Electrochemical Insights Weekly
Empowering Innovators in Electrochemistry and Biosensing
2nd edition
Welcome to the second edition of Electrochemical insights Weekly! Your go-to resource for valuable, actionable insights into the world of electrochemistry and biosensing. Whether you're an industry professional, an academic researcher, or simply passionate about cutting-edge biosensing technologies, there's something here for you.
This week's edition is packed with resources to help you level up your career, your skills, and your research impact. 🚀
Continue reading to get access to a custom LaTeX CV template designed to help you stand out in this competitive job market. You will also receive a powerful python script that makes it easy to create clean, professional, and publication ready calibration plots with multiple regression models to choose from. Or perhaps you are looking for some benchmark cyclic voltammetry data? Check out the public repository below where users can access real cyclic voltammetry data from real experiments. This is perfect for training new students or even machine learning applications!
🚀 Stop Guessing: Use DoE to Build Biosensors more Efficiently 🚀
If you’re still optimising your biosensor by changing one variable at a time, you might be wasting more time and resources than you think.
There’s a better way.
Design of Experiments (DoE) allows you to test multiple factors at once, understand how they interact, and focus on the changes that actually matter. By doing so, you can drastically reduce experimental workload while unlocking deeper insights about your system.
Here’s what you’ll learn:
✅ How to Set Up a DoE for Your Biosensor
✅ The Difference Between Full Factorial & Fractional Factorial Designs
✅ How to Use the 2^(k-p) Design to Reduce Workload & Save Time
✅ Example calculations & 3 Python Scripts to Generate, Analyse, and Visualize Your DoE
Continue reading here
The global materials science sector is experiencing explosive growth, with research output increasing by 25% from 2019 to 2023, according to the latest Nature Index data. While China leads in sheer volume, other countries like South Korea, India, Singapore, Italy, and Denmark are driving innovation in critical areas such as solar cells, sustainable materials, and advanced composites.
For investors and innovators, these trends highlight clear opportunities for growth. South Korea, for instance, is leading in perovskite solar cells, a key technology for renewable energy. India’s rapid rise in research output positions it as a potential hub for cost-effective R&D partnerships. Meanwhile, Denmark is focusing on recyclable materials for wind turbine blades, which aligns with the global push toward sustainable technologies.
Thinking of investing? The following tickers may allow you capture some of this growth: LG Chem (KRX: 051910), Hanwha Solutions (KRX: 009830), Tata Chemicals (NSE: TATACHEM), Hindustan Zinc (NSE: HINDZINC), Nanofilm Technologies (SGX: MZH), ST Engineering (SGX: S63), ENI S.p.A. (BIT: ENI), Saipem (BIT: SPM), Vestas Wind Systems (CPH: VWS).
Read more about this growth on Nature Index.
Investments are inherently risky, and you should consult a licensed financial advisor before making any investment decisions. Invest at your own risk.
This week, I’m sharing a powerful Python script that makes it easy to create clean, professional calibration plots for biosensing data. No more struggling with inconsistent plots or mismatched trendlines. This script does it all!
Key Features
Fits Multiple Curve Types:
Linear (for simple systems)
Power (for diffusion-limited responses)
Sigmoidal (for receptor-ligand interactions)
Langmuir (for surface adsorption)
Exponential (for chemical amplification)
Hill Equation (for cooperative binding)
Automatic Equation & R² Calculation: For each curve, you’ll see the equation and the R² value to help you decide which model fits best.
Customisable Plot Design: Easily change plot size, colours, and line styles to match your brand or publication style.
Summary Table of Models: After running the script, you’ll get a summary table with the model name, fitted equation, and R² value — so you know exactly which model fits your data best.
How It Works
Input Your Data: Change the data dictionary to your own concentration and signal data.
Run the Script: The script fits multiple models and plots them all on one graph, making it easy to see which one fits your data best.
Review the Summary Table: See which model has the best R² value and use the equation to describe your system.
Clean It Up: Remove the other models which do not describe your data and you will be left with a professional calibration plot.
Check out the link to this resource below.
Before looking at this week's career opportunities, it might be worth considering if your current CV is up to scratch. A well-crafted CV can be the difference between getting noticed or getting overlooked. To help you stand out in a competitive job market, I’m sharing a customisable LaTeX CV template designed with researchers, scientists, and technical professionals in mind.
