14 November 2026

Make Your Own DNS Server — And Block Adverts Across a Network

 

Make Your Own DNS Server — And Block Adverts Across a Network

A practical Linux project that makes DNS, networking and security visible.

Open a website and you see a page. What you do not see is the collection of requests that helped assemble it: requests for images, fonts, video, analytics and advertising services.

What if students could watch some of that activity happening — and decide which domain lookups their network should answer?

That is where Pi-hole becomes interesting.

It can block requests to many advertising and tracking domains across devices using it for DNS. But its greatest value in a computing lesson is the opportunity to investigate a service that normally works quietly in the background.

The aim is to build something useful, observe its behaviour and explain the evidence.

1. DNS: the question before the connection

DNS stands for Domain Name System. Among its jobs is finding IP addresses associated with names such as www.example.com.

A browser needs an address to establish a connection to a server. DNS helps it find one. The subsequent web connection is a separate operation. [1]

An address-book analogy is useful: looking up somebody's telephone number and making the call are different actions.

For students, that distinction matters. A DNS server does not normally download the webpage on the browser's behalf.

There is also more to DNS than one name matching one address. An A record provides an IPv4 address; an AAAA record provides an IPv6 address. A name can have multiple records, and answers can change.

The first question I would ask is:

If a computer cannot look up a website's address, does that necessarily mean its internet connection has failed?

That question becomes much easier to answer once students operate their own DNS service.

2. What Pi-hole actually does

Pi-hole provides a local DNS service with filtering. In a typical installation, permitted queries that cannot be answered locally are forwarded to a configured upstream DNS resolver. Pi-hole also caches answers for reuse. [2]

For blocked domains, it supplies a blocking response instead of the normal address. Its default NULL mode returns 0.0.0.0 for an A query or :: for an AAAA query. Other blocking modes exist. These are unspecified addresses, not the address of an alternative advert server. [3]

This is often called DNS sinkholing.

Imagine a fictional webpage whose main content comes from one domain while an advertising script comes from another. If the advertising domain is blocked, the browser cannot obtain its normal address through Pi-hole. The main page may still load.

Pi-hole filters domain lookups. It does not inspect each image or video and decide whether it is an advert.

That explains both its usefulness and its limitations.

3. Why this makes a strong computing project

My preference in practical teaching is to give students something they can change, measure and explain. A working service gives technical vocabulary a purpose.

Pi-hole brings together several ideas relevant to GCSE and A-level Computer Science, including OCR H446:

ConceptPractical question
Client-server systemsWhich device asks the question, and which service answers it?
IP addressingWhy must clients be able to find the DNS server reliably?
DNSWhat information does an A or AAAA query request?
DHCPHow can a device discover which DNS server to use?
CachingWhy might a repeated lookup avoid an upstream request?
SecurityWhat can filtering prevent, and what can bypass it?
TestingWhat evidence would demonstrate that our rule caused a change?

Installing software is the beginning. The deeper learning comes from predicting what should happen and investigating what actually happens.

4. Choose a small, manageable starting point

For this project, use:

  • A Raspberry Pi or another small computer running a currently supported Linux distribution.
  • Reliable power and storage.
  • A network connection; Ethernet is convenient for a service that should remain available.
  • A Windows or Linux computer for testing.
  • Access to the router's settings if you later extend the project across your own network.

Pi-hole does not require a Raspberry Pi. Its prerequisites currently list modest minimum resources: 512 MB RAM and 2 GB free storage, with 4 GB recommended. Check the supported operating systems and versions before preparing the host. [4]

A Linux virtual machine on a Windows computer is another possible teaching arrangement. It needs a network configuration that makes it reachable from the test client, usually a bridged or external network adapter. The host computer and virtual machine must remain running while clients depend on them.

For a first investigation, I would use one test client, then expand after verifying the results. Work on a network you own or have permission to administer.

5. Install Pi-hole and establish a baseline

Give the server a predictable address

Arrange a stable LAN address, for example through a router DHCP reservation. Pi-hole's documentation requires a static address or reservation. [4]

For the examples below, assume:

DeviceIllustrative IPv4 address
Router192.168.1.1
Pi-hole192.168.1.10
Test computer192.168.1.50

These are examples, not settings to copy blindly. Use addresses appropriate to your own network and avoid conflicts.

Use the official installation instructions

Update the host using its operating system's normal process. Then follow the current Pi-hole installation guide. One documented method downloads the installer before running it: [5]

wget -O basic-install.sh https://install.pi-hole.net

Review the downloaded script before running it with administrator privileges:

sudo bash basic-install.sh

During setup, select the appropriate network interface and an upstream resolver. For a short, agreed classroom investigation, enable sufficient logging to see the test queries. Record the final dashboard address and login details.

On a typical default setup, the web interface is available at:

http://192.168.1.10/admin/

Use the address and port reported by your installation if they differ. Set a strong administration password; the current command is: [6]

sudo pihole setpassword

Keep this first installation simple. Use the initial blocklist offered during setup and check that it has loaded. Adding numerous extra lists immediately makes it harder to explain why something was blocked.

Query Pi-hole directly

Before changing the computer's normal DNS settings, explicitly send it a test query.

On Windows:

nslookup example.com 192.168.1.10

On Linux, where dig is installed:

dig @192.168.1.10 example.com A
dig @192.168.1.10 example.com AAAA

Record the answer and check that the request appears in Pi-hole's query log. If it does not, investigate connectivity, the service and local firewall rules before proceeding.

This confirms that Pi-hole can answer an explicitly directed query. It does not yet prove that the browser or the rest of the network uses it.

6. The clearest experiment: block, observe, restore

Rather than starting with an unfamiliar advertising domain, use a harmless, predictable target: example.com.

Step A: Record the unblocked response

Run the direct query above. Save the result and note the query-log entry.

Step B: Add one exact rule

In Pi-hole's domain management interface, add example.com as an exact denied domain. Ensure the rule is enabled and applies to the test client, normally through the default group.

Menu labels can vary between releases. The important choice is an exact domain rule rather than a broad regular expression.

