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Best AI Prompts Directory for Science & Engineering

AI Prompts

Simply the Biggest AI Prompts Directory Specialized in Product Design and Innovation

Ai prompts for product design
A comprehensive ai prompts directory designed to enhance product design, engineering, and innovation through optimized data processing and solution generation.

Welcome to the world’s largest AI prompts directory dedicated to advanced product design, engineering, science, innovation, quality, and manufacturing. While online AI tools are rapidly transforming the engineering landscape by augmenting human capabilities, their true power is unlocked through precise and expertly crafted instructions. This comprehensive directory provides you a collection of such prompts, enabling you to command AI systems that can process vast amounts of data, identify complex patterns, and generate novel solutions far more efficiently than traditional methods.

Discover and fine tune the exact prompts needed to leverage online AI agents for optimizing your designs for peak performance and manufacturability, accelerating complex simulations, accurately predicting material properties, and automating a diverse range of critical analytical tasks.
The advanced search filters allow fast access to this extensive directory and cover the full spectrum of modern engineering.

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AI Prompt to Hypothesize Causes of Catalyst Deactivation

This prompt generates plausible hypotheses explaining catalyst deactivation based on a list of observed symptoms such as activity loss, selectivity change, and physical catalyst changes. The AI provides a ranked list of hypotheses with brief mechanistic explanations.

Output: 

AI Prompt to Generate C++ Code for Heat Exchanger Design

This prompt generates a C++ program to calculate heat exchanger parameters such as heat transfer rate, log mean temperature difference, and required surface area based on user inputs of fluid temperatures, flow rates, and heat capacities. The code is fully commented and ready for compilation.

Output: 

AI Prompt to Generate Python Code for Reaction Rate Calculation

This prompt creates a Python script to calculate reaction rates based on Arrhenius kinetics, given user inputs of activation energy, pre-exponential factor, temperature, and concentration. The code includes comments and example usage, facilitating integration into chemical engineering workflows.

Output: 

AI Prompt to Create Catalyst Property Variations

This prompt generates plausible variations of catalyst property data given an initial dataset in CSV format. It outputs an augmented CSV with new catalyst entries created by applying small random perturbations to surface area, pore volume, and metal loading, useful for training robust machine learning models.

Output: 

AI Prompt to Retrieve Safety and Hazard Statements

This prompt scans a provided chemical process description or material safety document to extract all safety and hazard statements, including precautionary measures and hazard codes. The output is a bullet-point list in plain text for easy review and compliance checks.

Output: 

AI Prompt to Identify Catalyst Types and Properties

This prompt identifies all catalyst types mentioned in a given technical document excerpt and extracts their key properties such as surface area, pore volume, and active metal loading. The AI outputs a neatly formatted markdown table listing each catalyst and its properties to facilitate catalyst comparison and selection.

Output: 

AI Prompt to Extract Experimental Parameters from Text

This prompt extracts key experimental parameters such as temperature, pressure, catalyst type, and reaction time from a provided unstructured text excerpt of a chemical engineering report or paper. It outputs a structured JSON summarizing each parameter with its value and units, helping engineers quickly gather critical data without manual reading.

Output: 

AI Prompt to Troubleshoot Packed Column Flooding

A packed distillation or absorption column is experiencing flooding. Given column internals packing type fluid properties (gas/liquid loads) operating conditions and observed symptoms (e.g. high pressure drop entrainment poor separation) this prompt asks the AI to suggest causes and diagnostic checks.

Output: 

AI Prompt to Simulate Reactor SteadyState Yields

This prompt generates a conceptual table of expected product yields from a specified chemical reactor type under varying steady-state operating conditions (e.g. temperature pressure catalyst concentration). It relies on general chemical engineering principles and user-provided qualitative impact of conditions. This is for conceptual design or educational exploration not rigorous simulation.

Output: 

AI Prompt to Extract Catalyst Performance Data URL

This prompt tasks the AI with scraping a given URL of a research article or patent that discusses catalysis. It should identify and extract specific data related to catalyst performance such as conversion selectivity turnover number (TON) turnover frequency (TOF) and reaction conditions under which these were achieved for a named catalyst or reaction system. The output is a JSON object.

Output: 

Table of Contents
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    Topics covered: test prompts, validation, user input, data collection, feedback mechanism, interactive testing, survey design, usability testing, software evaluation, experimental design, performance assessment, questionnaire, ISO 9241, ISO 25010, ISO 20282, ISO 13407, and ISO 26362..

    1. No one discussing the potential bias in AI selection for these directories? AI isnt immune to prejudices, folks.

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