New Product Development
Projects
Formulation Science
HLD-lab | Control the emulsion space
An open innovation initiative dedicated to advancing the utilization of the HLD (Hydrophilic–Lipophilic Difference) approach in the formulation industry, enabling data-driven optimization of surfactants, emulsions, and product performance through applied research, digital tools, and industry collaboration.
HSPiPy | Hansen Solubility Parameters in Python.
HSP Desktop | Hansen Solubility Parameters GUINew
Chemometrics
SPECTRIFY | Spectral Data Analysis Made Simple
SPECTRIFY is a browser-based spectroscopy analysis platform that lets users process, visualize, and model spectral data directly in the web. Built entirely in JavaScript, it runs without installations or server-side processing, enabling fast performance, cross-platform access, and local data privacy while providing advanced signal processing and machine learning tools.
Chemometrics Plugin | Spectroscopy Analysis for Claude CodeNew
A Claude Code plugin that brings chemometrics workflows straight into the CLI: preprocessing, modeling, validation, and diagnostics for IR/NIR/Raman/ATR-FTIR/UV-Vis spectral data. Built on chemotools and scikit-learn, it ships 10 skills, 3 slash commands, and a dedicated "chemometrician" subagent for end-to-end spectral analysis.
Electrochemistry
PYVIUM | Control Ivium potentiostats with Python
A lightweight Python wrapper for the IviumSoft driver DLL, enabling programmatic control of Ivium electrochemical instruments from Python. It offers low-level device control and high-level utilities (error handling and data processing) to automate electrochemical experiments and streamline scientific workflows.
Ivium.js | Control Ivium potentiostats with NodeJS
Tiny NodeJS wrapper around the "Software development driver DLL" for IviumSoft. It provides a simple and efficient interface to control Ivium potentiostats from JavaScript, enabling automation of electrochemical experiments and integration with web applications or NodeJS-based workflows.
SINMODE | A practical approach to sinusoidal data
Machine Learning
OpenModels | Transparent Machine Learning
OpenModels is a flexible and extensible library for serializing and deserializing machine learning models (currently supporting the scikit-learn ecosystem). It's designed to support any serialization format through a plugin-based architecture, providing a safe and transparent solution for exporting and sharing predictive models.
About
Welcome to Gnpd website! I'm Alejandro Gutierrez, and I created this site to share my projects and knowledge with the community. My goal is to create practical and innovative solutions across fields such as formulation science, chemometrics, electrochemistry and machine learning.
Feel free to explore the Projects section and reach out if you have any questions or collaboration ideas. I'm always happy to connect with like-minded individuals and organizations.
Thank you for visiting, and I hope you find my work inspiring and useful!