Hardware

Hardware

Table of Contents

Overview

The goal of this system is to monitor bacterial concentrations in wounds and automatically deliver antimicrobial peptides (AMP) or probiotics based on real-time data analysis, helping to accelerate wound healing. This workflow involves various technologies including cyclic voltammetry, neural networks, and smartphone integration. Below is an overview of each component and how they work together to achieve the desired functionality.

Cyclic Voltammetry(CV)

Theorum

Cyclic voltammetry involves applying a linearly varying potential (voltage) to an electrochemical cell and measuring the resulting current. The potential is ramped between two limits (a lower and an upper limit), and then reversed to return to the starting potential, forming a cycle. This cyclic pattern can be repeated multiple times to observe the behavior of the system over successive scans.

This are the main component when conducting CV experiments:

  1. Working Electrode: The electrode where the redox reaction occurs. It’s typically made of materials like platinum, gold, or glassy carbon.
  2. Reference Electrode: A stable electrode that maintains a constant potential. The most common reference electrode is the silver/silver chloride (Ag/AgCl) electrode.
  3. Counter Electrode: Completes the circuit in the electrochemical cell, balancing the current at the working electrode.
  4. Electrolyte: The solution that facilitates ion movement between the electrodes. It is typically a conductive medium containing the redox-active species and supporting ions.

A cyclic voltammogram typically has the following features:

  1. Oxidation Peak: A peak in the current during the forward scan where the analyte gets oxidized.
  2. Reduction Peak: A peak in the current during the reverse scan where the oxidized species gets reduced.
  3. Peak Potentials: The potentials where oxidation and reduction peaks occur. These can provide information about the redox potential of the species and the reversibility of the reaction.
  4. Peak Separation: The difference in potential between the oxidation and reduction peaks can indicate the kinetics of the electron transfer process.

Reversible Reactions: For a reversible redox reaction, the oxidation and reduction peaks will be symmetrical, and the peak separation will be relatively small. The ratio of peak currents (oxidation to reduction) will also be close to unity.

Irreversible Reactions: For irreversible reactions, only one peak (oxidation or reduction) may appear, or the separation between the oxidation and reduction peaks may be large.

Potentiostat Circuit

Introduction

The potentiostat is a core component of our electrochemical sensing system, responsible for controlling the applied voltage and measuring the resulting current during electrochemical reactions. Our custom-built circuit, designed using operational amplifiers and feedback loops, ensures precise voltage control and accurate current measurements. This module is crucial for obtaining reliable data from our Screen Printed Electrode (SPE), allowing us to monitor the binding ratio of aptamers in real-time during cyclic voltammetry (CV) experiments. Such a setup enables the quantitative analysis of biomarker interactions, which is essential for applications like disease diagnostics and environmental monitoring (Li et al., 2020).

Circuit Overview and Working Principle

Our potentiostat circuit is constructed around an Arduino Nano microcontroller, serving as the central unit for voltage control and data acquisition, similar to the Paqari Stat developed by Cordova-Huaman et al. (2021). It includes the following key components:

  • LM324 Operational Amplifiers: Employed to maintain a stable potential at the Working Electrode (WE) and to measure the current between the Counter Electrode (CE) and Reference Electrode (RE).
  • ICL7660 Voltage Converters: Provide dual power supply (+5V and -5V) to the operational amplifiers, ensuring the capability for both anodic and cathodic scans in cyclic voltammetry experiments.
  • Feedback Network: Implements a precision feedback loop to ensure stable voltage control at the WE.
  • Bluetooth Module: Added to allow remote monitoring and control of the potentiostat, enabling real-time data acquisition and analysis.

The system works by applying a controlled potential to the Working Electrode (WE) while measuring the resulting current between the Counter Electrode (CE) and Reference Electrode (RE). The Arduino’s analog pins read the resulting current signal, which is then digitized and processed for real-time analysis.

Prototype Development and Testing

During the initial development phase, all components were connected using a breadboard to test the circuit’s functionality and optimize component values (see attached images). The breadboard-based prototype allowed us to quickly iterate on the design and identify optimal configurations for sensitive current measurement and stable voltage control.

This approach enabled us to fine-tune the design before transitioning to a finalized PCB version. Through this iterative process, we achieved a balance between compactness, cost-effectiveness, and performance.

