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GP-PH-10VLDXAPhysicsReady

Stress-Protocol Comparability and Degradation Attribution in Perovskite Solar Cells

A completed physics study comparing perovskite solar-cell stability protocols while keeping stress definition, operating point, recovery, diagnostics, replication and module relevance visible.

Stress-Protocol Comparability and Degradation Attribution in Perovskite Solar Cells project visual
GP-PH-10VLDXA · Physics
  • Python 3.12
  • NumPy
  • pandas
  • Matplotlib

Software compatibility

Python 3.12.14 or later on macOS, Linux, and Windows

The released results use the frozen 30 August 2026 evidence catalogue, pinned Python packages, 20,000 uncertainty draws per protocol, and random seed 20260830. New evidence or changed positions require a fresh validation run.

Project definition

Problem statement

Perovskite stability results are difficult to compare when studies use different environments, temperatures, spectra, circuit conditions, encapsulation, baselines, recovery intervals and lifetime metrics.

The physics problem is to determine when two stress tests support a valid comparison and when a performance loss can be attributed to a bounded degradation mechanism.

Project objectives

  • Compare ten perovskite stability protocol archetypes across eight degradation mechanisms.
  • Apply hard gates for stress definition, reporting completeness, stabilized performance and mechanism applicability.
  • Separate reversible performance change from irreversible degradation.
  • Map thirteen validation criteria across eleven experimental stages.
  • Quantify evidence-position uncertainty with 20,000 fixed-seed draws per protocol.
  • Retain complete results, figures, tests, references and editable documentation.

Project structure

Project components

01

Protocol register

Compares dark storage, damp heat, continuous light, MPP operation, light-dark cycling, electrical bias, thermal cycling, outdoor exposure and combined stress.

02

Mechanism map

Links moisture, oxygen, heat, ion migration, phase segregation, interfaces, bias and thermomechanical failure to relevant tests.

03

Comparability model

Scores twelve experimental-design dimensions and rejects unsupported comparisons through hard gates.

04

Attribution model

Keeps recovery measurement, diagnostic evidence, alternative explanations and reversible-loss confounding visible.

05

Workflow FMEA

Ranks 143 stage-criterion combinations from protocol selection and calibration through diagnostics and cross-study comparison.

06

Verification pipeline

Regenerates the study, tests the model, validates the documents and reproduces the analysis in Docker.

Methodology

Project workflow

  1. 01
    Define the claim

    Specify device population, use context, relevant failure mode and permitted conclusion.

  2. 02
    Calibrate the stress

    Record chamber conditions, device temperature, spectrum, irradiance, atmosphere and electrical operation.

  3. 03
    Track stabilized output

    Retain MPP behaviour, current-voltage components, interruptions, baseline and censoring.

  4. 04
    Measure recovery and diagnostics

    Separate reversible change and test structural, chemical, optical and electrical mechanism evidence.

  5. 05
    Review transfer

    Inspect context, module relevance, uncertainty and outdoor correlation before making a lifetime claim.

Demonstration scenario

The retained analysis shows why controlled MPP light soaking, light-dark cycling, combined stress and outdoor exposure answer different questions. A long dark-storage test cannot override weak operational relevance, and a falling efficiency trace cannot by itself prove ion migration or irreversible degradation.

Engineering

Tools and method

Tools
The project uses Python 3.12, NumPy, pandas, Matplotlib for subject analysis, simulation, and results.
Declared inputs
Python data structures retain every protocol position, mechanism link, evidence case and decision boundary.
Deterministic analysis
NumPy and pandas generate all 1,280 protocol, mechanism, context and evidence cases.
Risk analysis
Validation criteria, reporting crosswalk and FMEA expose calibration, tracking, recovery, diagnostic and comparison risks.
Uncertainty
Fixed-seed Monte Carlo and one-factor sensitivity test stability of the declared evidence positions.
Evidence
CSV, JSON, PNG, PDF and Word files retain the complete analysis, source catalogue and report.
Release verification
Tests, linting, vulnerability audit, document QA, repository validation and Docker verify the handover.

Testing

Evaluation

Evaluation measures

  • Comparability score and hard-gate result by protocol and mechanism
  • Stress, environment, temperature, illumination and electrical-operation quality
  • Stabilized output, time-series, recovery, diagnostics and replication positions
  • Severity-weighted validation gaps and workflow FMEA priorities
  • Reporting crosswalk and assessment-context reversals
  • Monte Carlo intervals and evidence-position sensitivity

Project boundaries

  • All numerical inputs are literature-informed evidence positions rather than measurements from a common experiment.
  • The study does not predict device or module lifetime and does not establish an acceleration factor.
  • It does not prove a unique degradation mechanism or certify a material, cell, module or product.
  • It does not replace IEC qualification, outdoor correlation, calibrated testing or warranty evidence.
  • A real programme requires matched device populations, controlled stresses, recovery measurements, diagnostics and independent review.

Included

  1. 01Complete Python source and declared study configuration
  2. 021,280 protocol, mechanism, assessment-context and evidence cases
  3. 03Ten stress protocols and eight degradation mechanisms
  4. 04Thirteen validation criteria and a 143-cell workflow FMEA
  5. 05Twelve generated figures and one attributed CC BY 4.0 literature figure
  6. 0677-page project report in PDF and editable Word formats
  7. 0715-page project and defence guide in PDF and editable Word formats
  8. 08Fifty annotated references with evidence boundaries and source matrix
  9. 09Automated tests, repository validation and Docker reproduction

Project record

No information is collected on this page.

Permanent project ID
GP-PH-10VLDXA
Catalogued
21 Aug 2026
Completed
30 Aug 2026
Verified
30 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.