Influence of 0.5wt%Graphene Addition on Mechanical Performance of Alumina-Graphene Nanocomposite
Bibliographic record
Abstract
Technical ceramics such as alumina (Al2O3), boron nitride and silicon nitride have enormous applications in various engineering fields. The appealing properties of ceramics such as alumina including low density, high hardness, high oxidation resistance, high thermal and electrical resistance, and good chemical stability makes alumina vibrant in current applications including biomedical implants, cutting tools, aerospace, insulators in electronic [1]. Despite the attractive properties of structural alumina, its brittleness limits its potential for high performance applications. The strong directional bonding of atoms and limited plasticity (e.g., dislocation mobility) under loading conditions lead to the lower fracture toughness by minimizing the local relief of high stress [2]. With the aim to improve mechanical properties without compromising their lightweight attributes, similar low density but tougher second-phase nano-scale materials such as graphene and carbon nanotubes have been incorporated within the matrix structure. 2D graphene as a nanofiller possesses superior mechanical properties such as higher strength and stiffness, which has the potential to improve the physical and mechanical properties of ceramics [3]. Several literatures have reported that the enhancement of mechanical properties of alumina and other ceramic-based composites with relatively smaller amounts of graphene (up to 1.0wt%) [4]. Despite the enormous improvement of mechanical properties in alumina-graphene composites, other reports have suggested that the inclusion of graphene diminishes the mechanical properties such as hardness, fracture toughness and wear properties [5]. For instance, Porwal et al reported a decrease in the fracture toughness value of alumina-graphene nanocomposites with higher content of graphene (2–5 vol%) as compared to the lower amounts (order of 0.2–0.8vol%). In their work, the elastic modulus also decreased by about 15% with the addition of 5vol% graphene due to the high network of interconnected graphene nanofillers which also resulted in large defects [6]. Therefore, there still exists a need to further investigate the influence of graphene additions in ceramic alumina structures. Thus, in this study, an attempt has been made to produce alumina nanocomposite reinforced with a lower concentration (0.5wt%) of nanoscale graphene using colloidal mixing followed by hot-pressing process at 1600oC under applied pressure of 60MPa. The effect of graphene on matrix, as well as the relationship between grain structure and mechanical properties including micro-hardness, bending strength, fracture toughness (KIC), young modulus (E) and critical energy release rate (GIC) were studied. Fractured surfaces of both monolithic alumina and alumina-0.5wt%graphene were analyzed using the high-resolution field-emission scanning electron microscope (FE-SEM, Quanta-3D). The toughening mechanisms of alumina and alumina-0.5wt%graphene and the mechanism of fracture were also studied. Table 1 shows the variation of the grain sizes of the fabricated nanocomposites. Figure 1(a) also reveals the grain distribution histogram which demonstrates finer grain structure with the addition of 0.5wt%graphene as compared to the typical coarse grain matrix depicted by the monolithic Al2O3. This is due to the limited diffusion of the matrix by the presence of the graphene layer at Al2O3 boundary during sintering. Figure 1(b) also illustrates the nanomechanical properties of the nanocomposites with regards to the grains and the grain boundary distribution using the atomic force microscopy (AFM). The modulus map showed regions of higher stiffness within the grains, whereas the grain boundaries exhibited relatively lower stiffness. An average value of the elastic modulus from the map was recorded as ∼390GPa per the modulus map color scale, which also showed an increase in elastic modulus relative to the monolithic alumina. Table 1 also illustrates the mechanical properties of the fabricated samples. Figure 2a shows the graph of the calculated fracture toughness of the Al2O3 and Al2O3/GN samples from indentation and single edge notched beam (SENB) tests. About 111% and 163% increase in the fracture toughness value resulted from both the SENB and indentation fracture toughness (IFT) tests respectively. This was mainly due to the refined grain structure and various toughening mechanisms demonstrated by the uniformly distributed graphene. The bending strength and estimated critical energy were also superior for the Al2O3/GN sample as compared to the parent Al2O3 (Table 1). This is ascribed to the high energy accumulation and load transfer capabilities of the reinforced graphene structures during the bending tests. Fractured surfaces of Al2O3 and Al2O3/GN further demonstrate the grain structure and various modes of fracture during the bending tests. Typical fracture modes such as intergranular and transgranular fractures are illustrated by the micrographs in Figure 2b and 2c. The Al2O3/GN showed both intergranular and transgranular fracture modes. The transgranular fracture occurred at sites with wrapped tough graphene material at the grain boundaries which prevents crack propagation through the boundary. Crack is then propagated through the matrix grains to cause fracture. Intergranular fracture is also shown by the removal of matrix grains as cracks move along the boundaries during bending. Mechanical properties of alumina and alumina-0.5wt%graphene nanocomposites (a) Grain size distribution of Al2O3/GN nanocomposite (b) Atomic Force Microscopy (AFM) of Al2O3/GN nanocomposite showing topographical (height) and elastic modulus maps. (a)Influence of graphene on fracture toughness (KIC) (b) Fractured surface of monolithic alumina depicting intergranular fracture mode (c) intergranular and transgranular fracture of Al2O3/GN
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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 | 0.002 |
| 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".