitry is employed to monitor shift in the resonance frequency of the resonator. These circuits give a single point measurement at the frequency where the oscillation criterion is met. However, the resonator response itself is broadband and contains much more information than a single point measurement. Here, we present a method for the broadband characterization of a resonator using white noise as an excitation signal. The resonator is used in a two-port filter configuration, and the resonator output is subjected to frequency spectrum analysis. The result is a wideband spectral map analogous to the magnitude of the S21 parameters of a conventional filter. Compared to other sources for broadband excitation (e.g., frequency chirp, multisine, or narrow time domain pulse), the white noise source requires no design of the input signal and is readily available for very wide bandwidths (1 MHz-3 GHz). Moreover, it offers simplicity in circuit design as it does not require precise impedance matching; whereas such requirements are very strict for oscillator-based circuit systems, and can be difficult to fulfill. This results in a measurement system that does not require calibration, which is a significant advantage over oscillator circuits. Simulation results are first presented for verification of the proposed system, followed by measurement results with a prototype implementation. A 434 MHz surface acoustic wave (SAW) resonator and a 5 MHz quartz crystal microbalance (QCM) are measured using the proposed method, and the results are compared to measurements taken by a conventional bench-top network analyzer. Maximum relative differences in the measured resonance frequencies of the SAW and QCM resonators are 0.0004% and 0.002%, respectively. The ability to track a changing sensor response is demonstrated by inducing temperature variations and measuring resonance frequency simultaneously using the proposed technique in parallel with a network analyzer. The relative difference between the two measurements is about 5.53 ppm, highlighting the impressive accuracy of the proposed system. Using commercially available digital signal processors (DSPs), we believe that this technique can be implemented as a system-on-a-chip solution resulting in a very low cost, easy to use, portable, and customizable sensing system. In addition, given the simplicity of the signal and circuit design, and its immunity to other common interface concerns (injection locking, oscillator interference, and drift, etc.), this method is better suited to accommodating array-based systems.

Project description:Model topology is divided into two compartments, cell programming and performance testing. The cell programming compartment is split into history and pre-treatment. History ( History I or H1: E.coli grown for 18hrs in LB flask, transferred to fresh LB flask after that. History II or H2: E.coli grown for 18hrs in LB flask, transferred to fresh LB flask for 45 min. From this flask, 0.1O.D./ml transferred to rich medium and grown for 4 hours. From this,0.1O.D./ml transferred to fresh rich medium. History III or H3: E.coli grown for 18hrs in LB flask, transferred to fresh LB flask for 45 min. From this flask, 0.1O.D./ml transferred to starvation medium and grown for 4 hours. From this,0.1O.D./ml transferred to fresh starvation medium. Pre-treatment (Pre-treatment 1(T1): 2.5g glucose/litre 5mM NH4Cl. Pre-treatment 2 (T2): 2.5g glucose/litre 0.25mM NH4Cl. Pre-treatment 3 (T3): - 0.25g glucose/litre 5mmM NH4Cl. Pre-treatment 4 (T4): 0.25g glucose/litre 0.25mM NH4Cl). Each pre-treatment given for 2.5 hours.The culture nomenclature indicates the adaptive path followed, for example, H1T1 indicates the culture has encountered history I (H1) and then transferred to pre-treatment 1 (T1).RNA was extracted for selected combinations. Performance testing : Performance testing describes the type of analysis done which is the growth pattern study onto three substrates, glucose, succinate and pyruvate. This performance testing revealed specific history-pretreatment combinations to be better suited for growth on certain substrate and some not suited for growth. The samples were harvested for RNA isolation at peak growth points and named worst_glucose, best_glucose,worst_succinate, best_succinate, worst_pyruvate and best_pyruvate according to the growth shown after testing all 12 history-pretreatment combinations. The differences in physiology were studied in details using microarray analysis of 13 samples including 3 history samples, 4 pre-treatment samples and 6 samples at performance testing level. RNA extraction was done using Qiagen RNeasy minikit (Germany). Standard Affymetrix protocol was followed for hybridization on Affymetrix E. coli Genome 2.0 Array.

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Utilization of Inexpensive Carbon-Based Substrates as Platforms for Sensing.

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Utilization of Inexpensive Carbon-Based Substrates as Platforms for Sensing.

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