昆山狄邦华曜学校 · Student Research Programme
Advanced Composite
Materials Laboratory
Structural composites & environmental filtration — researched through 3D-printed specimens, bio-coating experiments, and controlled load testing.
ACM-Lab studies how lattice geometry, bio-coating composition, and fabrication parameters influence structural performance and environmental filtration — documented from hypothesis to measured outcome across two integrated tracks.
Track A — Structural
3D-printed PLA lattices in uniform and gradient geometries, loaded to failure under standardised conditions. We measure how geometry controls stiffness, failure mode, and strength-to-weight ratio.
Track B — Environmental
Chitosan biopolymer at 2% w/v applied across three coating cycles to PLA substrates. Planned evaluation: dye removal rate, flow resistance, and reuse stability in gyroid-geometry cartridges at 40% infill.
Structure
3D-printed PLA lattices tested for load distribution, failure mode, and strength-to-weight performance — comparing uniform and gradient geometries under standardised loading.
Gradient vs. uniform · Failure mode mapping · Load testing
Interface
Chitosan biopolymer at 2% w/v applied across three coating layers — evaluated for mass gain, water resistance, and adhesion quality under structural loading.
2% w/v · 3-layer protocol · Mass gain per layer
System
Composite adsorbent designed for gyroid-geometry cartridges at 40% infill — planned evaluation of dye removal rate, flow performance, and reuse stability in future trials.
Planned: dye removal · flow rate · reuse stability · Gyroid 40% infill
Research Commons
ACM-Lab operates as a student research commons where fabrication, material preparation, testing, documentation, and publication are divided into clear responsibilities.
Research strategy, team coordination, experimental planning, and project oversight.
Guides experimental safety, research direction, and experimental standards. Reviews research outcomes and provides mentorship to the team.
Oversees research strategy, experimental planning, team workflow, and project documentation. Maintains website as a public-facing research archive.
Manages team coordination, tracks experiment progress, organises meetings and milestone records, and supports material preparation documentation.
3D modeling, print operations, prototype assembly, and sample fabrication for structural and filtration prototypes.
Leads 3D modeling (Shapr3D) and external print coordination. Translates lattice geometries into filtration and structural prototypes. Supports solution preparation and lab work.
Turns CAD designs into physical samples. Supports 3D printed prototype fabrication, structure assembly, and pre-experiment sample preparation.
Operates 3D printers, configures print parameters, and oversees print quality for lattice structures, truss bridges, and filtration prototypes.
Chitosan composite synthesis, coating application protocols, solution preparation, and filtration system evaluation.
Leads chitosan composite experimental design, develops solution synthesis protocols, oversees data logging, and troubleshoots formulation inconsistencies.
Works on 3D printed filtration system design, filter cartridge testing, flow rate measurement, and filtration system prototype evaluation.
Mechanical load testing, failure mode observation, data collection, and experimental performance analysis.
Runs truss bridge load-bearing experiments, observes and records failure modes, and measures load-at-failure and strength-to-weight data.
Organises experimental data, creates graphs, and supports comparison of load capacity, adsorption indicators, flow rate, and material cost data.
Research documentation, experiment logs, outreach materials, project budgeting, and cost-performance evaluation.
Maintains experiment logs, writes research reports, prepares presentation copy and poster content, and produces team introduction materials.
Manages experimental cost control, material budgeting, and cost-performance analysis. Handles project resource management and feasibility evaluation.
Each experiment follows this sequence from initial question to documented output.
Six systems ensure every experiment produces a usable, traceable record.
Standardised preparation steps shared across teams to ensure consistent experimental conditions between runs.
Timestamped records of each experiment: variables controlled, observations made, and outcomes noted.
Structured entries documenting what failed, the likely cause, what was isolated, and what was adjusted.
Lab photos attached to experiment entries as visual evidence of fabrication and testing steps.
Metrics identified as evaluation targets for future test rounds, labelled as pending until measured.
Research summaries and poster drafts prepared for internal review before any external sharing.
Mission
Research Programme · ACM-Lab · Dipont Huayao School Kunshan
ACM-Lab studies how material composition, lattice geometry, and fabrication parameters affect structural performance and environmental filtration behavior — documented from hypothesis through controlled testing across two integrated research tracks.
How do lattice geometry, gradient distribution, and bio-composite coating affect load transfer efficiency, failure mode, and strength-to-weight ratio?
Which adsorbent formulation and 3D-printed cartridge geometry combination achieves optimal dye removal, flow throughput, and reuse stability?