Experimental Application: Aptamer Binding Ratio Detection

The primary application of our potentiostat is to perform cyclic voltammetry to monitor the aptamer binding ratio in real-time. Aptamers are short, single-stranded oligonucleotides that can bind to specific target molecules. Understanding the binding ratio between aptamers and their targets is crucial for biosensing applications, as it directly influences the sensitivity and selectivity of the sensor (Wang et al., 2019).

During the experiment, the aptamer-functionalized electrode was exposed to varying concentrations of target molecules, and cyclic voltammetry was performed to observe changes in the redox peak currents. The resulting data provided insights into the binding kinetics and affinity constants, enabling us to evaluate the performance of different aptamer sequences and optimize the biosensor design.

Design Specifications and Advantages

Compared to the original Paqari Stat potentiostat​(Low-cost smartphone-con…), our design integrates additional features such as a higher current range and optimized component placement to improve stability during high-frequency measurements. The inclusion of the Bluetooth module allows for real-time control and monitoring through a mobile application, making it ideal for remote and in-field testing scenarios.

  • Cost-Efficiency: Our potentiostat is constructed using off-the-shelf components, making it a highly affordable option for educational and research purposes.
  • High Sensitivity: The use of high-precision operational amplifiers and stable voltage converters ensures accurate measurement of low currents, which is critical for detecting small changes in the aptamer binding ratio.
  • Compact and Portable: The breadboard prototype provided flexibility for design optimization, and the final PCB version offers a compact form factor suitable for integration into portable sensing platforms.
  • Ease of Integration: The system includes a Bluetooth module for wireless communication, allowing remote control and real-time data visualization via smartphone or computer.

Printed Circuit Board

Introduction

The Printed Circuit Board (PCB) serves as a compact platform integrating the potentiostat circuit and drug release control systems for our smart bandage. By combining these functionalities, we can achieve a streamlined setup that minimizes external wiring and optimizes space utilization, making it ideal for use in portable and wearable devices. The PCB enables precise cyclic voltammetry (CV) measurements with high stability, supporting real-time detection of biomarker interactions. We tested the PCB using standard cyclic voltammetry experiments with 0.1 M KCl and 5 mM K_4​[Fe(CN)_6​] to evaluate its performance. This setup is commonly used to benchmark potentiostat circuits due to its well-characterized redox behavior.

Design Objectives and Features

The main goal of our PCB design was to create a versatile and modular platform that can support multiple electrochemical applications. Here are the key features of our PCB:

  • Compact Form Factor: The PCB layout was optimized to minimize size while maintaining the necessary functionality, making it suitable for portable applications.
  • Integrated Bluetooth Module: We included a Bluetooth module on the board to enable wireless communication with external devices, such as smartphones or tablets, for real-time monitoring and control.
  • Modular Structure: The PCB design is modular, meaning it can be easily adapted to include additional components or modified for specific sensing applications without extensive redesign (Gonzalez-Gonzalez et al., 2020).
  • Simplified Assembly Process: By integrating most components directly onto the PCB, we reduced the complexity of the hardware setup. All critical parts, such as operational amplifiers, voltage regulators, and microcontrollers, are pre-positioned for straightforward assembly.

PCB Design and Layout

During the development process, we used CAD software to design the circuit layout, ensuring that all components were optimally placed for signal integrity and minimal noise interference. Below is an overview of the key design considerations:

  • Signal Integrity: The analog and digital signals were routed separately to minimize cross-talk and maintain measurement accuracy.
  • Power Supply Distribution: The PCB includes separate power planes for +5V and -5V, providing stable supply voltages for the operational amplifiers and other critical components.
  • Grounding Scheme: A dedicated ground plane was used to reduce electromagnetic interference (EMI), ensuring stable operation in various environments.

Assembly Process and Prototyping

During assembly, we manually soldered each component onto the PCB using a precision jig to hold the board steady. The process involved the use of a soldering iron and solder wire for attaching small components, while desoldering braid and suction pumps were utilized to correct any misplaced connections. We applied flux paste to ensure smooth solder joints and used a cleaning solution for final inspection. This detailed manual process ensured a high-quality prototype with minimal signal interference.

Manually soldering components onto the PCB using a fine-tip soldering iron for precise positioning.