With this sleek, professional design, you’ll be able to:
Highlight key skills and technical expertise using easy-to-read, visually appealing layouts.
Showcase your publications and projects in a way that impresses hiring managers and PIs.
Make a lasting impression with icons, clean formatting, and a design that stands out.
No LaTeX experience? No problem. The template is easy to edit, all you need to do is swap out the placeholder text with your own details.
I recommend using Overleaf, a free and user-friendly online LaTeX editor, to edit and generate this document. Simply copy and paste the LaTeX code (provided below) into the Overleaf code editor, input your own information, and hit 'Compile' — watch your professional CV come to life in seconds!
Check out the link to this resource below.
Soft Robots with Integrated Sensing, On-Demand Therapy and AI-assisted Control
Location: United Kingdom - University of Southampton - Faculty of Engineering and Physical Sciences
Funding: Competition-based studentships offered by our schools typically cover UK-level tuition fees and a stipend for living costs (minimum of £19,237 in 2024-25), there is a range of funding opportunities for both UK and international students - Horizon Europe fee waivers automatically cover the difference between overseas and UK fees for qualifying students.
Presidential Bursaries from the University can also cover the different between overseas and UK fees.
Deadline: n/a Start date: 31/07/25
Requirements: Two references, Academic transcripts/degree certificates, Personal statement, Curriculum vitae (CV), English language certificates (if applicable).
Apply on the University of Southampton website
Post-doc in homogeneous catalysis and molecular electrochemistry H/F
Location: France - Ile-de-France, Essonne (91) - The French Alternative Energies and Atomic Energy Commission (CEA)
Salary: n/a
Start date: 06/01/2025
Requirements: The applicant must hold a PhD in molecular chemistry with an experience in catalysis and/or electrochemistry. Must be fluent in English.
If you’re working with cyclic voltammetry (CV), this week’s featured resource is a must-have tool. EChemDB is a public, open-access repository that provides real experimental CV data from a variety of electrochemical systems (different combinations of electrodes and electrolyte solutions).
This resource is incredibly useful for:
Training new researchers: Use real-world CV data to teach students how to interpret voltammograms.
Machine learning applications: Access large datasets for training AI models to recognize patterns in CV data.
Method validation: Compare your own experimental results against benchmark data from established sources.
With the growing demand for open scientific data, EChemDB provides a reliable source of free, high-quality CV datasets. It’s a valuable tool for anyone working in electrochemical analysis, biosensing, and sensor development.
Advances in Electrochemical Biosensors: Types, Applications, and the Role of Machine Learning
This week's featured paper offers a comprehensive overview of electrochemical biosensors. Showcasing their applications in health care, food safety, defence, and even plant biology. By detecting biomarkers in body fluids like sweat, blood, and urine, these sensors offer non-invasive diagnostic capabilities. The authors emphasize how advances in immobilisation techniques and transducer design have led to more efficient and sensitive biosensors.
One of the most exciting areas of development is the role of machine learning (ML) in biosensing. As biosensors generate vast amounts of complex data, ML algorithms help filter out noise and distinguish target signals from contaminants, leading to more accurate and reliable detection. This review explores how machine learning is transforming data analysis in biosensing, especially in cases where manual interpretation would be too labour-intensive.
The paper also identifies key challenges in the field, including sensor fouling, contamination, and the need for improved sensitivity. It provides valuable insights for researchers and engineers looking to develop next-generation biosensing platforms, making it an excellent resource for those entering the field.
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The premium section of this week’s newsletter contains 3 additional PhD positions, exclusive access to the Python code for the calibration plot, the LaTeX code for the CV template, and a spotlight on a powerful online resource for spectral analysis of Organic Compounds. This database is a game-changer for anyone working with functionalised electrode coatings or identifying unknown organic compounds, offering free access to NMR, IR, UV, and mass spectra for thousands of molecules.
Paid subscribers also get to ask questions, make suggestions, and receive personalised guidance on topics related to electrochemistry and biosensing. You'll play an active role in shaping the content, ensuring it aligns with your needs and interests. Your support keeps this newsletter running and helps deliver even more valuable insights to the community.