Research Design
Research Process
Lattice density · coating condition · channel geometry
3D printing · dip-coat protocol · ionic gelation
Compression loading · adsorption test · filtration run
Failure mode · absorbance reading · flow rate
Geometry or formulation adjustment based on results
Lab Evidence
Assembling PLA lattice truss bridge specimens — aligning members and preparing for load testing.
Four variants — A1 uniform, A2 uniform+coated, A3 gradient, A4 gradient+coated — for load-path comparison.
Weighing and dissolving chitosan at 2% w/v in 1% acetic acid solution before dip-coating.
Dip-coating PLA lattice with chitosan solution — 1–3 layers, mass gain recorded per cycle.
Magnetic stirring to dissolve chitosan — controlled for consistent coating viscosity across specimens.
Research conducted under faculty mentor guidance in a professional materials laboratory setting.
Compressive load applied to PLA lattice specimens — recording peak load, deflection, and failure mode for coated vs. uncoated comparison.
3D-printed filtration cartridge — 90+ hr build on Flashforge, chitosan-coated lattice insert for MB dye adsorption evaluation.
Research Modules
How do lattice density, truss geometry, and gradient distribution affect load transfer, failure mode, and strength-to-weight ratio?
Can a chitosan bio-coating improve surface durability and water resistance without significantly increasing mass or weakening the structure?
Which alginate–carbon adsorbent formulation achieves the best MB dye removal while remaining structurally stable over multiple reuse cycles?
Which cartridge geometry and adsorbent combination best balances removal efficiency, flow rate, and structural stability?
Current Phase
No filtration efficiency, dye removal rate, or structural improvement is claimed without measured experimental evidence. All forward-looking items are planned evaluation.
Experiments Log · ACM-Lab · Dipont Huayao School Kunshan
A structured record of every fabrication, coating, and testing session — each entry documents controlled variables, observed outcomes, and honest data status at each phase of the research process.
Experimental Timeline · 8 Entries · Mar 2026 — Mar 2027
Research framework session — team defining experimental groups and controlled variables
Prototype evidence — PLA lattice bridge variants assembled in lab
Lab evidence — chitosan powder weighing and solution preparation (2% w/v, acetic acid solvent)
Coating protocol — chitosan solution applied to specimens before load comparison testing
Lab preparation — MB dye solution and adsorbent bead formulation work
3D printing completed — 90 hr 32 min build time for lattice geometry prototype
Filtration prep — dye solution and flow-through setup for performance evaluation
ACM-Lab research team — cross-track members behind both the structural and filtration programmes
Experimental Control Matrix
Planned Measurements
Research Workflow
Documentation
A structured record of what did not work as expected, why we believe it happened, and what we changed in response. Each entry documents an observation from ongoing experiment cycles.
"A failure that is documented and understood is more valuable than a result that cannot be explained."
Lab photo — chitosan batch preparation
Lab photo — coating application test
Lab photo — drying test setup
Lab photo — bridge prototype nodes
Lab photo — test jig assembly
Printed lattice members showing assembly tolerance issue.
Surface roughness and stringing observed on printed PLA lattice members.
Bridge assembly alignment condition before structural testing.
Uneven coating access inside lattice openings.
Coating accumulation around lattice joints and corners.
Coated specimens prepared for layer-by-layer mass measurement.
Surface condition after drying under the current coating protocol.
Early load-test setup showing loading position and support alignment.
Bridge specimen after testing with failure region to be marked.
Early documentation showing incomplete linkage between sample condition and photo evidence.
Each row maps to one logged case. Root cause categories help identify patterns across failures.
| ID | Title | Root cause category | Phase | Status |
|---|---|---|---|---|
| F-01 | Chitosan viscosity inconsistency | Process control — temperature, time | A | Protocol Updated |
| F-02 | Uneven coating distribution | Application method — surface tension | A | Protocol Updated |
| F-03 | Sample warping during drying | Residual stress — differential evaporation | A | Protocol Updated |
| F-04 | Node stress concentration fractures | Geometry — stress concentration factor | B | Under Evaluation |
| F-05 | Load test repeatability variance | Measurement — jig alignment, load rate | B | Under Evaluation |
| F-06 | Print tolerance mismatch during lattice assembly | Fabrication — printer tolerance, shrinkage, clearance | A | Protocol Updated |
| F-07 | Surface roughness inconsistency on PLA members | Fabrication — print temperature, speed, retraction | A | Under Evaluation |
| F-08 | Bridge alignment inconsistency before load testing | Assembly — accumulated tolerance, warping, joint contact | A | Protocol Updated |
| F-09 | Coating accessibility issue inside lattice openings | Geometry — lattice opening size, viscosity, drainage | B | Under Evaluation |
| F-10 | Local coating accumulation near lattice joints | Application — surface tension, drainage resistance | B | Protocol Updated |
| F-11 | Layer-to-layer coating mass variation | Process — drainage time, surface area, layer count | B | Under Evaluation |
| F-12 | Non-uniform drying between coated surfaces | Drying — thickness, orientation, ambient conditions | B | Protocol Updated |
| F-13 | Load point placement uncertainty | Measurement — loading position, support spacing | B | Under Evaluation |
| F-14 | Failure location not consistently marked after testing | Documentation — photo angle, marking protocol | B | Protocol Updated |
| F-15 | Sample identity and evidence records not fully linked | Data logging — naming convention, record format | B | Protocol Updated |
Record the unexpected result precisely — what was expected vs what happened.
