To what extent does molar mass influence the solubility of common salts, and how do other factors such as atomic radius, lattice enthalpy, and ionic charge compare in their impact on solubility?HL
Loading scores...
5
Official IB Result
11/24
Criteria A: Research Design
3/6
0
3
6
Criteria Strands
A.1Research question context
Good
A.2Methodological considerations
Moderate
A.3Methodology description
Moderate
Criteria Feedback
- Clear articulation of research question within a broad pharmaceutical and industrial context
- Variables defined with correct units and a named, credible data source (CRC Handbook)
- Procedure described sufficiently to allow reproduction with minor ambiguities
- Context remains rather general (all “common salts”) rather than tied to a specific system
- Lacks detailed justification for sample selection criteria beyond “common salts”
- Omission of precise methodological details (specific CRC pages, software/tools, calculation steps)
1.1·Strength
Page 1
The introduction clearly explains why molar mass and solubility are studied and situates the investigation in a scientific context. This establishes a solid foundation for the research question.
1.2·Suggestion
Page 1
The student provides a coherent context linking molar mass and solubility to pharmaceutical and industrial applications but treats “common salts” too broadly. Narrowing the context to a specific salt family or application would strengthen the investigation’s relevance and specificity.
1.3·Suggestion
Page 1
The secondary aim to identify salts opposing the hypothesis is stated, but the progression from aim to method could be clearer. Consider outlining how these salts will be analysed beyond identification.
1.4·Strength
Page 1
The research question is precisely stated, articulating both the primary influence of molar mass on solubility and secondary factors like atomic radius and lattice enthalpy, which adds clarity and focus.
1.5·Suggestion
Page 2
The control of “common salts” as a variable is vague. Define selection criteria (e.g., solubility range, ionic family) to justify the sample and ensure representativeness.
1.6·Weakness
Page 2
The description of methodology omits key details (e.g., specific CRC handbook pages, software or tools for plotting, and calculation steps for Pearson’s r). Adding these will ensure full reproducibility.
1.7·Strength
Page 2
Citing the CRC Handbook as a data source demonstrates methodological consideration for data validity. Referencing the edition and year strengthens the credibility of the data.
1.8·Strength
Page 2
Dependent and independent variables are defined with correct units (mol / kg and g / mol), which facilitates reproducibility and clarity in data collection.
Criteria B: Data Analysis
3/6
0
3
6
Criteria Strands
B.1Communication of data recording and processing
Excellent
B.2Consideration of uncertainties
Poor
B.3Data processing quality
Good
Criteria Feedback
- Raw data are well‐structured with consistent units and significant figures
- Scatter-plot is clearly labelled and the negative correlation (r≈−0.67) is correctly calculated and reported
- Qualitative identification of outliers shows thoughtful chemical reasoning
- No numerical uncertainty estimates, error bars or propagation calculations
- Lack of statistical tests (e.g., R², significance) or quantified outlier criteria
- Minor formatting issues (split tables) and missing caption details
2.1·Strength
Page 2
The raw data table is well-structured with consistent units and significant figures, facilitating clear data communication. Consider adding row/column lines to improve readability.
2.2·Suggestion
Page 3
The extensive raw data table on page 3 is valuable but could be split into two to reduce crowding. Consider grouping by cation family for clarity.
2.3·Weakness
Page 3
The rounding and significant-figure discussion shows awareness of precision but lacks numerical uncertainty estimates or propagation. Include error margins or uncertainties to strengthen quantitative rigor.
2.4·Suggestion
Page 3
Relying solely on CRC Handbook reliability without quantified uncertainty is insufficient. Consider adding manufacturer or experimental error bars to indicate data variability.
2.5·Suggestion
Page 4
The analysis notes that correlation does not imply causation, but the discussion could quantify the goodness-of-fit (e.g., R²) or perform a significance test to contextualize the correlation strength.
2.6·Strength
Page 4
Figure 1 is clearly labelled with axes and units, ensuring that the negative correlation is immediately interpretable and visually accessible.
2.7·Strength
Page 4
Calculating Pearson’s r (r≈−0.6727) correctly confirms the negative correlation. Explicitly stating the calculation tool or formula would complete the record of data processing.
2.8·Weakness
Page 4
The plot lacks error bars or confidence intervals around each point and the trendline. Including these would demonstrate appropriate consideration of uncertainties in the correlation.
2.9·Suggestion
Page 5
Including specific numeric lattice enthalpy or hydration enthalpy values for the outliers would strengthen the causal argument linking ionic structure to solubility.
2.10·Strength
Page 5
Part (ii) effectively identifies clear outliers to probe additional factors; this strategy strengthens the discussion by linking statistical deviations to chemical reasoning.
2.11·Suggestion
Page 5
Outliers are chosen based on personal judgment without a clear threshold. Define quantitative criteria (e.g., distance from trendline) to ensure replicable outlier selection.
Criteria C: Conclusion
3/6
0
3
6
Criteria Strands
C.1Conclusion relevance and support
Good
C.2Scientific context comparison
Moderate
Criteria Feedback
- Conclusion restates the observed correlation and links to calculated r value
- Discussion integrates lattice enthalpy, ionic charge and atomic radius to explain outliers
- Synthesizes analysis effectively to highlight multifactorial influences
- Occasional overgeneralisation implying causation rather than trend
- Lacks quantitative discussion of goodness-of-fit (e.g., R²)
- Incomplete detail on lattice enthalpy values for specific salts
3.1·Weakness
Page 4
The conclusion that molar mass affects solubility is supported by data, but overgeneralizes by implying causation. Framing it as a trend rather than a direct cause would be more precise.
3.2·Strength
Page 5
The discussion of ammonium nitrate’s low lattice enthalpy and ionic charge is chemically sound and well-explained, showing depth in evaluating factors beyond molar mass.
3.3·Weakness
Page 5
The analysis of sodium chloride and ammonium chloride is truncated and lacks detail on lattice enthalpy values. Completing this argument with quantitative enthalpy data would improve rigor.
3.4·Strength
Page 7
Highlighting how lattice enthalpy links atomic radius and charge shows a strong understanding of interrelated factors, encouraging deeper theoretical integration.
3.5·Strength
Page 7
The conclusion effectively synthesizes findings, noting that molar mass, lattice enthalpy, ionic charge, and atomic radius interplay to determine solubility trends.
3.6·Weakness
Page 7
The conclusion repeats that molar mass affects solubility but implies direct causation. Reframe as an observed trend and emphasize multifactorial influences to align with analysis.
Criteria D: Evaluation
2/6
0
3
6
Criteria Strands
D.1Methodological weaknesses
Moderate
D.2Suggested improvements
Moderate
Criteria Feedback
- Identifies specific methodological weaknesses (small dataset, single database, broad salt selection)
- Proposes realistic improvements (gather more data, narrow salt families, perform experiments)
- Does not analyse the relative impact of each weakness quantitatively
- Improvement suggestions remain at a high level without experimental design details or target sample sizes
4.1·Suggestion
Page 6
The reflection sensibly proposes gathering more data and narrowing salt groups, which are realistic improvements. Specifying target sample sizes or experimental parameters would make these suggestions stronger.
4.2·Weakness
Page 6
Proposing multiple databases and experimental measurements is valuable, but the plan remains at a high level. Outline how experimental designs would be conducted to enhance feasibility.
4.3·Strength
Page 6
The suggestion to perform experimental solubility measurements rather than relying solely on literature values is well-founded and would enhance data robustness.