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CuxS Nanopowder — Phase Prediction & Synthesis Optimization

Thermodynamic (Equilibrium Diagram) modelling and statistical analysis to optimize microwave-assisted synthesis of copper sulfide nanopowders — identifying synthesis temperature as the dominant factor governing phase composition and electrical conductivity.

Used an open-source thermodynamic database to predict the phase composition of CuxS nanopowders from varied precursor ratios and synthesis conditions, then validated predictions against experimental results across three temperatures (200, 220, 250°C) and three microwave durations (2, 5, 10 min). Statistical analysis in Excel of SEM imaging and electrical conductivity measurements revealed that synthesis temperature was the primary driver of phase and purity — with 250°C results aligning most closely with the 25°C thermodynamic predictions. A key finding was that purity governed electrical conductivity more strongly than phase, while phase composition remained critical for determining thermoelectric application suitability.

Year
2020
Category
Machine Learning
Tags
PythonExcelStatistical AnalysisThermodynamic ModellingEDAMaterials InformaticsSEM AnalysisExperimental Design
CuxS Nanopowder — Phase Prediction & Synthesis Optimization
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