Identify the most probable variable responsible, without claiming certainty.
Change one variable at a time in the next experiment to test the hypothesis.
Update the protocol or design based on what the isolated test showed.
Run the revised protocol and document whether the failure mode is resolved.
This log separates actual observations from planned tests. Entries marked Planned check, Next iteration, or Risk under evaluation have not yet been tested. No performance claims, dye removal rates, filtration efficiencies, or strength improvements are stated here unless directly supported by a documented experiment entry.
Benchmark
Research Commons
Post experiment reports, protocol notes, failure records, and progress updates. Your post appears in Research Commons for the whole team to see.
Log in to publish research posts to Research Commons.
Research Commons
Working drafts, protocol notes, experiment updates, failure analyses, and internal documentation from active lab research.
No research posts have been published yet.
Community — Personal Timeline
Community is a personal contribution timeline connected to the main ACM-Lab feed. Post photos, short updates, and lab snapshots here. Friends can view and comment on your community updates.
Post photos from today's printing session, coating experiment, or lab setup. Add a short caption.
Record an observation or measurement that isn't ready for a full Community post yet.
See what your connected ACM-Lab friends have been posting to their personal timelines.
ACM-X Materials Lab
Design a sustainable composite adsorbent.
Simulate flow, capture, cost, and failure risk.
Connect the prediction to real ACM-Lab experiments.
ACM-X Materials Lab
A parameter-driven simulation environment for sustainable composite adsorbents, microplastic capture, and 3D-printed filtration systems.
This module models how material composition, lattice density, coating thickness, and operating conditions interact to determine filtration efficiency, pressure drop, and structural durability — bridging virtual design with physical ACM-Lab prototypes.
Choose adsorbents — chitosan, biochar, activated carbon, or hybrid matrices — ranked by removal capacity and environmental footprint.
Set pore diameter, mesh density, layer count, and channel geometry to balance hydraulic resistance against adsorption contact time.
Visualise microplastic transport, adsorption kinetics, pressure drop, and breakthrough risk across your full parameter space.
Balance capture efficiency, pressure drop, structural integrity, and sustainability — then upload ACM-Lab experimental data to close the loop.
Research Workflow
Build a composite material genome, run the adsorption-filtration model, and record evidence that can directly support ACM-Lab physical experiments.
3D Structure Parameters
Focus on manufacturable settings: pore diameter, mesh density, layer count, pressure, pH, and reuse cycles.
Digital Twin Control System
Simulate flow velocity, particle capture, adsorption efficiency, pressure drop, and cost-performance tradeoffs inside a 3D-printed composite filter.
Section 3
Section 5
This graph set helps identify the tradeoff between removal efficiency and hydraulic resistance.
Section 6
Section 7
Section 8
Select 2-3 saved trials in the notebook to compare engineering performance.
Bridge Constructor Lab
Design a 3D-printed lattice bridge, tune material placement and chitosan bio-coating, then run a cinematic load-crossing test across the valley.
Composite Genome Project
Mapping sustainable composite materials through data, simulation, and engineering optimisation for 3D-printed filtration systems and functionally graded structural design.
Filter Materials
Best Match
Showing 10 materials
Side-by-Side Material Comparison
Performance Radar
Research Goal
Priority Weights (adjust to rerank)
Set your priorities and click Generate Recommendation.
Composite Material Digital Twin — Parameter-Driven Prediction Model
Adjust composite parameters to predict material performance. Simplified research model — prototype dataset.
Material Parameters
Research Insights
Key findings from ACM-Lab's composite materials analysis programme — internal research model.
Biochar-chitosan hybrid composites are projected to achieve the highest combined adsorption efficiency and sustainability score across simulated configurations, making them the primary candidate adsorbent layer for low-cost 3D-printed filtration systems — pending physical validation.