Step C: Repeat the same query

Run the identical command again. In default NULL mode, the answer should now contain 0.0.0.0 for A or :: for AAAA, and the log should identify the query as blocked. [3]

Step D: Remove the rule and retest

Remove the temporary denial, then repeat the query and confirm that normal resolution returns. If an answer persists, allow relevant cache entries to expire and retest.

StagePredicted observation
Before denialNormal DNS answer
Exact denial activeBlocking response and blocked log entry
Temporary denial removedNormal resolution restored after any relevant caching

The table describes expected behaviour, not measurements already collected.

Ask students to explain why the evidence supports a causal link. What stayed constant? What changed? Why is restoring the original result useful?

Also ask whether blocking example.com necessarily blocks every subdomain. An exact rule matches the specified name; it is not automatically a wildcard rule.

7. Let one device use Pi-hole normally

Record the test device's original DNS configuration. In its network settings, set its DNS server to the Pi-hole address, taking account of any separately configured IPv6 DNS servers.

Then try a lookup without specifying a server:

nslookup example.com

Check which server answers. Browse a few agreed websites while watching the query log.

The browser may use a different DNS path from the command-line tool, particularly if secure DNS is configured. Treat the log as evidence: did the browser's requests actually arrive?

If testing fails, restore the original DNS settings — often automatic settings supplied by the router — and reconnect the device. Keep that rollback available throughout the investigation.

8. Extend filtering across the network

Once one client works, configure your router's LAN DHCP settings to advertise Pi-hole as the DNS server. Renew client leases or reconnect devices, then verify requests from several clients. Pi-hole's post-install guide describes this arrangement. [7]

Some routers instead forward client DNS requests themselves. This can work, but Pi-hole may identify the router as the requester rather than each individual device.

Do not assume that changing a router's WAN DNS setting is equivalent to configuring what LAN clients receive. Check the router's own documentation.

If the router cannot advertise a custom DNS server, Pi-hole can provide DHCP. That is a separate configuration task: disable the router's DHCP service before enabling Pi-hole's replacement, and record the original settings. Avoid accidentally operating two competing DHCP servers. [7]

Two details deserve particular attention:

A public secondary DNS server can bypass filtering. Clients do not universally treat the second address as an emergency-only option. For filtered redundancy, use a second working Pi-hole rather than assuming an unfiltered resolver will remain unused.

IPv6 needs consideration too. Clients may receive IPv6 DNS information separately. Ensure the DNS resolvers advertised for both protocols match your intended setup. Looking up an AAAA record through Pi-hole's IPv4 address is valid; the query's transport and the address type being requested are different things.

Check for forwarding loops as well. If the router forwards queries to Pi-hole, Pi-hole should not send those same queries back to the router as its upstream resolver.

9. Watch the requests — but interpret them carefully

Open an agreed webpage and inspect the query log. You may see names associated with content delivery, analytics, advertising and background activity.

Ask:

  • Which requests appear when the page opens?
  • Which were blocked, cached or forwarded?
  • Which names remain unfamiliar?
  • What happens when the browser is closed?

An unfamiliar domain is not automatically malicious. A familiar-looking name is not proof of safety. Background apps and operating system services also generate requests.

Nor is the DNS log a complete browsing history. It records domain queries that reached Pi-hole, not full HTTPS URLs or the contents of pages. Caching can mean a page visit produces no fresh lookup, while background activity can produce lookups without a person opening a page.

For teaching, I would use a dedicated test device and agreed activities. Pi-hole offers privacy settings that hide domains, hide clients or reduce recorded detail further. Avoid publishing identifiable query logs in screenshots. [8]

10. What does “25% blocked” really mean?

Suppose a fictional observation period contains:

Total DNS queries = 8,000
Blocked DNS queries = 2,000

Blocked query percentage = (blocked queries / total queries) x 100
Blocked query percentage = (2,000 / 8,000) x 100 = 25%

That tells us about the share of recorded DNS queries blocked during that period.

It does not establish that 25% of internet traffic was advertising, that 25% of bandwidth was saved or that pages loaded 25% faster.

A small background service can repeatedly retry a blocked name. A video stream can transfer a large amount of data after relatively few lookups.

This makes an excellent data-literacy exercise: what does the counter measure, and what additional measurements would justify a stronger claim?

11. Why some adverts remain

Pi-hole works well when unwanted content uses domains that can be blocked separately from useful content.

When adverts and wanted material share a hostname, domain filtering cannot reliably distinguish between them. YouTube adverts are a familiar example: blocking relevant video-serving domains can also break the videos. [9]

Pi-hole also does not tidy up every empty advert space in a page. Browser extensions can operate within the browser and make decisions that a DNS filter cannot.

Filtering can also be bypassed when a device or application uses another resolver. DNS over HTTPS can send lookups to an external provider rather than the local DNS service; Mozilla documents this interaction with local filtering policies. [10]

VPNs, explicit DNS settings and some privacy services can also change the path. Investigate those settings on the agreed test client instead of assuming the router controls every lookup.

12. Troubleshooting is part of the lesson

SymptomUseful next check
Explicit query to Pi-hole times outCheck its address, service, client reachability and DNS firewall rules.
Explicit queries work, but browsing creates no log entriesCheck browser secure DNS, VPNs, client DNS settings and caching.
Only some devices are filteredCheck DHCP leases, manual settings and separately advertised IPv6 DNS.
A useful feature stops workingReproduce it, inspect recent blocked queries, and test a narrow allow rule.
Websites appear to fail when the Pi-hole host stopsRestore the previous DNS configuration and confirm whether DNS availability caused the failure.

More blocklists do not automatically produce a better setup. A false positive — a useful domain blocked by mistake — can break logins, embedded media or other features.

The response should be specific: identify the relevant domain, test the explanation and make the smallest justified exception.

13. Security includes the service you have just created

Domain filtering can reduce access to some known unwanted or harmful destinations when suitable lists are used. It does not replace software updates, careful account security or endpoint protection.

Keep the operating system and Pi-hole updated, protect administration access and retain a configuration backup. The Pi-hole command reference documents update commands; container installations require their own image-update workflow. [6]

Keep DNS and administration access restricted to the intended local network. Do not forward public internet traffic to DNS port 53 or expose the dashboard for this exercise.

