Safe starter experiments

All activities avoid hazardous chemistry, open flames for chemistry, high-voltage, pressurized gases, and biological agents. Suitable for young scientists/engineers with adult supervision where noted. Prefer simulation when wet/physical resources are limited.

Safety baseline: no strong acids/bases, no organic solvents beyond household vinegar/soap, no neodymium magnet swallowing risk for young children (use taped/embedded magnets), eye protection for any small-parts / elastic-band work.

Experiment 1 — Floating magnet tiles (2D magnetic self-assembly)

Goal: Watch discrete parts form ordered clusters from local attraction/repulsion rules.

Materials: Foam or cork tiles; small disc magnets (tape/embed so they cannot detach); shallow tray of water; ruler; phone camera. Optional: orbital shaker or gentle hand agitation.

Steps:

1. Embed magnets with known pole orientations (e.g., N-up vs S-up patterns, or edge magnets with assigned polarity).
2. Float 8–20 tiles on water; record initial disordered state.
3. Agitate gently for 30–60 s; stop; photograph.
4. Repeat at “low / medium / high” agitation (proxy for temperature/noise).

Measurement: Cluster size distribution; number of distinct motifs; time to first stable dimer/trimer.

Success criteria: At least one reproducible ordered motif appears more often than chance across 5 trials.

Discussion: Which rules (polarity patterns) favor chains vs closed shapes? How does excess shaking cause defects?

Experiment 2 — Cheerios / capillary “FloaTiles” effect

Goal: Observe capillary-driven assembly without magnets.

Materials: Bowl of water; Cheerios or 3D-printed/light floating shapes; dish soap (for a disruption trial); camera.

Steps:

1. Sprinkle pieces on still water; observe clustering at the surface.
2. Time how long until a connected cluster forms.
3. Add one drop of dish soap far from the cluster; observe change.

Measurement: Cluster diameter vs time; effect of soap (surface-tension change).

Success criteria: Clear before/after difference when surface tension is disrupted.

Discussion: How is this like/unlike molecular hydrophobic assembly? Why is soap a “rule change”?

Refs for educators: UCSD MRSEC experimental self-assembly ; FloaTiles 

Experiment 3 — Soap-bubble raft packing

Goal: See emergent crystal-like order from identical “particles.”

Materials: Dish soap + water + glycerin (optional); shallow tray; straw; phone camera; graph paper overlay.

Steps:

1. Blow a monolayer of similar-sized bubbles.
2. Photograph packing; count neighbors (coordination number).
3. Intentionally introduce a larger/smaller bubble; watch defect formation.

Measurement: Fraction of 6-neighbor bubbles; defect count.

Success criteria: Majority hexagonal packing in a calm raft; visible defects around size impurities.

Discussion: Polydispersity and defects — why monodisperse colloids matter in labs.

Experiment 4 — Paperclip / chain “foldamer” with local rules (tabletop algorithm)

Goal: Simulate algorithmic assembly with physical tokens (no chemistry).

Materials: Colored paperclips or LEGO bricks with labeled edges (A/a, B/b); printed rule card; timer.

Steps:

1. Assign complementary edges (A sticks only to a, etc.).
2. Start from a “seed” piece; add pieces only when a rule matches (cooperative binding optional: need 2 matching edges).
3. Compare greedy random attachment vs rule-constrained attachment.

Measurement: Yield of target shape over 10 runs; number of trapped wrong assemblies.

Success criteria: Rule-constrained protocol reaches target ≥70% of runs; random does worse.

Discussion: Connect to DNA sticky ends and aTAM “temperature” (how many bonds needed).

Experiment 5 — Kilobot / swarm simulation lab (software)

Goal: Recreate shape formation with local rules in simulation.

Materials: Laptop; Kilombo / ARGoS / NetLogo / Python agent-based sim (pick one available); target shape bitmap.

Steps:

1. Implement robots with: edge-following, distance-to-seed estimate, join-if-inside-shape.
2. Run N = 50, 200, 500 agents; inject 5–10% “failed” robots.

Measurement: Fraction of shape filled; completion time; defect holes.