Carbon fibre reinforcement is projected to improve structural strength by up to 73% relative to PLA baseline based on material property modelling, but significantly reduces cost efficiency and sustainability score. The design guidance is to reserve it for critical structural zones — pending validation through physical bridge load testing.
Applying high-performance coatings only to high-stress or high-contact zones reduces material cost by 30–45% while maintaining equivalent performance to uniform application across the full composite structure.
Multi-objective optimisation — balancing structural strength, adsorption efficiency, fabrication cost, and sustainability — consistently produces better engineering outcomes than single-metric maximisation strategies.
Material intelligence that integrates structural performance data with filtration efficiency prediction and digital twin simulation creates a closed research loop — enabling faster parameter-driven optimisation across ACM-Lab's core research tracks.
Sustainability Performance Matrix
Material classification by structural performance vs. sustainability index — internal research model.
Research Intelligence Center
Prototype data architecture, simulation evidence, and optimization planning for future sustainable composite materials experiments.
ACM-Lab's pre-experimental research intelligence system — mapping independent variables, control conditions, simulation benchmarks, and validation targets before physical testing begins. Rigorous experimental design is the foundation of credible materials science.
Prototype Planning Framework · Simulation-Based Benchmarks · Validation Pending Physical Testing
About this platform: This dashboard currently uses prototype and simulation-based values to define ACM-Lab's future experimental data structure. All metrics, benchmarks, and dataset values are planned experimental targets informed by materials science literature — not validated laboratory results from physical testing. Final values will be updated after experiments are completed.
Experimental Variable Planning Matrix
Pre-experimental research design records defining independent variables, dependent variables, control conditions, target benchmarks, and measurement basis for each planned experiment. Organised by project track to demonstrate systematic variable planning before physical testing begins.
Adsorption Performance — Simulated Scenario
Projected adsorption efficiency benchmarks and flow-capture scenarios for planned filtration material experiments. Simulated Model — values are design targets, not validated lab measurements.
Projected Adsorption Efficiency — Simulated Scenario
Coating Thickness vs Flow — Prototype Model
Simulation modelling suggests that a two-layer bio-based coating (chitosan + glycerol toughener) could improve projected adsorption by ~6–10% without excessive pressure drop, provided porosity remains above 45%. Coatings beyond 3 layers are modelled to introduce flow resistance below practical thresholds. These are planning hypotheses to be validated by physical filtration experiments.
Structural Load Efficiency — Prototype Model
Projected load-to-cost ratios and failure mode scenarios for planned bridge and truss geometry experiments. Prototype Model — values are simulation-based design targets pending physical validation.
Structure Type vs Projected Load Efficiency — Prototype Model
Projected Failure Mode Distribution — Design Scenarios
Simulation modelling projects triangular truss geometry at 3.8× load-to-cost ratio — 171% above straight-beam baseline. Chitosan-glycerol coating is modelled to reduce crack-induced failure by ~14% at high-stress nodes. Mid-span bending is identified as the highest-risk failure mode (41% of design scenarios), addressable through diagonal bracing and lattice density gradient. These projections are based on simulation and design analysis — not yet validated by physical load testing.
Cost-Performance Optimization Matrix
Projected material classification by simulated performance vs. estimated cost efficiency. Positions are based on literature-informed design targets — not validated ACM-Lab experiment results. Simulation-Based Planning Model.
The optimal engineering material is not necessarily the strongest material, but the system with the best projected performance-to-cost and sustainability balance. Based on simulation modelling, Biochar-Chitosan Hybrid and Chitosan Bio-Coating are positioned in the projected optimal quadrant — high performance at low-to-moderate cost. These positions will be validated by future physical experiments.
Failure Intelligence System
Planned failure scenario analysis — design-stage risk identification and preventive strategy for future experiments. Design Scenarios — not all failure cases have been physically observed yet.
Research Progress Intelligence Timeline
Planned research milestones and forward decision points across ACM-Lab project phases. Completed phases are indicated; future phases are planned targets.
Next Optimization Targets
Hypothesis-driven experimental priorities derived from simulation analysis, variable gap identification, and design model review. Each target defines the next physical experiment with specific measurable objectives.
Research Collaborators
AI Research Communication Hub
Composite Lab Control Center
Research Verification System
Upload video, CSV, SEM images, raw data, and experimental conditions for credibility checks.
Shows a badge for reliable contributors, strong research identity, and documented work.
Future badge for high-quality materials experiments, AI optimization, and public evidence.
AI can flag abnormal data, missing controls, weak methods, or unsupported claims.