There is also an availability lesson. Once other devices depend on your DNS service, switching it off can disrupt name resolution even while broadband remains connected.

Building infrastructure creates a responsibility to keep it available — or provide a clear way to recover.

14. Take the investigation further

Compare cached and forwarded queries

Repeat a permitted lookup and inspect its status in the log. DNS answers are cached for a time governed by their TTL, or time to live. Caches exist at several levels, including the client and resolver. [1]

Use several observations before claiming a speed improvement. A changing query time alone does not prove which cache was responsible.

Compare devices

Use a Windows computer, a Linux computer and an iPad as clients. Do they reach the same DNS service? What background requests differ? Use anonymised observations rather than judging users by their devices' traffic.

Add a local DNS name

Create a local record for a laboratory web server, using a name within home.arpa, such as labserver.home.arpa. Then test name resolution separately from loading its webpage. Use the server's real LAN address.

Explore recursion with Unbound

For a later project, Pi-hole's official Unbound guide explains how to add a local recursive resolver. That creates a useful comparison between forwarding to an upstream provider and resolving through the DNS hierarchy. It is an extension, not a requirement for the first lesson. [2]

15. The real success is being able to explain it

Adverts provide the initial motivation. The lasting educational value is understanding the service behind the effect.

A student who can explain why one query was blocked, another was forwarded and a third never reached Pi-hole has moved beyond following installation instructions.

They are connecting addressing, protocols, caching, security and evidence.

At Hemel Private Tuition, this is the kind of practical investigation I value: a useful system that gives students reasons to ask better questions. For GCSE and A-level learners, including OCR H446 students, it offers a way to connect networking theory with decisions they can observe and justify.

Build the server. Follow a query. Change one rule. Then explain exactly what happened.

13 November 2026

Can We Make Copper Grow Old? The Chemistry of Artificial Patination

 


Can We Make Copper Grow Old? The Chemistry of Artificial Patination

A new sheet of copper has a warm, reddish metallic shine. An old copper roof may be brown, nearly black or green. A bronze statue can carry several colours across the same surface.

Those changes are chemistry made visible.

The metal has reacted with its surroundings, producing a surface layer called a patina. By changing those surroundings deliberately, we can investigate how patinas develop—and why the same metal can age differently in different places.

For students, this offers a particularly satisfying investigation. It connects oxidation, atmospheric chemistry and experimental design with buildings, sculpture and the difficult decisions involved in conservation.

The challenge is to do more than produce an attractive green surface. Can we explain what changed, compare treatments fairly and recognise what our observations cannot establish?

A Patina Is a Reaction Layer

Paint is applied over a surface. A corrosion patina develops through reactions involving the metal itself.

Copper atoms can lose electrons and enter compounds containing oxygen and other elements. The products accumulate, dissolve, crystallise or change again as conditions vary.

A patina may therefore contain several compounds arranged in layers. Its composition depends on moisture, oxygen, pollutants, deposited salts and the history of wetting and drying.

Some artists deliberately encourage these changes. Architects value copper partly because its appearance develops over time. Conservators study the resulting layers because they can contain evidence about an object's manufacture, treatment and environment.

Why “Copper Turns Green” Is Only the Beginning

Students often meet the explanation that copper reacts with air to form green copper carbonate. That introduces a useful idea, but real outdoor surfaces are more complicated.

Examples of relevant compounds include:

CompoundFormulaRelevance
Copper(I) oxide, or cupriteCu2OOften associated with the early reddish-brown surface layer
Copper(II) oxide, or tenoriteCuOBlack oxide; readily demonstrated by heating copper in a separate practical
Basic copper carbonate, malachiteCu2CO3(OH)2A green carbonate-containing corrosion product
Basic copper sulphate, brochantiteCu4SO4(OH)6An important constituent of many mature outdoor patinas
Basic copper chloride, atacamiteCu2Cl(OH)3One chloride-containing product associated with suitable salt-rich conditions

Research on atmospheric corrosion identifies sulphate-rich and chloride-rich patinas in different environments. Carbonates can also occur; there is no single formula for every green roof or statue. [1, 2]

The table describes possible chemistry. It is not a colour chart that lets us identify an unknown coating by eye.

A blue-green surface is an observation. Calling it a particular copper compound requires further evidence.

The Chemistry Underneath the Colour

One possible initial oxidation reaction is:

4Cu + O2 -> 2Cu2O

For a simplified model of basic copper carbonate formation, we can write:

2Cu + O2 + CO2 + H2O -> Cu2CO3(OH)2

This balanced overall equation summarises a possible net change. It does not mean that an outdoor patina forms in one step, or that carbonate must be the main product.

For A-level students, the electrochemical interpretation goes further. In a simplified model involving copper(II) ions:

Cu -> Cu2+ + 2e-

At other sites, oxygen can accept electrons:

O2 + 2H2O + 4e- -> 4OH-

The electrons move through the metal, while ions move through a thin film of water. Subsequent reactions depend on the local solution chemistry. Real copper corrosion can also involve copper(I) intermediates.

This helps explain why a surface that merely looks damp can support chemical change. It does not need to be submerged in a beaker.

The Investigation: Give Identical Copper Samples Different Environments

Suitable level: GCSE upwards, with electrochemistry extensions for A level.

Timing: About 45–60 minutes to prepare; brief daily observations for 7–14 days. Some treatments may show little change in that period. Longer observation can be valuable.

Main question: How do salt and dilute vinegar affect the development of visible surface changes on copper during repeated wetting and drying?

This is a proposed exploratory method, not a guarantee of a particular colour or a proven recipe for a durable architectural finish.

Materials

  • Twelve equal pieces of uncoated copper sheet, approximately 2 cm x 3 cm, prepared with smooth edges by an adult.
  • Four labelled treatment solutions and separate small measuring syringes or pipettes.
  • Distilled or deionised water.
  • Sodium chloride.
  • Plain white vinegar with its labelled acidity recorded; ideally 5% acidity.
  • Fine abrasive pad, detergent, forceps and eye protection.
  • Separate labelled plastic or glass dishes, a protective tray and ventilated splash covers.
  • Camera or phone, ruler and a white or grey reference card.

Use genuine copper from the same sheet. Modern coins may be plated rather than solid copper, and scrap pieces may carry lacquer, solder or unknown contamination.

