Comprehensive characterization and stability analysis of APTES- and SDS-modified graphene nanofluids for enhanced thermosiphon performance in geothermal systems
Notice bibliographique
Résumé
The incorporation of graphene nanoparticles surface modified with (3-Aminopropyl) triethoxysilane (APTES) and coated with sodium dodecyl sulfate (SDS) into water has emerged as an effective strategy to enhance the thermal performance of fluids. This study investigates the impact of surface modifications with APTES (surface modification) and SDS (physical coating) on the thermal efficiency and long-term stability of graphene-based water nanofluids. The nanoparticles were synthesized using the co-precipitation method, followed by surface modification. Comprehensive characterization was performed using Energy-Dispersive X-ray Spectroscopy (EDX), Scanning Electron Microscopy (SEM), Fourier-Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD). EDX confirmed the elemental composition, highlighting the successful incorporation of functional elements such as silicon (Si) in APTES and sodium (Na) in SDS. FTIR analysis verified the presence of functional groups like N-H (APTES) and S O (SDS), confirming successful surface modification. XRD analysis indicated reduced crystallinity post- surface modification, while SEM provided insights into surface morphology and nanoparticle dispersion. Nanoparticles with volume concentrations ranging from 0.002 % to 0.012 % significantly improved the thermophysical properties of the nanofluids. Thermal stability and decomposition behavior were evaluated using Thermogravimetric Analysis (TGA), which showed enhanced thermal stability for APTES-surface modified and SDS-coated nanofluids, maintaining structural integrity up to approximately 500°C and 450°C, respectively—results further supported by FTIR analysis. This improvement translates to approximately 30 % and 25 % increases in thermal stability for APTES-surface modified and SDS-coated nanofluids, respectively, compared to water. Stability analysis, including Dynamic Light Scattering (DLS) and viscosity measurements, confirmed that nanofluids maintained dispersion stability for up to 90 days, with APTES-surface modified nanofluids exhibiting superior long-term stability. Finite Element Method (FEM) simulations assessed the effects of nanofluid concentration, pressure drop, and thermosiphon behavior in closed-loop geothermal systems. Results demonstrated that surface modification significantly improved heat transfer efficiency and the thermosiphon effect. The Grashof number increased by 30 % and 25 % for APTES-surface modified and SDS-coated nanofluids, respectively, compared to water, driven by enhanced buoyancy forces and improved thermal conductivity. Pressure drop analysis revealed increments of 25 % and 42 % for APTES-surface modified and SDS-coated nanofluids, respectively. Furthermore, the thermosiphon effect improved by 12 % and 14 % for APTES and SDS, respectively. Higher inlet temperatures and nanoparticle concentrations significantly improved thermal performance, with APTES-surface modified nanofluids exhibiting notably superior heat transfer capabilities. Overall, the study confirms that APTES-surface modified nanofluids offer superior thermal stability, enhanced heat transfer, and reduce pumping power requirements, making them more suitable for high-temperature geothermal applications. SDS-modified nanofluids, while effective, showed comparatively lower stability and thermal performance. This research provides valuable insights into the design and optimization of nanofluids for efficient and sustainable geothermal energy systems. • Graphene nanofluids were surface-modified with APTES and SDS for stability. • CT, TGA, Cryo-TEM, DLS, and FTIR used for novel multi-technique stability analysis. • APTES nanofluids had higher thermal stability and lower pressure drop than SDS. • FEM simulation showed up to 60 % gain in natural convection with APTES. • Optimum at 0.17 % APTES gave highest Nusselt number and thermosiphon ratio.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».