Success criteria: Shape recognizable at N≥200 with ≤10% failures still completing outline.

Discussion: Why local communication scales; what breaks in cluttered environments.

Media tie-in: Harvard Kilobot swarm video 

Experiment 6 — Cellular automaton / tile assembly toy model

Goal: Connect self-assembly to computation.

Materials: Computer; Python or online CA tool; optional physical tiles printed on cardstock.

Steps:

1. Code a 1D or 2D tile assembly with glue strengths 0/1/2 and a temperature threshold.
2. Grow from a seed; try an XOR or binary-counter tile set (simplified).
3. Randomly flip glue matches with probability p (errors).

Measurement: Correct structure length/area vs p.

Success criteria: Plot showing sharp drop in yield as p increases; write 1 paragraph relating to experimental DNA tile error.

Discussion: Why proofreading / redundancy appears in advanced designs.

Experiment 7 — Patchy-particle packing with 3D-printed modules (dry)

Goal: Explore geometry-directed assembly.

Materials: 3D-printed tetrahedra/cubes with Velcro or embedded magnets on chosen faces; tray; optional mild vibration plate.

Steps:

1. Design two species: “only face X binds.”
2. Shake gently in a box; separate bound dimers/trimers.
3. Compare isotropic Velcro-all-faces vs patchy designs.

Measurement: Fraction of intended bonds vs unintended.

Success criteria: Patchy design shows ≥2× higher specificity than isotropic control.

Discussion: Directional interactions in DNA origami / colloidal patchy particles.

Experiment 8 — “Programmable matter” reconfiguration on a grid

Goal: Experience distributed reconfiguration cost.

Materials: Checkerboard; stackable cubes or sliding pixels; printed start/goal shapes; stopwatch. Optional: micro:bit/LEGO robots pushing blocks.

Steps:

1. From shape A to shape B using only local moves (slide to empty neighbor; keep connectivity if required).
2. Compare centralized planner (one student directs) vs local rules (each cube follows a simple priority).

Measurement: Move count; time; connectivity violations.

Success criteria: Both methods reach goal; students quantify move overhead of local rules.

Discussion: Why programmable-matter algorithms care about connectivity and parallel moves.

Experiment 9 — Evaporative coffee-ring / crystal demo (household, observational)

Goal: See nonequilibrium patterns from drying — contrast with designed assembly.

Materials: Coffee or food coloring in water; glass slide or white plate; lamp; camera.

Steps:

1. Place drops of different concentrations; let dry undisturbed.
2. Photograph rings/patterns; vary humidity/airflow gently with a fan at low setting.

Measurement: Ring width vs concentration (qualitative OK).

Success criteria: Reproducible coffee-ring observation; written contrast vs “programmed” sticky-end assembly.

Discussion: Not all self-organization is useful/designed — kinetics and evaporation create patterns “for free.”

Safety: Food-safe materials only; no solvents.

Experiment 10 — DNA origami design assignment (simulation / CAD only)

Goal: Learn scaffolded origami workflow without wet lab.

Materials: Computer; cadnano / oxDNA / online DNA design tutorials; Rothemund 2006 figures (author PDF ).

Steps:

1. Sketch a simple 2D silhouette (heart, letter, triangle).
2. Route a scaffold conceptually (or in cadnano if available).
3. List staple roles; estimate approximate size (~100 nm classic origami).
4. Write a mini “methods” section: anneal, verify with AFM (describe, don’t perform if unequipped).

Measurement: Completeness of design checklist; peer review of staple/scaffold understanding.

Success criteria: Correct explanation of scaffold vs staples; scale bar estimate; one limitation noted (cost, yield, imaging).

Discussion: Why educational DNA wet labs are rare (cost, equipment) — simulation is a valid scientific skill.

Safety: No oligonucleotides ordered for young students unless under proper lab program.

Optional extension — NetLogo “emergence” gallery

Compare flocking (dynamic self-organization) vs tile assembly (static programmed structure). Write a one-page essay: which is closer to Kilobots? to DNA crystals?

Shared assignment rubric