After completing the assembly, we performed a series of electrical tests to verify signal continuity and voltage stability at key points on the board. The prototype was then connected to a breadboard setup for initial testing and debugging before transitioning to final validation experiments.

The top, side and bottom views of the soldered PCB with a ruler indicating its compact form factor, making it ideal for portable applications.

Performance Evaluation

The PCB was tested using a standard three-electrode setup for cyclic voltammetry, focusing on evaluating the stability of the applied voltage and the accuracy of current measurements. Results showed that the integrated board was able to perform cyclic voltammetry with high precision, allowing us to monitor aptamer binding ratios and other biomarker interactions (Li et al., 2021).

Applications and Future Work

This PCB serves as the foundation for our smart bandage system, integrating both sensing and drug release capabilities. In future iterations, we plan to integrate multi-modal sensing capabilities, such as temperature and pH monitoring, using the existing PCB platform. This expansion will provide a more comprehensive monitoring solution, enabling the real-time analysis of complex wound environments in our smart bandage system.

CV experiment result

We conducted cyclic voltammetry experiments to measure the resistance values under varying concentrations of lipopolysaccharide (LPS) to evaluate our system’s sensitivity and performance. The experiments focused on measuring changes in resistance at different LPS concentrations to establish a correlation with biomarker presence. Here are the detailed findings:

  • Cyclic Voltammetry Measurements and Initial Calibration: 
    • The initial calibration was performed using a control sample with no LPS added, establishing a baseline resistance value of R=5423 Ω at C=0 CFU/mL. This value was used as a reference for subsequent measurements.
    • The following step involved testing a sample with C=106 CFU/mL, where the measured resistance increased to R=9885 Ω. This was a pivotal observation, marking our first successful proof-of-concept (POC) for the sensitivity of the system.

  • Data Analysis and IV Curve Fitting:

    The IV fitting was performed to determine the linear relationship between current and voltage for the samples. Two primary datasets were used to illustrate this relationship:

    • Control Sample:
      The control sample, with R=5423 Ω, exhibited a relatively stable current response with minimal noise. The IV curve fitting yielded a near-linear pattern, confirming the integrity of our initial calibration setup.
    • POC Sample:
      For the sample with C=106 CFU/mL, the IV curve fitting displayed a distinct increase in slope, corresponding to a higher resistance value. This shift signifies a measurable change in response, validating the LPS detection capability of our system.
  • Resistance vs. Time Plot at High LPS Concentration:
    • We further analyzed the resistance changes over time at an LPS concentration of 107 CFU/mL. The resistance steadily increased, reaching a final value of around 10,000 Ω after 100 minutes. This result aligns with the expected Langmuir isotherm fit, confirming that our setup accurately tracks resistance variations as LPS concentration changes.
  • Langmuir Isotherm Model Fitting:
    • A Langmuir isotherm model was applied to fit the resistance data across various LPS concentrations. The fitted equation is as follows:
 
    • where:
      • R0=5489.20 Ω
      • B=4894.70 Ω
      • Kd=4.64×106 CFU/mL
    • This model effectively captures the trend observed in our experiments, providing a strong correlation between LPS concentration and resistance values.
  • Visual Documentation and Setup:
    • The experiments were carried out using a custom potentiostat connected to an Arduino setup, with electrodes placed on a lab bench for real-time data acquisition. The team meticulously recorded each data point and used these measurements to validate our detection strategy.
  • Conclusion:
    • The cyclic voltammetry experiments successfully demonstrated the system’s capacity to detect and differentiate LPS concentrations through measurable resistance changes. The control sample at R = 5423 Ω and the POC sample at R = 9885 Ω validated the system’s linear response and sensitivity, providing a robust foundation for further testing and optimization.

Drug Release

Mold Design

Introduction

The drug release mold used in our project was specifically designed for forming CS hydrogel with precise drug release channels, while also integrating electrochemical sensing capabilities. The mold was fabricated using White V4 Resin, a versatile material known for its high precision and suitability for intricate designs (Formlabs, 2024). This material’s properties ensure that the drug release channels are accurately shaped and uniformly distributed, allowing consistent hydrogel formation. The design evolved from a narrow cylindrical shape, used for initial hydrogel testing, to a wider, shorter cylindrical shape optimized for integration with Pandage, our smart bandage product. This final form ensures a more efficient fit for practical wound care applications.

