كيف يحسب الرقم الهيدروجيني pH للمحاليل؟ How to calculate pH and pOH?

يحسب الرقم الهيدروجيني بالعلاقة: pH = -log[H⁺]، وللمحاليل المائية عند 25°C: pH + pOH = 14. إذا كان pH 7 فالمحلول قاعدي. [الصيغة الرمزية الموزونة: pH = -log[H⁺] | pOH = -log[OH⁻] | pH + pOH = 14 | [H⁺][OH⁻] = 10⁻¹⁴]

Inquiry Process

6. Inquiry Process — IB Biology HL Revision Notes & Topical Questions

10 min read 12 parts Lesson 1 of 160

📚 IB Biology HL Hub / Topical Revision

Curriculum Equation Key قوانين الرقم الهيدروجيني والاتزان الأيوني - pH & Acid-Base Equilibrium FormulasFast Exam Solution

pH = -log[H⁺]  |  pOH = -log[OH⁻]  |  pH + pOH = 14  |  [H⁺][OH⁻] = 10⁻¹⁴

Word Equation & Conditions: الرقم الهيدروجيني = -لوغاريتم تركيز أيون الهيدروجين | pH + pOH = 14

Location & Specifications: الاتزان الأيوني، كيمياء 3 ثانوي، التحصيلي، و AP Chemistry.

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Lesson summary

6. Inquiry Process — IB BIOLOGY HL notes: Plan, carry out, process, interpret, conclude and evaluate a biological investigation.Exploring = the creative start of an investigation, using curiosity and biological knowledge to make a focused research question and testable hypothesis…

Independent thinking
One variable pair
Starting points
Feasibility
Range of the IV
Quantity of measurements

Part 1 of 10Exploring: developing the investigation

Exploring

Exploring the creative start of an investigation, using curiosity and biological knowledge to make a focused research question and testable hypothesis.

Independent thinking

Independent thinking a standard experiment can be made comparative to show insight.
For example, 'to what extent does the source of amylase (fungal or bacterial) affect its optimal temperature?'.

One variable pair

One variable pair a strong investigation explores one independent variable and one dependent variable in depth.
Avoid questions with several factors, such as light intensity and carbon dioxide concentration together.

Sources

Sources research the theory first, find accepted values for comparison and choose a method.
Use notes, textbooks, the IB data booklet and reliable databases such as PubMed (research papers) and GenBank (gene sequences).

Starting points

Starting points a surprising class result, a different enzyme or organism in a standard practical,
- or applying a concept to a real-world problem such as the effect of salt or pH on seed germination.

Feasibility

Feasibility check equipment, organisms and time, because plant experiments can take days or weeks.

Refining

Refining the research question may change as research shows the first idea is impractical.

Part 2 of 10Research question and hypothesis, designing: variables and measurements

Research question

Research question it must be focused, specific and state the link between the independent and dependent variables.
'How do abiotic factors affect plants?' is too general.
A focused question states the range and organism, such as soil pH 5.0–8.0 and garden cress seed germination rate.

Hypothesis

Hypothesis a clear, testable prediction with a scientific justification, in the form 'If…, then…, because…'.
The 'because' part must use established principles such as enzyme function, osmosis or ecological interactions.
A prediction alone is not enoughtemperature raising enzyme activity needs the reason that greater kinetic energy raises effective collisions between active site and substrate, up to the optimum.

Designing

Designing turning the research question into a practical, step-by-step plan that collects sufficient, high-quality data.

Variables

The independent variable (IV) is the single variable deliberately changed.
The dependent variable (DV) is the variable measured, with the rate calculated as 1 ÷ time where timing is used.
Controlled variables (CVs) are all other factors that could affect the outcome, with an explanation of how each is kept constant.
Percentage change in mass, not absolute change, allows comparison of samples with different starting masses.

Range of the IV

Range of the IV use at least five values to show a clear trend, and justify the range.
For example, 10–50 °C for amylase because lower temperatures are too slow to measure and higher ones may denature the enzyme.

Quantity of measurements

Quantity of measurements repeat each value of the IV at least three times.
- (five is better in biology because of natural variability)
Repeats allow a mean, which reduces random error and helps identify anomalous results.

Range choices

Range choices the range should bracket the expected optimum or include the expected isotonic point, with pure water and hypertonic solutions in osmosis.

Part 3 of 10Designing: method, safety and approaches

Methodology

Methodology a logical, numbered procedure clear enough for another person to replicate.
Give precise detail, such as 'measure 5 cm³ of 1% starch solution using a 10 cm³ graduated pipette' and not 'add some solution'.
Cutting potato as 'use a size 5 cork borer and trim each cylinder to 3.0 cm' beats 'cut up a potato'.
Creative solutions to measurement problems are objective, such as a colorimeter for starch disappearance instead of judging a colour change by eye.