Prepare Four Treatments

TreatmentPreparationWhat it helps investigate
A: Water control100 mL distilled waterChanges during water wetting and drying
B: Salt solutionDissolve 1.0 g sodium chloride and make up to 100 mL with waterEffect of adding salt compared with A
C: Dilute vinegarMix 20 mL of 5% acidity vinegar with 80 mL waterEffect of dilute acetic acid compared with A
D: Salt plus dilute vinegarDissolve 1.0 g sodium chloride in 20 mL of the same vinegar; add water to a final volume of 100 mLEffect of salt in the vinegar treatment, compared with C

C and D have approximately 1% acetic acid when made from vinegar labelled 5%. If the vinegar has a different labelled acidity, record it and recognise that the treatment strength differs.

Three samples per treatment provide a first look at repeatability. Keep an additional cleaned copper piece untreated if available, as an ambient-air reference.

Prepare the Copper Consistently

  1. Label each dish, keeping labels and adhesive away from the tested copper face.
  2. Lightly abrade every sample using the same pad, number of strokes and direction.
  3. Wash with detergent, rinse thoroughly with water and finish with a distilled-water rinse.
  4. Let the pieces dry and handle them with forceps or by the edges.
  5. Photograph every sample before treatment.

Surface preparation matters. Fingerprints, grease and different abrasion marks can change wetting and the development of the coating.

Apply a Measured Film

Lay each sample horizontally in its own dish. Apply 0.20 mL of its assigned solution to the upper face, spreading it gently with the pipette tip without scratching the surface. Keep the treated area consistent and record any run-off.

Allow the samples to dry at room temperature under ventilated splash covers. Once daily, photograph them before applying a fresh 0.20 mL of the assigned solution. Continue for 7–14 days.

If a surface remains wet at the next observation, record this before adding more. Drying time is itself useful evidence.

Keep temperature, lighting, sample orientation, solution volume and observation times as consistent as practical. Rotate dish positions systematically to reduce the effect of a warmer or draughtier part of the bench.

Do not rinse between daily treatments. This means salts and other residues can accumulate, so the investigation models repeated deposition as well as repeated wetting. State that limitation when interpreting it.

Practical Safety and Clean-Up

Carry this out as supervised laboratory work. Wear eye protection, protect against sharp copper edges, use small quantities and keep food and drink away.

These preparations need no heating, spraying, ammonia, bleach, peroxide or strong acids. Do not introduce household cleaners into the experiment.

Once the liquids contact copper, they may contain copper compounds. Collect used solutions and rinsings in a labelled waste container; keep contaminated wipes and loose deposits contained. Arrange suitable chemical-waste disposal rather than pouring them into drains, soil or ponds. Avoid touching or brushing dry corrosion powders.

Use new copper samples, never historical objects, valued coins, cookware or drinking-water fittings.

What Might Happen—and Why It May Surprise You

The water control may change slowly. Salt-treated copper may develop localised patches, while vinegar-containing treatments may initially appear cleaner or brighter before later discolouration.

That last possibility is especially useful for teaching.

Acetic acid can dissolve existing oxide or other surface deposits. In the presence of oxygen it can also participate in a chemical environment that promotes further copper corrosion. Salt changes the conductivity and chemistry of the surface film; chloride ions can influence the corrosion products and localised attack.

Acetate-containing copper products may occur in vinegar treatments. Chloride-containing products may occur when salt is present. Neither assignment is established by appearance alone. Conservation guidance also identifies acetic acid and airborne chlorides as possible causes of green corrosion layers. [3]

There may also be white salt crystals left by evaporation. Those are not automatically a copper patina.

The treatment that produces the strongest colour is not necessarily the treatment that produces the most stable coating.

Turn Photographs into Evidence

Use the same camera position, background, lighting and exposure settings wherever possible. Photograph dry samples at the same point in the daily cycle, with the reference card in view.

A simple record can include:

DaySampleColour and patternApproximate changed areaSurface notes
0D1Bright reddish copper0%Fine abrasion marks
3D1Record actual observationEstimate from photographNote crystals, wetness or loose material
7D1Record actual observationEstimate from photographCompare with D2 and D3

For a repeatable estimate, place a digital grid over each photograph and apply the same rule for counting altered squares:

Estimated coverage (%) = altered area / total assessed area x 100

Keep the assessment away from labels and cut edges. Decide beforehand whether brown darkening and blue-green patches will be counted together or recorded separately.

Graph coverage against time for each treatment, showing the three individual samples or a mean with their spread. Be clear that colour coverage is a measure of appearance, not a direct measurement of corrosion rate.

Weighing can be an extension, but mass change is difficult to interpret: it may include retained salts, water and corrosion products, while some copper may have dissolved into liquid. A heavier sample has not necessarily suffered less corrosion.

Can We Tell What the Patina Contains?

Colour, texture and experimental history support hypotheses. They rarely establish a unique composition.

A convincing investigation separates three statements:

  • Observation: A blue-green deposit appeared after repeated salt-and-vinegar treatment.
  • Interpretation: The environment encouraged the formation or accumulation of copper-containing surface products.
  • Unresolved question: Which compounds are present, and in what proportions?

Researchers can investigate those questions using methods such as X-ray diffraction and Raman spectroscopy. Published artificial-patination research uses instrumental analysis alongside photographs and mass measurements. [1]

There is a powerful lesson here for examination answers: describe the evidence accurately, then make a conclusion that the evidence can support.

The Carbonate Connection: An Extension with a Clear Limit

To explore carbonate chemistry, introduce a fifth treatment containing 1.0 g sodium hydrogencarbonate made up to 100 mL with water. Use fresh, identically prepared copper samples and the same application schedule.

This changes both the available ions and the pH, so it does not isolate a single variable. Little visible change over a fortnight would be a valid result.

Adding hydrogencarbonate does not guarantee a malachite coating. For a stronger comparison, examine a labelled malachite specimen alongside the experimental surfaces and discuss what would be needed to prove that the same compound is present.

Do not mix this treatment with vinegar: the acid–hydrogencarbonate reaction would create a different investigation.