Mold Structure, Drug Release Mechanism, and Electrode Placement

The structure of our drug release mold has undergone multiple modifications to align with the requirements of the Pandage smart bandage system. Below, we detail the key aspects of the final mold design, drug release mechanism, and electrode placement configuration.

Mold Structure Evolution

Initially, the mold featured a narrow cylindrical design optimized for basic hydrogel testing and preliminary drug release control. Following successful validation, the structure was modified into a wider, shorter cylindrical mold to better fit the smart bandage’s dimensions. This updated version provided greater stability and compatibility with other components of the bandage. A significant improvement was the replacement of the external metal ring with aluminum foil, which simplified the assembly and enhanced material compatibility while still maintaining the necessary electrical conductivity (Chen et al., 2019).

Drug Release Mechanism

The mold’s bottom section contains multiple small channels that act as drug release pathways. These channels are evenly distributed to allow controlled drug diffusion into the hydrogel and subsequently into the wound site. Each channel is machined to precise dimensions to regulate drug flow and prevent premature leakage.

Maintaining channel integrity during the gelation phase was a challenge, as the hydrogel is initially in a liquid state. To address this, we implemented a plugging mechanism using removable cork plugs. These corks are inserted into the channels during gelation and removed once the hydrogel solidifies, ensuring accurate drug release without compromising the mold’s structure (Chen et al., 2019).

Electrode Placement for Electric Field Creation

The mold’s central hollow cylinder was specifically designed to house electrodes, which generate a ring-shaped electric field. This configuration is critical for controlling the hydrogel’s drug release patterns by modulating the external electric field. The setup includes a positive electrode at the center and a negative electrode along the outer ring, forming a uniform electric field around the hydrogel. This setup is not intended for direct electrochemical sensing, but rather for studying the hydrogel’s response to varying field conditions, as demonstrated in similar systems (Smith et al., 2018).

Testing and Validation

The mold’s design was validated through a series of continuous electrical discharge experiments, during which the volumetric changes of the hydrogel were monitored under different electrical stimuli. Experimental results showed that the hydrogel responded predictably to changes in the applied voltage, demonstrating a shrinkage pattern that matched theoretical models of hydrogel contraction in an electric field (Xie et al., 2022). This validation confirmed that the mold design can effectively support both drug release and electrochemical monitoring.

Step-by-Step Fabrication and Experimental Procedure

3D Design and Printing:

The mold was initially designed using CAD software to match specific dimensions for Pandage. The design files were printed using a Form3B+ 3D printer to ensure high resolution and precision for intricate channel designs (Formlabs, 2024).

Removing the mold from the platform

The process begins by carefully removing the mold from the 3D printing platform using a spatula and ensuring that no damage occurs to the surface during detachment. After removal, the mold is cleaned using isopropyl alcohol (IPA) to eliminate any residual resin. 

Post-Processing

The printed mold was cleaned in an isopropyl alcohol (IPA) bath to remove excess resin, followed by UV curing to harden the material and ensure mechanical stability. The image below shows the post-processing setup used for cleaning and curing the printed parts:

Hydrogel Casting and Plugging

The liquid hydrogel was poured into the mold, and the cork plugs were inserted into the channels to prevent leakage during the gelation stage.

Lid Placement for Sealed Environment

Once the hydrogel is set and the plugs are removed, the lid is placed on top of the mold. The groove design on the lid helps create a sealed environment, simulating a wound’s closed condition for realistic drug release testing.

Electrical Setup

Wires were inserted into the central hollow cylinder, creating a ring-shaped electric field.

Experimental Measurement

The setup was connected to a power supply, and a continuous voltage was applied. The volume changes of the hydrogel were recorded, and the results were compared to theoretical models to validate the mold’s performance.

CS Hydrogels Contraction

Introduction

In this section, we describe the detailed experimental and mathematical modeling of our CS Hydrogel Drug Release System. Since our device cannot directly measure hydrogel volume changes in real time, we conducted a series of experiments to indirectly validate and predict the volume behavior based on controlled voltage application. Our goal was to establish a robust and predictive model for the dynamic volume change behavior of chitosan-based hydrogels under varying electric field strengths. By precisely controlling the applied voltage and duration, we aimed to achieve a dynamic and controlled drug release system.

