Materials, experiments, and data
Explore chemistry through material preparation, laboratory measurements, and data analysis, with questions about semiconductor materials, electrochemistry, and energy.
Chemistry • Materials • Data
The question underneath the device
Introduction
Connect a physical observation with data.
This collection brings together chemistry projects, data analysis, and questions about functional materials.
The projects connect experimental methods with observations and interpretation. Unexpected results can suggest new questions, while the limits of the evidence remain part of the explanation.
Materials chemistry
Laboratory measurements
Data analysis
Group course project
How do sweet-potato-peel mass, grind size, and incubation time affect the adsorption of methylene blue from water?
This group course project used boiled and dried sweet-potato peels, compared coarse and fine samples, varied adsorbent mass, and tested 30- and 60-minute incubation periods. The project brought material preparation, instrumental measurements, data organization, scientific interpretation, poster design, and oral presentation into one investigation.
Methods and tools
Project workflow
The group project combined experimental work, measurements, and scientific communication:
Interpreting the findings
Greater adsorbent mass and longer incubation generally improved removal. Coarse particles, however, unexpectedly outperformed fine particles under several conditions.
The initial interpretation compared larger pieces to larger sponges. However, fine particles should ordinarily expose more surface area and binding sites.
Aggregation of fine particles during incubation could reduce the surface accessible to the dye. That remained a possible explanation, not a demonstrated mechanism. SEM images appeared to show greater porosity in the fine material, which made the result more interesting rather than resolving it.
Current direction
Semiconductor devices raise questions about the chemistry of wafers, thin films, purity, processing, interfaces, and defects.
Everyday electronic devices offer a starting point for questions about the materials and processes that influence speed, heat, and energy use.
How do material choice, purity, processing, interfaces, and defects influence the quality and efficiency of semiconductor devices?
Wafer growth, thin films, material purity, defects, and interfaces affect manufacturing yield and device quality. Quality control also raises questions about the costs and benefits of further defect reduction.
How do processing choices and quality control shape performance and yield?
Electrochemical materials connect chemical reactions with the storage and conversion of energy.
How do material properties influence energy storage and conversion?
Coursework projects
These coursework examples use SQL and R to explore patterns while considering context and limits.
Guided coursework
23,335 games · 2004–2020 seasons
This analysis used SQL to examine scoring patterns, home-team win rates, and the association between three-point accuracy and team success.
A narrow explanation has limits: defense, rebounding, free throws, and broader scoring efficiency would be needed for a fuller account of team success.
Statistical coursework
Class-collected dataset
This coursework analysis used R to examine a class-collected dataset on hissing-cockroach thermoregulation.
The analysis examined ambient versus body temperature, distribution among warm, mild, and cool zones, and differences between live and model cockroaches.
Methods and tools
The projects draw on laboratory measurements, quantitative analysis, and scientific communication.
Laboratory work includes following procedures, preparing materials and solutions, making measurements, and recording observations.
Acid–base titration
Liquid–liquid extraction
Thin-layer chromatography (TLC), including developing and interpreting TLC plates
Melting-point determination
Mixed melting-point analysis
Solution preparation and general laboratory measurement
The examples use SQL for the basketball dataset, R for thermoregulation data, and Excel for the adsorption project’s results.
SQL for filtering, grouping, joining, and analyzing datasets
Python
R for statistical analysis
Quantitative data analysis
Scientific communication connects laboratory notes and group results with written methods, conclusions, posters, and presentations.
Microsoft PowerPoint
Scientific and academic presentations
Research-based writing
Communicating technical information clearly