From the Laboratory Bench to Roofs and Statues

A roof experiences rain, drying, deposited particles and changing temperatures. A sheltered section may remain damp for a different length of time from an exposed edge. Water running from one material onto another can carry dissolved substances with it.

That makes a weathered building a record of its surroundings, although its colour alone cannot reconstruct that history reliably.

A useful outdoor activity is to photograph an accessible copper feature or bronze sculpture from public ground. Compare sheltered and exposed areas, upward-facing and vertical surfaces, and streaks below projections. Do not touch, sample or clean it.

Ask which differences might arise from water flow, previous coatings, alloy composition or maintenance. A bronze statue is an alloy object, so results from pure copper are a useful model with limits.

Conservation: When Cleaning Can Remove the Evidence

For a new decorative object, an artist may deliberately create a patina. For an old object, the existing surface may carry historical and artistic significance.

Polishing it away can remove original material as well as alter the appearance.

Conservators distinguish between relatively stable, adherent patinas and active corrosion associated with continuing deterioration. Fresh powder, flaking or new erupting spots deserve attention; green colour by itself is not a diagnosis. Chloride-related active corrosion is a particular concern for some archaeological copper alloys. [3]

Historic England explains that a compact natural green patina on bronze can reduce the corrosion rate, while maintenance remains necessary. [4]

Our accelerated coatings have not been shown to offer equivalent protection. Demonstrating durability would require a further investigation into adhesion and behaviour during continued exposure.

The conservation question is therefore richer than “How do we make it shiny?” It is: What should be preserved, what is changing, and what evidence justifies intervention?

Why I Would Use This in My Teaching

What appeals to me about this investigation is that students have to return to it.

They make a prediction, prepare samples, notice an unexpected patch, compare photographs and decide whether their original explanation still fits. The learning continues between the first application and the final observation.

At Hemel Private Tuition, that is the kind of practical thinking I want to encourage. A student can know the definition of oxidation and still need practice deciding whether an experiment supports a particular explanation.

I would be especially interested in the student who asks why two supposedly identical pieces look different. That question opens a useful discussion about surface preparation, uneven drying and the value of repeats.

The most attractive sample may make the best photograph. The awkward, inconsistent sample may generate the best scientific discussion.

Conclusion: A Changing Surface Can Change How We See Chemistry

Artificial patination turns a small piece of copper into a meeting point between chemistry, architecture, art and history.

It shows how surface reactions depend on the environment, how several compounds can contribute to a familiar colour, and why corrosion can produce both valued surfaces and serious deterioration.

The strongest outcome is a carefully supported explanation: what we changed, what we observed and what remains uncertain.

The next time you pass a green roof or a weathered bronze statue, look at the patches, streaks and sheltered corners. There is an experiment unfolding there—one that may have been running for generations.

Can we make copper look old? Often, yes. Understanding what we have made is the more interesting challenge.

For GCSE and A-level chemistry tuition that connects practical investigation with clear explanations and examination skills, contact Hemel Private Tuition.

Sources and Further Reading

  1. Bureš and colleagues, Artificial Patination of Copper and Copper Alloys in Wet Atmosphere with Increased Content of SO2, Coatings (2019). Research context, not a method to reproduce in this practical: https://www.mdpi.com/2079-6412/9/12/837
  2. Smithsonian Museum Conservation Institute, abstract of Vernon and Whitby's research on the mineralogical relationships of copper corrosion products: https://mci.si.edu/node/1202800
  3. Canadian Conservation Institute, Recognizing Active Corrosion: https://www.canada.ca/en/conservation-institute/services/conservation-preservation-publications/canadian-conservation-institute-notes/recognizing-active-corrosion.html
  4. Historic England, Bronze Statuary: https://historicengland.org.uk/advice/technical-advice/buildings/maintain-and-conserve-metals/bronze-statues/

12 November 2026

The Fossil Record Is Missing Most of the Story What gets preserved — and what disappears?

 


The Fossil Record Is Missing Most of the Story

What gets preserved — and what disappears?

Stand in front of a museum display and it is easy to imagine that you are looking at a representative selection of ancient life.

There are shells, teeth, bones and perhaps a beautifully preserved leaf. Put them together and a lost world begins to emerge.

But imagine trying to reconstruct your garden from the things that would still be recognisable after years of decay, burial and disturbance. Would your collection represent everything that once lived there?

Or would it mostly represent the things that were hardest to destroy?

The fossil record is an extraordinary source of evidence. It is also a highly selective one.

That makes fossils a wonderful starting point for a question that reaches far beyond palaeontology:

What might be missing from the evidence we have?

Nature does not preserve everything equally

A dead organism does not automatically become a fossil.

It may be eaten, decomposed, broken apart, transported or dissolved. Even remains that become buried can undergo changes that destroy their structure or remove details.

The study of these processes is called taphonomy: the investigation of what happens to organisms and their remains after death, including the changes involved in preservation.

A useful classroom description is:

Taphonomy asks how a living thing became the evidence we can examine today.

A shell and a piece of soft fruit provide a simple comparison. Both come from living organisms, but their structures and chemical compositions give them very different chances of surviving.

Hard parts such as shells, teeth and bones generally have a better chance of preservation than soft tissues. Rapid burial can also help by reducing exposure to scavenging and physical disturbance. Neither guarantees a fossil. [1, 2]

There are several filters between life and a museum

Think of the fossil record as the result of a sequence of opportunities for information to be lost.

FilterQuestion to ask
Original communityWhat organisms actually lived here?
Death and decayWhich parts survived decomposition and scavenging?
BurialWhich remains entered sediment rather than being destroyed?
Geological changeWhich evidence survived subsequent physical and chemical changes?
DiscoveryWhich fossil-bearing rocks became accessible and were searched?
DisplayWhich finds were selected for the public to see?

The final collection is not a random sample of the original community.

That is what bias means here: some kinds of evidence have a greater chance of entering the record than others.

A museum display adds another selection. A spectacular skeleton can attract attention in a way that a tray of tiny fragments may not. Yet small, less dramatic fossils can be scientifically valuable too.

The question to take into a museum is therefore:

“Why am I seeing these organisms, and what else might have lived alongside them?”

Where an organism lived matters too

Body structure is only part of the story.

