Comprehensive characterization and stability analysis of APTES- and SDS-modified graphene nanofluids for enhanced thermosiphon performance in geothermal systems
Bibliographic record
Abstract
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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".