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Montclair State University / CESAC

A foundation for circular solar research

The Solar Revitalizer™ began with a broad question: how can assessment, data and recovery research help preserve more value from photovoltaic modules? A completed feasibility project with Montclair State University’s Clean Energy and Sustainability Analytics Center (CESAC) helped frame that question and the work that should follow.

CompletedResearch case study2023–2024
Research periodMay 10, 2023 – May 10, 2024
SupportNew Jersey CSIT R&D Voucher
SBX responsibilityResearch direction and product priorities
Research stageCompleted feasibility; next-stage integration remains proposed
Research question → Feasibility → Next validation

A completed foundation.
A sharper next experiment.

Circular-PV research, data exploration and customer discovery helped define the next module-intake study.

The research question

The project explored the end-of-life solar-panel landscape and the potential role of machine learning in recycling and refurbishment decisions. It considered technical pathways, economic questions and the environmental context rather than treating recycling as a single uniform process.

What the project delivered

Completed feasibility outputs and how they shape the next study.
DeliverableWork documentedHow it informs the next stage
Research poster & companion presentationCircular-PV landscape and machine-learning research questions.Focus the next study on a defined module-intake decision.
Solar-data explorationPotential source data and environmental-performance questions.Define provenance, usable inputs and analytical limitations.
Customer-discovery supportMarket segments, outreach lists and a proposed survey.Choose operator workflows, evidence needs and useful evaluation measures.
Prototyping resources3D-printing equipment and associated prototyping materials.Support future material and part experiments after feedstock qualification.

From concept to funded research.

Frank Jackson originated Solar Revitalizer™ and led its translation from a circular-solar concept into a funded research program. As SBX’s principal investigator and product lead, he secured CSIT support, defined research priorities and the university scope, recruited scientific and engineering contributors, and managed budgets, milestones and delivery obligations.

He connected customer discovery, technical research and commercialization planning so each phase informed the next development decision. Montclair State University collaborators contributed specialist research, analysis and modeling. The voucher-funded project met its agreed objectives on schedule and within budget, establishing the foundation for SBX’s next diagnostic and routing study.

Inspect the coauthored research

The 2024 poster connects a technical question with an economic one: where could machine learning help, and would the resulting pathway make sense over the lifecycle? The feasibility work produced landscape synthesis, research questions and a proposed methodology.

Today’s proposed intake study narrows that agenda to test selection: when does another optional measurement or repeat capture improve the next module decision enough to justify its cost?

From the 2024 research poster

Two questions that shaped the research.

  1. How can machine learning be seamlessly integrated into recycling and refurbishing processes of solar panels to minimize waste?
  2. What are the long-term economic benefits of machine learning-optimized solar panel recycling, considering the total lifecycle costs and environmental impact compared to landfilling?
Inspect the original poster excerptOriginal poster research questions, transcribed aboveOpen the excerpt at full size ↗
Research-question excerpt from Recycling Solar Panels for a Circular Economy (2024). Sarahana Shrestha, Pankaj Lal, Archana Prasad, Frank Jackson and Aparna Varde; Montclair State University / CESAC and SBX.

What follows

The next phase investigates optional testing and routing for a defined retired-module population. It requires documented module access, qualified safety and reference tests, repeatable acquisition and a fair comparison with efficient fixed and established adaptive workflows. The completed feasibility project provides a foundation for this separate physical study.

Continue into the architecture.

See how this work informs the next research question.

Review the proposed diagnostics study