An organism living where sediment accumulates has a different preservation opportunity from one living on an exposed upland slope. Marine environments, rivers and floodplains are generally better represented than mountainous terrain. [2]

Try this thought experiment.

One animal dies on a muddy river floodplain and is soon covered by sediment. Another dies on an exposed hillside.

Before considering their species or size, ask:

  • Which is more exposed to weathering and scavenging?
  • Which is more likely to be buried?
  • Could water move either animal’s remains?
  • Might their eventual resting place differ from where they lived?

A fossil’s location is evidence to interpret. It is not automatically an exact map of the organism’s original home.

Practical investigation 1: Which materials survive burial?

Suitable for: families, GCSE students and A-level discussion
Time: about 30–45 minutes to set up, followed by observations over 4–6 weeks

This investigation compares the early survival of biological materials under different conditions.

It is a decay and preservation investigation. It will not turn the samples into geological fossils.

Choose the materials

A manageable selection is:

  • clean, empty shells;
  • leaves from one plant;
  • small twigs;
  • pieces of apple peel or another soft plant material.

For the separate impression activity, you could also use a clean craft feather and a bone replica.

Real bone is unnecessary. A plastic replica can model shape but cannot model the decay, chemistry or mineralisation of bone.

Use small quantities of plant material and avoid raw meat, animal carcasses or unknown animal remains. Wear gloves when handling soil and old samples, cover cuts and wash hands afterwards. Keep containers away from food preparation areas. Do not sniff samples or bring visibly mouldy material into the classroom; an adult should dispose of affected containers without further disturbance.

Ask a precise question

Rather than changing several conditions at once, start with:

“Does initial sediment moisture affect how much recognisable material remains after four weeks?”

Use the same clean sand or other chosen sediment throughout. Compare a measured dry treatment with a measured damp treatment.

Do not call a damp container “oxygen-free”. Oxygen availability has not been measured, and moisture alone does not establish it.

Set up a fair comparison

  1. Photograph each material beside a ruler.
  2. Prepare similar-sized samples of each material.
  3. Place samples in separate labelled containers so that materials do not overlap.
  4. Use the same sediment mass and burial depth in every container.
  5. Add the same measured amount of water to each damp-treatment container. Add none to the dry treatment.
  6. Keep containers in the same shaded location with the same type of ventilated cover.
  7. Prepare at least three independent containers for each material in each treatment.
  8. Record the starting conditions and examine the samples after four weeks.

For four materials, two treatments and three repeats, that means 24 containers.

If you want to compare several retrieval dates, prepare a separate set for each date. Repeatedly digging up and reburying the same sample changes its exposure and may damage it.

Keep a simple record of container mass or moisture changes during the investigation. If you replenish evaporated water, use a consistent method and record the amount. A ventilated dry treatment may absorb moisture from the air, so describe the actual conditions rather than assuming they stayed perfectly dry.

Decide what “survived” means

A sample does not have to disappear completely to lose useful information.

Record several features separately:

ObservationWhat to record
Recognisable outlineClear, partial or absent
Surface detailVeins, ridges or texture still visible?
FragmentationIntact, cracked or broken into pieces
ColourChange compared with the starting photograph
RecoveryWas the sample found, and how easily?

Photograph the recovered material under similar lighting, with a ruler.

For a simple graph, use a defined score:

  • 0 = no recognisable material recovered;
  • 1 = fragments recovered, original form unclear;
  • 2 = original form partly recognisable;
  • 3 = original form clearly recognisable.

This is an ordered description, not a precise measurement of the amount of tissue remaining. Show individual results as well as any summary.

A score of 0 means “not recovered”, which is not necessarily the same as “completely decomposed”. Small fragments may have been missed.

Predict first, then observe

Which will retain the most recognisable detail: shell, twig, leaf or apple peel?

Ask students to explain their predictions before retrieval.

Do not promise a particular result. Some materials may change very little within four weeks. That is a result to discuss, rather than a reason to invent a more dramatic conclusion.

The strongest conclusion will be limited:

“Under our conditions, these materials retained recognisable features better than those materials.”

It will not be:

“We have proved which organisms fossilise.”

Practical investigation 2: Which objects leave the clearest impressions?

Time: 30–45 minutes

This quicker activity investigates a different question:

“How much information can an impression preserve?”

You will need

  • soft clay or reusable modelling clay;
  • a clean shell;
  • a leaf with visible veins;
  • a small twig;
  • an optional clean craft feather;
  • a ruler and camera.

Flatten equal-sized clay pads to a similar thickness. Press each object gently into a pad, then lift it away.

Try to keep the pressing method consistent. Complete at least three impressions of each object.

Compare the original with the impression:

  • Can you identify the outline?
  • Which surface details survived?
  • Did pressing distort the object?
  • Can you infer its original thickness?
  • What can you no longer tell about its colour or composition?

For an extension, compare a fine clay surface with damp fine sand. Keep the objects and pressing method as similar as possible.

Clay is a useful model of impression formation; it does not reproduce the full range of natural sediment properties or geological changes.

Challenge someone to identify the evidence

Hide the original objects and ask another student to identify the impressions.

A leaf outline might be obvious while its species remains uncertain. A shell may preserve ridges beautifully but reveal nothing about the animal’s behaviour.

This introduces the difference between having evidence and having enough evidence to answer a particular question.

An impression of a body part records that structure. A footprint records an activity. Both can provide evidence of life, but they tell different kinds of stories.

Not every fossil is a skeleton

Fossils include more than bones and shells.

Body fossils preserve evidence of organisms themselves. Trace fossils, such as tracks and burrows, record their activities. Microscopic fossils, including pollen and tiny shelled organisms, can also contribute to reconstructing past environments. [3]

This helps answer the question:

“If an animal’s body was never preserved, how could we know it existed?”

Sometimes its activity leaves evidence even when its body does not.

A trackway may show that an animal crossed a surface. A burrow may reveal an organism living within sediment.

But there are limits. A footprint does not always identify an exact species. A burrow does not necessarily reveal the complete anatomy of its maker.

Ask students to separate three statements:

  1. What can we observe?
  2. What can we reasonably infer?
  3. What remains uncertain?

That distinction is useful in every experimental science.