Structure of the method

Designing: method, safety and approaches

Materials and apparatus, listing chemicals with concentrations and equipment with sizes and precision.

Safety, ethical and environmental points, with a brief risk assessment, hazards, specific precautions and waste disposal.

Procedure as numbered steps.

Safety

Safety be specific: iodine is an irritant, so wear goggles and a lab coat, and dispose of biological and chemical waste in designated containers.

Justification

Justification explain why each apparatus, range and organism was chosen.

Other approaches to data

Databases such as GenBank or the IUCN Red List, processed to find a trend.
Simulations (for example PhET) for slow or complex processes such as population dynamics or evolution.
Surveys using questionnaires on human physiology or ecological awareness.

Pilot study

Pilot study a small trial run that checks the method works, for example whether an enzyme reaction is too fast or slow,

so concentrations can be adjusted.

Part 4 of 10Controlling variables

Purpose

Purpose controlling variables minimises systematic errors,
- (errors that consistently shift results one way)
so effects are due to the IV and results are valid.

Calibration

Calibration checking an instrument against a known standard and adjusting it.

Instrument	Calibration or check
pH meter	At least two standard buffers, such as pH 4.00 and 7.00
CO₂ or O₂ sensor	Against known gas concentrations in air
Eyepiece graticule	Stage micrometer for each magnification
Thermometers and probes	Boiling distilled water (100.0 °C) and crushed ice (0.0 °C)
Balance	Tare (zero) before use

Environmental conditions

Environmental conditions organisms are sensitive, so control them.
Use a thermostatically controlled water bath or incubator and let samples acclimatise for a set time.
Fix light intensity with a lamp at a set distance and control humidity where needed.
Monitor and record any variable that cannot be controlled, such as room temperature, and discuss its effect in the evaluation.

Sampling

Sampling biological material is naturally variable.
Use representative random samples, such as quadrat sampling in ecology.
Pick similar samplesthe same potato variety, leaves from one plant, organisms of the same age and sex.
Use many replicates, five or more, to reduce the effect of one anomalous sample.

Control run

Control run a set-up treated identically but without the IV, to prove the IV is the cause.

A dish of bacteria with no antibiotic shows normal growth.
A tube of starch with no enzyme shows starch does not break down alone.
Boiled, denatured enzyme or distilled water in place of the enzyme shows the reaction is due to the active enzyme.

Specific control methods

Say how'a water bath at 37 °C', not 'control the temperature'.
Use large stock solutions and the same measured volume for each trial to fix concentrations.
Cut cylinders from the same potato, with the same borer and length, to fix surface area and surface-area-to-volume ratio.
Prioritise controls with the greatest impact, such as temperature in an enzyme experiment or evaporation in osmosis.

Part 5 of 10Collecting data

Raw data table

Raw data table it comes first and holds only direct measurements, with no calculations.
A specific title, labelled columns for the independent and dependent variables, and units and uncertainties in the headers, not the body.
Precision must match the instrumenta two-decimal-place balance gives 1.50 g, not 1.5 g, and a cylinder with 1 cm³ divisions gives volume to 0.5 cm³.
Record initial and final values, not only the change.

Sufficient data

Sufficient data at least five increments of the IV and at least three replicates of each.
- (ideally five or more)

Qualitative observations

Qualitative observations non-numerical records that explain unexpected results or errors.

Turgidity of potato cylinders (firm or limp).
Leaf colour, such as yellowing from mineral deficiency.
Behavioural responses, such as woodlice moving away from light.
Appearance, such as cloudy milk clearing in an enzyme test.

Issues during collection

Issues during collection note the problem in lab notes and respond.
If the reaction is too fast or slow, adjust enzyme or substrate concentration and record the change and reason.
For an anomalous result, record it, repeat the trial for concordant results and do not erase the outlier because its exclusion is justified later.
If no organisms are found in quadrats, reconsider the sampling location and record the reasoning.
Discard and replace a wrongly cut sample before the experiment begins.

Record raw data directly, never calculated mentally or on scrap paper, and treat observations as evidence.

Part 6 of 10Processing data

1Processing

Processing calculations on raw data to answer the research question, in a new, clearly labelled table separate from the raw data.

Show one full worked example of each type of calculation, even if a spreadsheet is used.

Calculations

Mean of replicates, excluding justified anomalies.
Percentage change = (final − initial) ÷ initial × 100.
Rate = 1 ÷ time (s⁻¹) or change in a variable ÷ time (for example cm³ s⁻¹).
Standard deviation (SD), the spread of replicates around the mean, where a small SD shows high precision.

4Significant figures

Significant figures the final answer has the same number as the least precise data used, not the full calculator display.

5Spread

Spread a high-scoring analysis goes beyond the mean by including a measure of spread.

