co2rr-selectivity

Analyze CO2 reduction reaction intermediates and branching pathways to predict product outcomes.

181|20|Updated Apr 29, 2026

Search Tags:#mcp-workflow #copper #electrochemistry #intermediates #selectivity #gibbs-energy #co2rr

Ready to run

npx skills add https://github.com/Hello-QM/catgo-LRG --skill co2rr-selectivity

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: co2rr-selectivity
Source: https://github.com/Hello-QM/catgo-LRG/tree/main/.claude/skills/co2rr
Command: npx skills add https://github.com/Hello-QM/catgo-LRG --skill co2rr-selectivity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

CO2 reduction reaction selectivity analysis helps researchers predict product outcomes from CO2 electroreduction by mapping key intermediates and branching pathways to distinguish between CO, methanol, methane, and formic acid products.

Core Features & Use Cases

Map CO2RR intermediates and branching pathways to identify major product routes.
Provide selectivity descriptors, recommended checks, and pH/solvation considerations for reliable predictions.
Use Case: A catalyst researcher can compare energy differences between key intermediates (e.g., *COOH vs *OCHO) to anticipate CO vs formic acid or methanol formation.

Quick Start

Analyze a target CO2RR product to outline the required intermediates, descriptors, and computational steps for selectivity evaluation.

Frequently Asked Questions about co2rr-selectivity

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I predict CO2 reduction reaction selectivity for different products?▼

CO2 reduction reaction selectivity is predicted by analyzing energy differences between key intermediates like *COOH and *OCHO to map branching pathways and distinguish between CO, methanol, methane, and formic acid product outcomes.

How does analyzing CO2RR intermediates help distinguish between CO and formic acid formation?▼

Analyzing CO2RR intermediates distinguishes products by evaluating branching pathways where the *COOH intermediate typically leads to CO or methanol, while the *OCHO intermediate directs the reaction pathway toward formic acid formation.

What computational settings and checks are recommended for CO2RR selectivity analysis?▼

Recommended computational settings for CO2RR selectivity analysis include enforcing consistent parameters, applying dipole corrections, and accounting for solvation effects and pH considerations to ensure reliable product outcome predictions.

Can I use an MCP workflow for electrochemistry CO2RR computational studies?▼

Yes, you can use an MCP workflow for CO2RR computational studies; the analysis provides a complete MCP workflow example that guides computational steps, enforces safe defaults, and helps avoid common pitfalls in electrochemistry research.

What are common pitfalls when calculating Gibbs energy for CO2RR intermediates?▼

Common pitfalls when calculating Gibbs energy for CO2RR intermediates include neglecting solvation effects, omitting dipole corrections, and applying inconsistent computational settings, all of which compromise selectivity descriptor accuracy.

Does copper catalyst CO2RR selectivity require specific pH and solvation considerations?▼

Yes, copper catalyst CO2RR selectivity requires specific pH and solvation considerations to accurately evaluate selectivity descriptors and predict reliable product outcomes under varying electrochemical environments.