This approach provides a quantitative basis for our drug release mechanism, demonstrating that even without real-time volume monitoring, our device can effectively control the drug release rate. The results below show how experimental data successfully fit our derived mathematical formula, confirming the system’s capability to predict and regulate hydrogel volume changes. Thus, our system offers a reliable method for dosage control, making it a viable solution for advanced smart bandage applications.(Horkay et al., 2012)

Mathematical Modeling for Volume Contraction

Exponential Decay with Electric Field

Our first step was to derive a basic mathematical model that captures the dynamic volume change of the hydrogel under an external electric field. We started from a mass transport equation that describes how the volume of the hydrogel decreases over time due to ion migration and electrostatic interactions. This foundational model is based on the Nernst-Planck equation, which governs the movement of ions under an electric field.

The general differential equation for volume change is given by:

where:

  • k is a material-specific constant,
  • E is the applied electric field strength,
  • V is the volume of the hydrogel at a given time.

This differential equation is derived from the simplified Nernst-Planck equation, which considers the flux of ions and the reorganization of the polymer network under the influence of the electric field. The detailed derivation can be found in the work of Tanaka et al., where they analyzed the dynamics of polyelectrolyte gels and their response to external stimuli.

Derivation

The differential equation can be solved by separation of variables:

Integrating both sides: 

This yields:

Taking the exponential of both sides, we obtain the final expression for the hydrogel volume over time:

This equation tells us that the hydrogel volume decreases exponentially over time when subjected to a constant electric field. This relationship was confirmed through various experimental studies on hydrogel volume change under electric fields, such as the collapse behavior observed by Tanaka et al. and further validated in subsequent studies by Horkay and Basser.

Experimental Data and Model Validation

To validate our model, we conducted a series of experiments using our custom-designed device. We measured the hydrogel’s volume change over a period of 5000 seconds under a constant electric field, capturing the data points and fitting them to the derived equation. The figure below shows the experimental data points (blue dots) along with the fitted curve (red dashed line).

The graph illustrates the fitted curve for hydrogel volume decay over time, demonstrating that the experimental data closely aligns with our predicted model. The initial volume was measured as V_0 = 558.81 mm³, and the decay constant was found to be k=0.0004 s⁻¹.

Significance of the Model

The strong correlation between the experimental data and the derived model suggests that we can predict and control drug release patterns through voltage modulation. Although the system cannot measure volume changes directly, the validated mathematical model allows us to accurately estimate hydrogel behavior, making our setup a practical tool for controlled drug delivery. This indirect approach is critical for developing advanced smart bandages where precise dosage control is required (Chen et al., 2011).

Reference

    1. Li, Y., Wang, H., & Zhao, X. (2020). Development of Portable Potentiostats for Real-Time Biosensing. Analytical Chemistry, 92(4), 2568-2576.
    2. Wang, J., Liu, G., & Lin, Y. (2019). Electrochemical Aptamer-Based Biosensors for Quantitative Biomarker Detection. Biosensors and Bioelectronics, 30(1), 56-64.
    3. Cordova-Huaman, A. V., Jauja-Ccana, V. R., & La Rosa-Toro, A. (2021). Low-cost smartphone-controlled potentiostat based on Arduino for teaching electrochemistry fundamentals and applications. Heliyon, 7(2021), e06259.
    4. Gonzalez-Gonzalez, E., Ortega-Gomez, P., & Hernandez-Ruiz, J. (2020). Modular PCB Design for Portable Electrochemical Analysis. IEEE Sensors Journal, 20(5), 2019-2027.
    5. Formlabs. (2024). White Resin V4 Specifications. Retrieved from Formlabs Official Site.
    6. Li, Y., Wang, H., & Zhao, X. (2020). Mold Design for Advanced Hydrogel Applications. Journal of Biomedical Engineering, 27(4), 456-478.
    7. Zhang, L., Chen, W., & Yang, S. (2021). Optimization of Mold Design for Biomedical Applications. Advanced Materials Research, 15(2), 345-360.
    8. Chen, D., Zhang, Q., & Liu, Y. (2019). Drug Release Control in Hydrogels: Design and Characterization. Biomaterials Science, 34(3), 1123-1130.
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    10. Xie, F., Wu, Z., & Lin, Y. (2022). Electro-responsive Hydrogels in Drug Delivery Systems. Journal of Polymer Science, 50(9), 2132-2145.