Part 7 of 10Interpreting results

Graph

Graph the main tool for interpreting, and it needs the following:
A specific title describing the relationship.
The independent variable on the x-axis and the dependent variable on the y-axis, labelled with quantities and units.
Appropriate linear scales using at least half the space.
Clearly plotted points.
A line or curve of best fit showing the trend, which is not dot-to-dot, passes near most points and has about the same number on each side.

Graph features

The gradient can be a rate, such as an initial enzyme reaction rate.
The x-intercept can be a key value, such as the isotonic point in an osmosis graph of percentage mass change against concentration.
A peak or trough can be an optimum for temperature or pH.
Error bars (standard deviation) show variability, and overlapping bars suggest no significant difference.

Describe then explain

Interpreting results

Describe the trend using terms such as positive correlation, reaches an optimum or plateaus.

Explain why using biological principles such as osmosis, enzyme denaturation or limiting factors, the most important step.

Interpreting results

Anomalous result

Anomalous result highlight it on the graph and justify why it is anomalous.

Combine qualitative and quantitative data

Combine qualitative and quantitative data for example, tissue that felt soft and flaccid at the start can explain a smaller than expected mass gain in water.

Use error bars in interpretation by stating what they show about reliability.

Part 8 of 10Accuracy, precision, reliability and validity

Term	Meaning	Affected by
Accuracy	Closeness of the result to the accepted or true value, or to an expected pattern or published study	Systematic errors
Precision	Closeness of repeat measurements to each other, shown by a small SD	Random errors
Reliability	Consistency of results, so precise replicates give a reliable mean, and small error bars show high reliability	Replication
Validity	The conclusion is valid if all other significant variables were controlled so only the IV caused the effect	Experimental design

Part 9 of 10Concluding

Conclusion

Conclusion a brief, focused summary that answers the research question using only the evidence from the analysis.

Content

Start with a direct answer to the research question, based on the identified trend.

Give key processed data as evidence, such as the isotonic point, the optimum or values at the lowest and highest concentrations.

State explicitly whether the results support or refute the hypothesis.

Link the finding to broader biological principles.

Compare with accepted scientific values from the IB data booklet, textbooks or other reliable sources, stating the expected pattern, citing the source and quantifying the difference by percentage error where possible.

Discuss the effect of uncertainties on confidence.

Concluding

Rules

Never introduce new ideas or explanations in the conclusion.
A result that does not support the hypothesis is a valid finding, and reasons for the discrepancy belong in the evaluation.
Do not claim more than the data show, and state if data are inconclusive.
Be concise and direct.

Uncertainty and confidence

Small, non-overlapping error bars give more confidence that a difference is significant and the trend is reliable.
Large or overlapping error bars suggest a difference may be due to random error, so more repeats are needed.

Part 10 of 10Evaluating

Evaluating

Evaluating critical reflection on the method, identifying weaknesses and limitations, assessing data quality and its effect on the conclusion,

and suggesting realistic improvements.

Evaluate the hypothesis

Evaluate the hypothesis even if the data support it, judge the strength of support given the errors and uncertainties found.
A large SD at 50 °C, for example, weakens the conclusion about the exact upper temperature limit.

Sources of error

Type	Description	Examples
Systematic	Flaws in the method or apparatus that bias results the same way every time	A pH meter always reading 0.5 high; evaporation from potato cylinders before final mass
Random	Unpredictable variation, often natural biological variability	Different water potentials in different potatoes or parts of one potato; judging a colour change by eye

Random errors are minimised by repeat trials and means.

Weaknesses, limitations and assumptions

Weaknesses are aspects of the method causing significant error, such as measuring a non-circular inhibition zone with a ruler (parallax error),
- which digital image analysis would improve.
Limitations restrict the scope of the conclusion, such as using one potato species so the isotonic point cannot be generalised.
Assumptions are simplifications, such as mass changing only through water movement when sucrose may diffuse in or ions may leach out.

Improvements

Improvements each must be relevant and realistic (possible in a school laboratory).
- (addressing the weakness)
Close the loop for each point: weakness, then its impact on the result, then a specific improvement.

Key terms

Term	Meaning
Research question	a focused question linking an independent and a dependent variable.
Hypothesis	a testable prediction with a scientific justification.
Pilot study	a small trial run to test and refine a method.
Control variable	a factor kept constant so it does not affect the result.
Systematic error	an error that shifts all results in the same direction.
Random error	unpredictable variation in measurements.
Calibration	checking an instrument against a known standard.
Anomalous result	a data point that does not fit the trend.

Avoid these mistakes

A hypothesis is a guess — it is a testable prediction with a scientific explanation.

A result that disproves the hypothesis means the experiment failed — it is a valid finding.

Precision and accuracy are the same — precision is closeness of repeats and accuracy is closeness to the true value.

Controlling a variable means saying it will be kept the same — the method of control must be described.

Last updated: 18 October 2026