Exceptional fossils show us what we usually miss

Soft tissues can sometimes be preserved under unusual conditions. Exceptional fossil deposits may retain fine details that are absent from more ordinary collections. [2]

These discoveries matter because they broaden our view of ancient life beyond the organisms and structures that normally survive.

However, an exceptionally preserved site is still a sample of a particular environment and set of circumstances. It does not remove every bias.

A useful question is:

“Does this discovery show us a more complete community, or a different selection from it?”

Often, scientists must investigate both possibilities.

Missing evidence is not automatically evidence of absence

Suppose students recover many shells from a sediment sample but no soft-bodied animals.

Can they conclude that soft-bodied animals never lived there?

No. Poor preservation is one possible explanation.

Could the absence still matter?

Yes. It becomes more informative if comparable organisms would be expected to preserve, suitable rocks have been sampled thoroughly, and the sampling method could detect them.

“We did not find it” and “it was never there” are different claims.

Equally, an incomplete record does not mean that every imagined organism existed. Scientific explanations must remain tied to evidence.

Taphonomy helps researchers judge what a gap can reasonably tell them.

A classroom model of a biased sample

Imagine an ancient community containing:

  • 100 soft-bodied animals;
  • 20 shelled animals.

Now suppose, purely for illustration, that the recoverable fossil record contains:

  • 1 soft-bodied animal;
  • 10 shelled animals.

Shelled animals were a minority in the original community but dominate the recovered sample.

These are invented numbers for teaching, not measured fossilisation rates.

The lesson is straightforward:

The most common fossil in a collection was not necessarily the most common organism in the living community.

For an A-level extension, ask students to explain why counts of fossils cannot simply be converted into original population sizes without considering preservation, transport and sampling.

Why I would use this in tuition

What appeals to me about this investigation is the change in the questions students are encouraged to ask.

“Which fossil is this?” is a good starting point.

“How did this evidence survive?” takes the thinking further.

In my home laboratory, I would pair the immediate satisfaction of making an impression with the slower burial investigation. One produces something to examine within minutes; the other asks students to predict, wait, compare and reconsider.

It also gives biology, chemistry and geology a shared purpose. Students consider biological structures, decomposition, material properties, sediment and the interpretation of incomplete evidence.

At Hemel Private Tuition, this is the kind of practical enquiry I want to encourage: using observations to build an explanation, then checking how far that explanation can go.

The wider thinking extends well beyond a particular examination specification, even where fossils and evolution are already familiar syllabus topics.

The missing pages are part of the lesson

The fossil record has revealed an astonishing amount about the history of life.

Its incompleteness does not make it useless. It makes understanding preservation essential.

A shell, a leaf impression and a trackway each carry information. Each also leaves questions unanswered.

The next time you look at a fossil, ask two things:

“What does this show me?”

“What had to happen for this evidence to reach me?”

The history of life includes everything that once lived. The fossil record contains the evidence that survived.

Learning to understand the difference is part of learning to think scientifically.

11 November 2026

The Birthday Paradox — Why 23 People Are Enough

 


The Birthday Paradox — Why 23 People Are Enough

Put just 23 people in a room and there is already a better-than-even chance that two share a birthday. How can that possibly be true?

Best level: GCSE upwards
Area: Probability, complementary events, combinations and simulation
Investigation time: 20–40 minutes, with optional spreadsheet extensions

Make Your Guess Before Reading On

How many people would you need in a room before there is at least a 50% chance that two of them share a birthday?

By birthday, we mean the same day and month. They do not need to have been born in the same year.

Would you choose 100 people? Perhaps 150? Or would you halve 365 and suggest about 183?

The answer, under the usual simplified assumptions, is just 23.

With 23 people, the probability of at least one matching birthday is approximately 50.73%.

This is called the birthday paradox. There is no mathematical contradiction: the result simply conflicts with what many people expect.

What appeals to me as a teacher is that the surprise gives us a reason to investigate. A probability calculation becomes much more interesting when it challenges an answer we felt quite confident about.

The Question Your Brain May Have Answered Instead

Imagine standing in a room with 22 other people.

If you ask, “Does anyone here share my birthday?”, you are looking for one particular date. Each of the other 22 people gives you another opportunity to find it.

Under our simplified model, the probability that at least one of them shares your birthday is only about 5.86%.

But that is not the birthday paradox question.

We are asking:

“Do any two people in the room share a birthday?”

Two people on the other side of the room could share 17 March. Another pair could share 4 October. Your own birthday does not have to be involved at all.

Changing “my birthday” to “any birthday” changes the problem enormously.

This is a useful habit for students: before choosing a formula, say clearly what event you are trying to measure.

Twenty-Three People Create 253 Pairs

Start with a smaller group.

With two people, there is one pair to compare.

Add a third person and there are two new comparisons: the newcomer with each person already present. That gives three pairs altogether.

The fourth person adds three more comparisons. The fifth adds four.

For n people:

Number of pairs = n x (n - 1) / 2

We divide by two because comparing Alice with Ben is the same pair as comparing Ben with Alice.

For 23 people:

Number of pairs = 23 x 22 / 2 = 253

That is the first clue. A modest-sized group produces a surprisingly large number of comparisons.

However, 253 pairs does not mean 253 independent attempts. In particular, if Alice shares Ben's birthday and Ben shares Chloe's, Alice must share Chloe's too.

The pair count explains the intuition, but it does not by itself give the exact probability.

Solve the Opposite Problem First

Trying to count all the ways birthdays can match soon becomes awkward.

There could be one matching pair, two separate pairs, three people sharing a date, or several overlapping groups.

Fortunately, the opposite event is much easier:

Everyone has a different birthday.

For this calculation, assume that:

  • There are 365 possible birthdays; we leave out 29 February.
  • Every date is equally likely.
  • Different people's birthdays are independent.

Now imagine people entering the room one at a time.

Person 1: Any birthday is acceptable. There is nobody to match.

Probability of keeping all birthdays different = 365/365.

Person 2: Must avoid the first person's birthday.

Probability = 364/365.

Person 3: If the first two birthdays are different, must avoid both dates.

Probability = 363/365.

Person 4: Must avoid the three different dates already present.

