## 2 Hybrid Graphene-Based Materials (Hybrid GBMs)
### Silica Derivatives  
The use of graphene derivatives in microextraction techniques is mainly due to their advantages, such as their high surface area, which allow these materials to be successfully employed in dispersive techniques. However, the irregular morphology of GBM particles is a challenge in terms of their use in techniques involving the packing of sorbents, such as automated in-tube solid-phase microextraction (IT-SPME) or online SPE, as well as offline techniques such as MEPS and PT-SPE, due to issues with high system pressure, device clogging, or particle aggregation. Functionalizing the oxygenated groups of GO or rGO with ionic liquids (ILs), deep eutectic solvents (DESs), carbon-based biosorbents, or magnetic particles can help resolve particle aggregation challenges, especially in dispersive techniques. However, in packed techniques, grafting GBMs onto the surface of the support material with precise and well-defined particles ensures better method performance, avoiding backpressure problems, and even improving the sorbent's extraction capacity [46; 47]. As a result, silica-derived materials, such as aminopropyl silica particles, are widely used to modify GBMs [8]. [PERSON] et al. [28] synthesized a sorbent based on graphene oxide supported on aminopropyl silica (GO@SiO\({}_{2}\)). They used it in a miniaturized extraction column within a column-switching method coupled with LC-MS/MS to extract and determine some \(\beta\)-lactam antibiotics from wastewater samples. The coupling between the aminopropyl silica and the graphene oxide occurred through covalent bonds between the carboxyl groups of the GO and the amino groups of the silica, using a water-based synthetic route, which is consequently more sustainable. The results showed that the method achieved recoveries above 60% for the analytes, demonstrating high performance due to using the prepared sorbent, which showed good stability and reusability for up to 100 cycles without significant losses in the extraction process. Previously, the same research group used GO@SiO\({}_{2}\) to extract antidepressants and antiepiletips from urine samples in a capillary column in the first dimension of a multidimensional liquid chromatography system. In this study, low detection limits (LOD) for the analytes (0.5-20 \(\upmu\)g L\({}^{-1}\)) were obtained, and the samples were analyzed without any prior treatment, such as precipitation and/or dilution. Additionally, the extraction column containing the modified graphene phase could be used more than 250 times, attesting to the robustness and stability of the sorbent [29]. To enhance the selectivity of graphene particles supported on silica for a variety of compounds, in addition to the interactions promoted by the structure of GO and rGO (such as \(\pi\)-\(\pi\) and n-\(\pi\) interactions), hydrophobic interactions are further encouraged by functionalizing the graphene supported on aminopropyl silica with octadecylsilane (C18), thereby forming hybrid particles known as SiO-C18 [47; 48]. In this context, [PERSON] et al. [30] employed this sorbent in an automated in-tube SPME method coupled with LC-MS/MS to quantify ten multi-class pesticides in Brazilian sugarcane spirits called \"cachacas\". As a result, the authors demonstrated excellent analytical performance of the method, with accuracy percentages exceeding 80%, attributed to the interactions of the sorbent even with analytes of different physicochemical characteristics. More recently, [PERSON] et al. [31] reported a sorbent with similar properties, consisting of GO anchored to silica through amino-terminated groups and doped with C18 applied in PT-SPE for the pre-concentration of aflat in food, and analysis by liquid chromatography with fluorescence detector (LC-FLD) analysis. As the main advantage, the authors noted the appropriate polarity of the sorbent for the efficient enrichment of analytes compared to matrix compounds, achieving recovery rates greater than 70% for the four compounds studied and high precision between extractions.