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Optimization of the Thermal Performance of Na2HPO4·12H2O-Based Gel Phase Change Materials in Solar Greenhouses Using Machine Learning paper
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Paper

Optimization of the Thermal Performance of Na2HPO4·12H2O-Based Gel Phase Change Materials in Solar Greenhouses Using Machine Learning

0032a2bab84710900956dc988ff9e258fa0685a1
Free2AITools Nexus Index
61.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 68
P: Popularity 43
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

In the design of gel phase change composite wall materials for solar greenhouses, the alteration of material composition could directly affect the thermal performance of gel phase change composite wall materials. In order to obtain better suitable gel phasechange composite wall material for solar greenhouses, Na2HPO4·12H2O-based gel phasechange materials with different content of ingredient (Na2SiO3·9H2O, C35H49O29, KCl, and nano-α-Fe2O3) were obtained via the Taguchi method and machine learn...

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Semantic Scholar 3 Citations
Paper Information Summary
Entity Passport
Registry ID 0032a2bab84710900956dc988ff9e258fa0685a1
License ArXiv
Provider semantic_scholar
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Academic & Research Attribution

BibTeX 
@misc{0032a2bab84710900956dc988ff9e258fa0685a1,
  title = {Optimization of the Thermal Performance of Na2HPO4·12H2O-Based Gel Phase Change Materials in Solar Greenhouses Using Machine Learning Paper},
  howpublished = {\url{https://api.semanticscholar.org/0032a2bab84710900956dc988ff9e258fa0685a1}},
  note = {Accessed via Free2AITools.}
}
APA Style
Optimization of the Thermal Performance of Na2HPO4·12H2O-Based Gel Phase Change Materials in Solar Greenhouses Using Machine Learning [Paper]. Free2AITools. https://api.semanticscholar.org/0032a2bab84710900956dc988ff9e258fa0685a1

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⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 68
Popularity (P) 43
Recency (R) 100
Quality (Q) 65

💬 Index Insight

FNI V2.0 for Optimization of the Thermal Performance of Na2HPO4·12H2O-Based Gel Phase Change Materials in Solar Greenhouses Using Machine Learning: Authority (A:68), Popularity (P:43), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"In the design of gel phase change composite wall materials for solar greenhouses, the alteration of material composition could directly affect the thermal performance of gel phase change composite wall materials. In order to obtain better suitable gel phasechange composite wall material for solar greenhouses, Na2HPO4·12H2O-based gel phasechange materials with different content of ingredient (Na2SiO3·9H2O, C35H49O29, KCl, and nano-α-Fe2O3) were obtained via the Taguchi method and machine learn..."

❝ Cite Node 

@article{Optimization,
  title={Optimization of the Thermal Performance of Na2HPO4·12H2O-Based Gel Phase Change Materials in Solar Greenhouses Using Machine Learning},
  note={Indexed by Free2AITools}
}

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📊 Research Signals

📈3CitationsSemantic Scholar
🏛️68AuthorityFNI pillar
⏱️100RecencyFNI pillar
✅65QualityFNI pillar
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📦Data Source: semantic_scholar
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source
semantic_scholar
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Unknown
license
ArXiv
tags
paper, research, academic

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citations
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