Probability = 362/365.

Continue until person 23, who must avoid 22 dates:

Probability = 343/365.

Multiply these successive conditional probabilities:

P(no shared birthday) = (365/365) x (364/365) x (363/365) x ... x (343/365)

This gives approximately 0.4927.

Either everyone has a different birthday, or at least two people share one. These are complementary events, so their probabilities add to one.

P(at least one shared birthday) = 1 - P(no shared birthday)

P(at least one shared birthday) = 1 - 0.4927 = 0.5073

That is approximately 50.73%.

For 22 people the probability is about 47.57%, so 23 is the smallest group size that passes 50% in this model.

How Quickly Does the Probability Rise?

People in the roomDifferent pairsChance of at least one shared birthday
104511.69%
2019041.14%
2325350.73%
3043570.63%
4078089.12%
501,22597.04%

By 50 people, a match is very likely. But even 97.04% is not a guarantee.

In this 365-date model, a match becomes certain at 366 people. If you put 366 people into 365 birthday categories, at least one category must contain more than one person. This is an example of the pigeonhole principle.

Notice the distinction between likely and certain. Probability questions often depend on it.

Practical Investigation: Test a Real Group

A class, family gathering or club meeting provides a good starting point.

  1. Ask everyone to predict whether there will be a match.
  2. Record birthdays by day and month only; birth years are unnecessary.
  3. Agree in advance how to treat 29 February. For a direct comparison with the model, use simulated dates instead if this creates a complication.
  4. Check for repeated dates.
  5. Compare what happened with the prediction for that group size.

Participation can be voluntary, and anonymous date slips work well.

The important discussion comes afterwards.

A room of 23 people without a match has not disproved the mathematics. That outcome has a probability of about 49.27%: it is entirely ordinary.

Equally, finding a match in a group of ten does not disprove an 11.69% prediction. Less likely events still happen.

One group gives one outcome. To investigate the probability properly, we need many repeated groups.

Simulate Birthdays Without Finding Hundreds of Volunteers

Label the possible birthdays with the integers 1 to 365. We do not need actual calendar dates because we are only checking whether two values match.

For one simulated room of 23 people:

  1. Generate 23 independent random integers between 1 and 365.
  2. Look for any repeated number.
  3. Record “match” or “no match”.
  4. Generate a completely new room and repeat.

If using numbered slips, replace and mix each slip after every draw. Drawing without replacement would prevent the very repeats we are investigating.

After many rooms:

Estimated probability = rooms containing at least one match / total rooms tested

For example, if 512 out of 1,000 simulated rooms contain a match:

Estimated probability = 512/1000 = 0.512 = 51.2%

This is an illustrative result, not a reported experiment. Your results will vary.

A larger simulation generally gives a more stable estimate, but it does not guarantee that every new batch moves closer to the theoretical answer.

A Spreadsheet That Makes the Pattern Visible

Use Excel or Google Sheets to simulate 1,000 rooms.

Each row represents one room; each of 23 columns represents one person.

  1. In A2, enter =RANDBETWEEN(1,365).
  2. Fill across to W2, then down to row 1001.
  3. In X2, enter =IF(SUMPRODUCT(COUNTIF(A2:W2,A2:W2))>23,1,0).
  4. Fill X2 down to X1001.
  5. In a separate cell, enter =AVERAGE(X2:X1001) and format it as a percentage.

The formula in column X returns 1 when a room contains a match and 0 otherwise. If all 23 values are different, each occurs once and the count total is 23. Any duplicate pushes the total above 23.

Random-number formulas recalculate. Copy and paste values if you want to preserve a particular set of results before discussing it.

Repeat the investigation for 10, 20, 30, 40 and 50 people, adjusting the row range and the comparison number in the match formula. For example, 30 people require A2:AD2 and a comparison with 30.

Before running each simulation, predict the result. Then compare the experimental percentage with the table.

Three Tempting Mistakes Worth Discussing

“Surely the answer is about half of 365?”

This treats the calendar as though birthdays were filling dates without repetition. In our model, each new person can have any of the 365 birthdays, including one already present.

“There are 253 pairs, so the probability is 253/365.”

253/365 is about 0.693, but it is not the probability of at least one match.

It is the expected number of matching pairs. A room can contain several matching pairs, so that average count is different from the probability that a room contains any match at all.

Simply adding pair probabilities counts some rooms more than once.

“A 50% chance means exactly half our rooms must match.”

Probability describes a pattern across repeated trials. It does not impose a quota on a small experiment.

Even the theoretical probability here is slightly above 50%, rather than exactly half.

How Realistic Is the Model?

Real birthdays are not perfectly evenly distributed throughout the year. Leap days exist, and groups involving twins or selected birth dates can differ from a collection of independent random birthdays.

Those details affect the precise probability. The table describes a mathematical model, not an exact forecast for every real gathering.

That makes a useful extension: how would the result change if some dates were much more common than others?

As an extreme thought experiment, imagine that everyone could be born on only ten equally likely dates. Matches would become common in much smaller groups.

Students can explore a less extreme version by simulating a deliberately uneven distribution and comparing it with the uniform model.

Why I Like This as a Maths Lesson

The birthday paradox brings together several habits I want students to develop:

  • Define the event before calculating.
  • Count comparisons carefully.
  • Look for an easier complementary event.
  • Distinguish a model from the real world.
  • Compare experimental evidence with a theoretical prediction.

It also gives students permission to revise an initial guess. Being surprised is a useful starting point for learning.

At Hemel Private Tuition, I want students to see mathematics as something they can question, test and explain. Investigations like this can begin with GCSE probability and develop into combinations, conditional probability and computer simulation.

The most valuable moment is when a student can explain why the answer makes sense, rather than simply remember the number 23.

The Real Surprise Is the Number of Connections

Twenty-three people do not seem like many. Two hundred and fifty-three possible pairs feel rather different.

Our intuition notices the people. The mathematics makes us count the relationships between them.

That is the lasting lesson: when a problem involves any pair matching, the number of opportunities can grow much faster than we first expect.

Next time you are in a room of around 23 people, make a prediction. Then ask about birthdays. A familiar group can become a surprisingly good mathematics laboratory.

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