Advancing bio-integrated building systems: a systematic review of architectural, structural and mechanical–electrical challenges in photobioreactor façades
Bibliographic record
Abstract
Purpose This study evaluates the feasibility of photobioreactor (PBR) façades across architectural, structural and mechanical–electrical (MEP) domains, identifying interdisciplinary gaps and proposing scalable solutions to advance their global implementation. Design/methodology/approach A PRISMA 2020-guided systematic mixed-methods review analyzed 82 studies (2013–2025) from six databases and preprint repositories. PICOS criteria structured the evaluation of building-integrated PBR systems against conventional façades, emphasizing energy efficiency, carbon sequestration and scalability. Dual independent screening (κ = 0.82) ensured reliability, with thematic categorization into architectural, structural and MEP domains. Findings Architectural aspects dominated research (91.6% of studies), emphasizing thermal performance, environmental benefits and climate consideration. Structural engineering received limited attention (15.7%), with gaps in dynamic load management for high-rise applications, despite innovations in graphene-reinforced polymers and 3D/4D printing. MEP systems (47% coverage) demonstrated AI-driven optimization, reducing energy consumption by 25–30%, though system complexity persisted. Geospatial analysis revealed 35% of studies concentrated in Iran, the US and Canada, neglecting climate-vulnerable regions. Modular design and hybrid energy grids emerged as critical enablers for scalability. Research limitations/implications The temporal scope (2013–2025) may exclude post-2025 innovations. Structural data scarcity limits conclusions on high-rise adaptability. Future studies should prioritize real-world validations in extreme climates. Practical implications Policymakers should adopt biomimetic certifications incentivizing cross-domain integration. Practitioners can leverage AI-driven modular designs and decentralized energy networks. Open-source prototyping hubs in vulnerable regions are urged to align innovations with local needs. Originality/value This review introduces the first integrative framework addressing PBR façade implementation across three interdisciplinary operational domains.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".