⚙️ Lab Automation — Turning Months of Manual Work Into Minutes

Between May and July 2025, I built something that completely changed the way I work in the lab.

I started with no formal coding experience — just curiosity and determination.
By the end of the summer, I had automated the data processing for most of our lab machines, cutting hours of repetitive work down to a few minutes.

Now, what once took me two hours — downloading raw data, formatting Excel tables, calculating results, plotting graphs — takes just 3–5 minutes.
And it all began in Thonny, the simplest Python IDE I could find.

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🧩 The Research Script Launcher

This app is called the Research Script Launcher.
Each tile represents a piece of lab equipment — and behind each one lies a full Python workflow I built from scratch.

• HOH — Heat of Hydration (Isothermal Calorimetry)
• TGA — Thermogravimetric Analysis
• Load-CMOD — Flexural Test Analysis
• Compression — Compressive Strength Curves
• XRD, FTIR, UV-Vis, Raman, Zeta, and others

Click a button → The corresponding Python script runs → You get a clean, formatted Excel file and pre-styled graphs ready for PowerPoint.

No more copying formulas. No more replotting data.
Just one click.

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🔬 How It Works (Simplified)

Each Python script:

1. Reads raw data files from the machine (CSV, TXT, or Excel).
2. Processes the data — cleaning, calculating, and normalizing values.
3. Generates formatted Excel workbooks with multiple sheets:
   • Raw Data
   • Calculated Parameters
   • Mechanical Results
   • Averages and Summaries
4. Creates publication-ready graphs (colors, fonts, legends, etc.).
5. Opens the Excel file automatically once done.

Everything follows the same structure — modular, consistent, and human-proof.

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💡 The Learning Curve

When I began in May 2025, I didn’t know how to write a function or a for-loop.
I just knew what I wanted: automation.

Through trial, error, and a lot of late nights, I built code that:

• Communicated directly with instruments via PyVISA
• Generated Excel files with Pandas and XlsxWriter
• Designed GUIs with CustomTkinter
• Integrated plotting and formatting standards across projects

I wrote thousands of lines of code — and broke them thousands of times too.
But by July, everything clicked.

That feeling when the automation finally works?
Unmatched.

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🧠 Example Script — Simplified

Below is a simplified conceptual example of how one script works (not the actual code):

def run_experiment(device):
    for freq in frequencies:
        r, x = device.measure(freq)
        data.append((freq, r, x))
    save_to_excel(data)
    plot_graphs(data)

Simple to look at, complex to perfect. Each instrument required its own communication protocol, timing logic, and output structure — all unified through one design philosophy: make it effortless. 🧮 Time Saved = Time for Research

Before automation:

2 hours per dataset

Multiple Excel sheets

Manual chart editing

Risk of human error

After automation:

3–5 minutes per dataset

Fully formatted workbook

Ready-to-insert graphs

0% manual error

That’s not just convenience — that’s time reinvested into discovery. 🧰 The App Ecosystem

This launcher connects everything:

HOH Analyzer → Reads calorimetry data and plots heat flow curves

TGA Processor → Normalizes weight loss and derivative curves

Load-CMOD Tool → Calculates flexural toughness, strength, and FCS

Vicat Timer → Automates setting time reports

Compression & Modulus Analyzer → Calculates strength/modulus, averages, and deviations

Photo Scraper → Captures experimental photos for archiving

To-Do Module → Personal research task tracker

Every machine, every dataset — unified under one interface. 🎓 Reflection

As a civil engineering PhD student, this was more than coding. It was system design — applied to scientific research.

Every hour spent debugging Python is now saving me dozens each month. And most importantly, it’s freeing me to focus on what really matters: understanding materials, not spreadsheets. 🚀 The Bigger Picture

This experience opened my eyes to how much manual academic work can be automated — especially in experimental research.

Data collection, cleaning, and graphing shouldn’t consume entire afternoons. Automation gives researchers their time back.

Eventually, I plan to expand this into:

Automated PowerPoint generation for reports

Integration with Google Sheets dashboards

Cross-lab usage tracking and data archiving

🔗 Related Posts

This article is part of the LifeLoggerz Automation Series:

1️⃣ Lab Automation — Turning Months of Manual Work Into Minutes (this post) 2️⃣ How My Load-CMOD Script Calculates Flexural Toughness

3️⃣ Automating Heat of Hydration and TGA Reports

4️⃣ Creating GUI Apps with CustomTkinter

5️⃣ Integrating Python with Google Sheets

“Automation doesn’t replace researchers — it amplifies them.” — LifeLoggerz

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