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Chemistry IA Exemplar: Temperature and Vitamin C Concentration in… | RevisionDojo
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IB Chemistry HL Internal Assessment Example
To what extent does temperature (10 oC, 25 oC, 50 oC, 80 oC) affect vitamin C (ascorbic acid) concentration in orange juice measured by redox titration
5
Official IB Result
16/24
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16/24
0
12
24
5.1·Suggestion
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Bibliography includes diverse sources but missing access dates for online journal sources; adhere to citation standards for completeness.
Criteria A: Research Design
5/6
0
3
6
Criteria Strands
Excellent
Research question context
Good
Methodological considerations
Good
Methodology description
Criteria Feedback
The research question is framed within a clear nutritional and chemical‐stability context, linking vitamin C degradation to temperature and redox titration.
Background integrates literature effectively, establishing context in food, cosmetic, and pharmaceutical industries.
Balanced redox equation is correctly presented, reinforcing the chemical basis of the method.
Minor ambiguities in practical relevance (e.g., pasteurization temperatures) limit contextual depth.
Typographical errors in chemical notation and reagent masses (e.g., “-1.3 g iodine”, C_eH_sO_g) undermine clarity.
Procedure for repeats is inconsistent (five trials vs. three listed) and key confounders (pH, dissolved oxygen) are not considered.
Equipment details (e.g., “100 L conical flask”) and truncated safety precautions introduce ambiguity.
1.1·Strength
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The title and research question context frames the study clearly within the nutritional and stability context of vitamin C, linking temperature effects to redox titration relevance.
1.2·Weakness
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The chemical caption contains typographical errors (C_e H_s O_g), undermining clarity; ensure precise chemical notation consistent with formula discussed.
1.3·Strength
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Background section integrates literature effectively, establishing clear context for vitamin C importance in food, cosmetic, and pharmaceutical industries.
1.4·Strength
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The balanced redox equation is correctly presented, reinforcing the chemical basis of the titration method.
1.5·Suggestion
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Consider specifying the practical relevance of the selected temperature range (e.g., pasteurization or storage conditions) to strengthen the aim’s context.
1.6·Weakness
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The list item displays '-1.3 g of iodine' instead of '1.3 g', indicating a typographical error; correct values to avoid confusion in reagent preparation.
1.7·Weakness
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Controlled variables are well listed, but the student hasn’t addressed key confounders like pH and dissolved oxygen, which could influence vitamin C degradation.
1.8·Weakness
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Procedure includes an erroneous '100 L conical flask' and lacks heating time details, introducing ambiguity that could affect reproducibility.
1.9·Weakness
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Safety table entries are truncated and misaligned, lacking complete 'Precautions' details; ensure full safety instructions are presented.
1.10·Weakness
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Procedure for repeats is inconsistent: initial variables mention five trials per temperature, but step 10 lists only three repeats, risking data inconsistency.
Criteria B: Data Analysis
3/6
0
3
6
Criteria Strands
Good
Communication of data recording and processing
Moderate
Consideration of uncertainties
Poor
Data processing quality
Criteria Feedback
Clear tabulation of raw and processed data with units and inclusion of error bars.
Effective reporting of Pearson’s r and the regression equation, quantifying the temperature–concentration relationship.
Fundamental stoichiometric calculation errors (volume divided by molar mass) lead to major inaccuracies in concentration values.
Uncertainty propagation is oversimplified, using only the largest burette error and omitting combined effects.
Formatting glitches (mis-labelled molarity, extraneous symbols) detract from precision.
2.1·Suggestion
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Enhance qualitative section by including systematic observations or photographs for each temperature to support visual end-point consistency.
2.2·Question
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The 80°C trial 5 value (15.00 mL) is a clear outlier but isn’t addressed; how was this anomaly considered in data processing?
2.3·Weakness
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Table heading mislabels concentration as 0.005 mol/L⁻¹ and includes extraneous √ symbols, reducing precision in communication of data.
2.4·Weakness
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The stoichiometric calculation uses volume divided by molar mass rather than applying n=cV, leading to major inaccuracies in moles of iodine.
2.5·Weakness
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The concentration calculation appears inconsistent with prior moles and volume values; ensure units and decimal accuracy when computing c=Vn.
2.6·Strength
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Calculation and clear reporting of Pearson’s r=−0.91 effectively quantify the strong negative relationship between temperature and vitamin C concentration.
2.7·Suggestion
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When propagating instrument uncertainties, combine both volumetric pipette and titrant uncertainties rather than isolating one to reflect total measurement error.
2.8·Weakness
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The choice to propagate only the highest percentage uncertainty from burette readings omits variation across trials and underestimates the combined uncertainty.
2.9·Weakness
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Assigning a constant percentage uncertainty for all concentrations overlooks that temperature and volume variation could change relative uncertainties.
2.10·Strength
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Reporting the regression equation and R2=0.83 quantifies the rate of change and explanatory power, enhancing the quantitative analysis.
Criteria C: Conclusion
4/6
0
3
6
Criteria Strands
Moderate
Conclusion relevance and support
Good
Scientific context comparison
Criteria Feedback
Conclusion refers directly to processed data (gradient, r-value) to describe the negative relationship.
Trend explanation is linked to oxidation kinetics and Arrhenius behaviour with literature support.
Reliance on flawed data calculations reduces full consistency and justification.
Lacks quantitative backing (e.g., statistical tests or confidence intervals) to strengthen claims about optimal temperature.
3.1·Suggestion
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The conclusion highlights 25 °C as optimal without quantifying significance; consider statistical tests or confidence intervals to support this claim.
Criteria D: Evaluation
4/6
0
3
6
Criteria Strands
Moderate
Methodological weaknesses
Moderate
Suggested improvements
Criteria Feedback
Identifies specific methodological weaknesses (endpoint detection, storage degradation) and links them to result accuracy.
Suggests realistic improvements (dilution for clearer endpoint, faster trial completion) that target identified issues.
Does not quantify the relative impact of each weakness on the overall uncertainty.
Descriptions of improvements lack detail on how much accuracy or precision would improve.
4.1·Strength
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Identifies endpoint detection as a major limitation affecting accuracy, demonstrating awareness of methodological weaknesses.
4.2·Weakness
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Identifies juice degradation during storage but lacks quantification of how storage time influenced data, weakening evaluation depth.