01
IMPORTDrop in the data
Upload individual CSV or Excel files, or feed an entire folder of supported CV datasets into the analysis workflow.
02
PROJECTS / BIOLOGY? CHEMISTRY? APPARENTLY YES.
Wait...
this isn't cybersecurity.
Correct. I checked twice. Somehow a security researcher ended up building a cyclic voltammetry analysis application for a biomedical research project. Long story. Useful tool.
DOMAINBIOMEDICALCONFUSIONHIGHSTATUSWORKING
01
CV ANALYSIS STUDIO / THE SCIENCE DETOUR
ACTIVEv0.2.2PYTHON / STREAMLIT
TRFFN / CV ANALYSIS STUDIO
A friend needed help.
I somehow learned electrochemistry.
CV Analysis Studio is a Streamlit application built to automate repetitive cyclic voltammetry data analysis for a biomedical research workflow.
This project started because a friend needed a better way to process experimental CV datasets. The workflow involved locating specific electrochemical markers, calculating several derived values, reviewing questionable detections, and producing a clean Excel result.
None of this was remotely related to my usual work in offensive security. Naturally, I built the application anyway.
The result is a small analysis studio that accepts experimental data, performs automatic marker detection, lets the researcher verify or correct the results, calculates the required values, and exports everything into a final workbook.
CV_ANALYSIS.STUDIOSCIENCE DETECTED
PYTHON / STREAMLITVIEW SOURCE ↗
02
CAPABILITIES / WHAT THE SCIENCE MACHINE DOES
From raw curves
to usable numbers.
The goal was simple: remove repetitive manual processing without removing the researcher from the decision-making process. Automation handles the boring part. Humans still verify the science.
02
DETECTFind the important points
Automatically identifies FBC, APC, BBC and CPC markers from cyclic voltammetry data, with a review threshold for uncertain detections.
03
REVIEWHuman still gets a vote
Detected markers can be reviewed and manually adjusted before any final calculation or export is accepted.
04
EXPORTExcel goes in. Better Excel comes out.
Calculated IPA, IPC, IPA/IPC and ΔE values are assembled into a clean workbook ready for the next stage of research.
03
WORKFLOW / FROM FILE TO RESULT
Upload.
Detect. Review. Export.
The application turns a repetitive analysis workflow into a guided pipeline while preserving manual review for detections that need a human eye.
IMPORT
Upload the experimental data
Load individual CSV, XLS, XLSX or XLSM files, or upload a folder containing multiple supported datasets.
files → workspaceANALYZE
Detect the CV markers
The application analyzes the data and identifies FBC, APC, BBC and CPC marker positions automatically.
curve → markersVERIFY
Review before trusting
Researchers can inspect the detected points and manually adjust them whenever automatic detection needs correction.
automation + humanCALCULATE
Generate the electrochemical values
Once the marker positions are accepted, the application calculates IPA, IPC, IPA/IPC and ΔE.
markers → metricsEXPORT
Send it back to Excel
Final reviewed results are exported into a structured Excel workbook for the rest of the research workflow.
metrics → .xlsx04
MATH / YES, I ACTUALLY HAD TO IMPLEMENT THIS
Security guy
meets electrochemistry.
These are the derived values generated from the reviewed CV markers. At some point during development I stopped asking why I knew what APC and CPC meant and simply accepted my new life.
01IPA
APC − FBC02IPC
BBC − CPC03IPA / IPC
IPA ÷ IPC04ΔE
EPA − EPC05
FIELD NOTES / HOW DID WE GET HERE?
This was not on the roadmap.
The project was built for a friend's biomedical research workflow. Apparently "can you help me automate this?" is all it takes to move me from vulnerability research into electrochemistry.
The app suggests. The researcher decides.
Automatic marker detection is useful, but experimental data is experimental data. Review and manual correction remain part of the workflow instead of pretending automation is infallible.
Code does not care what your field is.
Whether the input is an HTTP request or an electrochemical curve, the pattern is surprisingly familiar: understand the data, identify the important states, validate assumptions, and automate the boring part.
06
STACK / WHAT HOLDS IT TOGETHER
Surprisingly normal
software underneath.
The scientific domain changed. The engineering problem did not: ingest data safely, make state visible, allow correction, calculate deterministically, and produce something useful.
LANGUAGE
Python
Core analysis, marker detection, calculations and export logic.
pythonINTERFACE
Streamlit
Provides the browser-based analysis interface and hosted workflow.
streamlitINPUT
CSV + Excel
Supports CSV, XLS, XLSX and XLSM datasets used by the research workflow.
csv / xls / xlsxOUTPUT
Structured workbooks
Final marker values and calculated results are returned as an Excel workbook.
analysis → excelCYBERSECURITY RESEARCHER / TEMPORARILY LOST IN SCIENCE