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Chemistry IA Exemplar: NaOH Concentration and Neutralisation Enthalpy | RevisionDojo
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IB Chemistry HL Internal Assessment Example
How do different concentrations (0.0500 M, 0.100 M, 0.250 M, 0.500 M, 1.00 M) of
NaOH(aq) affect the temperature change of the neutralisation reaction with excess
0.500 M HCl(aq) at room temperature using calorimetry, which was then used to
determine the amount of Gibbs free energy in kJ mol-1?
5
Official IB Result
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14/24
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12
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5.1·Strength
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The balanced chemical equation for the neutralisation reaction is correctly presented, demonstrating foundational understanding of the reaction stoichiometry.
5.2·Strength
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Figure 1: Calorimeter setup is clearly depicted, offering useful visual context for the experimental apparatus.
5.3·Strength
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The energy profile diagram effectively illustrates reaction exothermicity; however, citing numerical activation energies would heighten analytical depth.
5.4·Suggestion
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The hypothesis predicts a negative correlation, but clarifying the theoretical basis—such as how ΔH and ΔS interplay with concentration—would strengthen its scientific grounding.
5.5·Strength
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Figure 4 conveys ΔG trends with error bars clearly, aiding visual interpretation of processed data.
Criteria A: Research Design
5/6
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3
6
Criteria Strands
Excellent
Research question context
Good
Methodological considerations
Good
Methodology description
Criteria Feedback
Research question placed in a specific industrial wastewater context
Key variables identified and apparatus uncertainties tabulated
Procedure detailed enough for reproduction with minimal ambiguities
Calorimeter heat capacity and insulation losses are not addressed
Rationale lacks quantitative data or references to expected energy savings
Some methodological considerations (e.g., calibration procedure, stirring rate) remain unexplained
1.1·Weakness
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The introduction effectively frames wastewater neutralisation; however, it would benefit from citing specific case studies or quantitative impact metrics to provide a stronger link between Gibbs free energy and cost‐effectiveness.
1.2·Weakness
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The research question is situated within a specific industrial wastewater context, but the rationale could be strengthened with quantitative data or references to expected energy savings to deepen justification.
1.3·Weakness
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The formula Q = mcΔT is correctly stated, but the method omits consideration of the calorimeter’s own heat capacity and heat losses, which are critical for accurate enthalpy measurements.
1.4·Weakness
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Dependent variable defined as ΔT, yet the protocol omits details on thermometer calibration, response time, or stirring rate, which are needed for reproducible temperature measurements.
1.5·Weakness
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Listing entropy change as a control variable ignores that ΔS may vary with concentration; this assumption could introduce systematic bias.
1.6·Weakness
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The step-by-step procedure is clear, but key details (calorimeter insulation, stirring rate, exact timing method) are missing, affecting reproducibility.
Criteria B: Data Analysis
4/6
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3
6
Criteria Strands
Excellent
Communication of data recording and processing
Moderate
Consideration of uncertainties
Poor
Data processing quality
Criteria Feedback
Data tables and graph presentation are clear, well labelled, and include error bars
Units and significant-figure consistency are mostly respected
Communication of data processing is generally precise
Key uncertainty sources (calorimeter heat capacity, mass measurements) are omitted
Propagation of uncertainties is oversimplified and arithmetic errors are present
Statistical methods (e.g.
Pearson’s correlation) are misapplied given data non-linearity
2.1·Weakness
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Assuming a constant ΔS neglects potential concentration effects; while simplifying, this overlooks systematic variation—a significant omission when interpreting ΔG across varying reactant amounts.
2.2·Weakness
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The calculation ΔS = (115.5 + 69.9) – (56.5 – 49.8) seems to subtract incorrect sum; reactant entropies should be summed (56.5 + 49.8), indicating a conceptual error in the entropy change formula.
2.3·Suggestion
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The standard entropy table is complete, but adding uncertainty estimates or source references next to each value would enhance precision and transparency in data recording.
2.4·Suggestion
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Uncertainty section lists apparatus but lacks specific uncertainty values (e.g., thermometer resolution) in the table—quantifying these is essential for propagating errors accurately.
2.5·Weakness
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The sample calculation ΔT = 27.8°C – 27.3°C = 0.500°C uses three significant figures inconsistently with table data; this undermines clarity in data processing.
2.6·Weakness
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Raw initial and final temperature data are presented to two decimal places, yet trial temperatures are only to one decimal, leading to inconsistent significant-figure reporting.
2.7·Weakness
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Temperature change table omits propagation of calorimeter uncertainty; without mass or heat‐capacity uncertainties clearly shown, processed ΔT lacks full accuracy.
2.8·Weakness
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The uncertainty propagation formula ΔT_uncertainty = ΔT_initial + ΔT_final is overly simplistic; a root-sum-square approach would be more appropriate for independent measurements.
2.9·Weakness
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Uncertainty formula for ΔG uses simplistic ratio rather than full propagation of enthalpy and temperature uncertainties, leading to potential underestimation of error.
2.10·Weakness
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Sample ΔG calculation uses a fixed mole value for all trials rather than adjusting for the actual limiting reactant in each concentration, undermining result validity.
2.11·Weakness
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Limiting reactant logic correctly identifies NaOH/HCl, but applying the same mole value across concentrations without recalculation introduces inconsistency in ΔH determinations.
2.12·Weakness
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The table of Gibbs free energy and uncertainties is well formatted, but relying on non-overlapping error bars as proof of significance is a flawed interpretation.
2.13·Suggestion
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Describing a non-linear “alternating” trend without fitting a proper model limits analytical insight; consider polynomial or segmented analysis.
2.14·Weakness
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Interpreting p > 0.05 as confirmation of the null hypothesis without addressing statistical power or effect size risks a misleading conclusion.
2.15·Weakness
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Pearson’s correlation is poorly suited to non-linear data and small sample size; discussing alternative metrics (e.g., Spearman’s rho) would strengthen the statistical analysis.
Criteria C: Conclusion
3/6
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3
6
Criteria Strands
Moderate
Conclusion relevance and support
Moderate
Scientific context comparison
Criteria Feedback
Conclusion summarises observed trends and links them to molar changes and spontaneity
Relevant comparisons are made to external literature on entropy and concentration effects
Discussion does not fully reconcile findings with the original hypothesis or data limitations
Quantitative uncertainties and their impact on the conclusion are not critically evaluated
3.1·Weakness
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The conclusion restates findings but does not fully reconcile them with the original hypothesis or address key data limitations in ΔG calculations.
3.2·Suggestion
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Discussion compares external studies well, but lacks critical evaluation of how quantitative uncertainties might reconcile contrasting literature findings.
Criteria D: Evaluation
2/6
0
3
6
Criteria Strands
Moderate
Methodological weaknesses
Poor
Suggested improvements
Criteria Feedback
Specific methodological weaknesses (e.g.
parallax, ambient temperature) are identified
Realistic improvements (e.g.
volumetric pipette, insulated setup) are proposed
Relative impact of each weakness on ΔG accuracy is not analysed
Improvements are stated with minimal explanation of how they reduce uncertainty
4.1·Weakness
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Suggested improvements are realistic, but explanations of how each (e.g., pipette use, extended reaction time) quantitatively reduces uncertainty would enhance their relevance.
4.2·Weakness
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The evaluation lists tangible weaknesses, but omits analysis of which specific limitation (e.g., calorimeter heat loss) most impacts ΔG accuracy.
