7 Computational and Experimental Analysis of Carbon Functional Nanomaterials
303
erties of carbon nanomaterials especially 0D of C-dots and GQDs, 1D of CNTs, and
2D of graphene have extensively employed toward the development of biosensors.
When applying carbon nanomaterials with biomolecules, a significant improvement
can be achieved in characteristics of biosensors such as high selectivity and sensitivity, biocompatibility, non-toxicity, and fast response. The biomolecules including
proteins, DNA, antibody, and enzyme can be easily associated with functionalized
carbon nanomaterials through covalent and/or electrostatic interactions. Due to
the strong specificity of carbon nanomaterials with biomolecules, they provide an
excellent advantage for biosensors by detecting a specific target biomolecules with
higher sensitivity. The other important characteristics of carbon nanomaterials are
low toxicity, good biocompatibility, as well as superior resistance to photobleaching
which make them good candidates for the development of biosensors.
From the viewpoint of fluorescence experiments, the interaction between the
carbon nanomaterials (GQDs and C-dots) and biomolecules occurs via due to
the hydrophobic and/or electrostatic interaction, and the corresponding sensing
mechanism proposed is based on the fluorescence change. However, understanding
the precise role of carbon nanomaterials in interacting with the biomolecules still
need to be discovered. Thus there is a good reason for further development of
fluorescence carbon nanomaterial-based biosensors. As such a future trend is to
better understand the relation between the probe and analyte, and the sensing
mechanism. In addition to experiments, the theoretical simulation studies will
play a major role in future to potentially solve the issues related to fundamental
understanding about the fluorescence of carbon nanomaterials, sensing mechanism,
etc. To achieve this goal, the integrating theoretically the complete structure of
GQDs and C-dots is necessary in accordance with the core moiety, size, shape,
and functional groups, so that it can help to predict the accurate interaction between
the sensing materials and biomolecules from a theoretical perspective. The sensing
performance would be significantly improved by regulating the properties of sensing
materials including size, shape, functional groups, electronic structures, optical
absorption and emission, and redox properties. The information from the theoretical
aspects can be aid to design the new and unique materials to sensing the specific
biomolecules with higher selectivity and sensitivity. We believe that theoretical
simulation and modelling studies of fluorescence-based biosensors will have a
promising future.
References
1. L.A. Curtiss, P.C. Redfern, K. Raghavachari, Assessment of Gaussian-3 and densityfunctional theories on the G3/05 test set of experimental energies. J. Chem. Phys. 123, 124107
(2005)
2. R.K. Raju, A. Ramraj, I.H. Hillier, M.A. Vincent, N.A. Burton, Carbohydrate-aromatic pi
interactions: A test of density functionals and the DFT-D method. Phys. Chem. Chem. Phys.
11, 3411–3416 (2009)
303
erties of carbon nanomaterials especially 0D of C-dots and GQDs, 1D of CNTs, and
2D of graphene have extensively employed toward the development of biosensors.
When applying carbon nanomaterials with biomolecules, a significant improvement
can be achieved in characteristics of biosensors such as high selectivity and sensitivity, biocompatibility, non-toxicity, and fast response. The biomolecules including
proteins, DNA, antibody, and enzyme can be easily associated with functionalized
carbon nanomaterials through covalent and/or electrostatic interactions. Due to
the strong specificity of carbon nanomaterials with biomolecules, they provide an
excellent advantage for biosensors by detecting a specific target biomolecules with
higher sensitivity. The other important characteristics of carbon nanomaterials are
low toxicity, good biocompatibility, as well as superior resistance to photobleaching
which make them good candidates for the development of biosensors.
From the viewpoint of fluorescence experiments, the interaction between the
carbon nanomaterials (GQDs and C-dots) and biomolecules occurs via due to
the hydrophobic and/or electrostatic interaction, and the corresponding sensing
mechanism proposed is based on the fluorescence change. However, understanding
the precise role of carbon nanomaterials in interacting with the biomolecules still
need to be discovered. Thus there is a good reason for further development of
fluorescence carbon nanomaterial-based biosensors. As such a future trend is to
better understand the relation between the probe and analyte, and the sensing
mechanism. In addition to experiments, the theoretical simulation studies will
play a major role in future to potentially solve the issues related to fundamental
understanding about the fluorescence of carbon nanomaterials, sensing mechanism,
etc. To achieve this goal, the integrating theoretically the complete structure of
GQDs and C-dots is necessary in accordance with the core moiety, size, shape,
and functional groups, so that it can help to predict the accurate interaction between
the sensing materials and biomolecules from a theoretical perspective. The sensing
performance would be significantly improved by regulating the properties of sensing
materials including size, shape, functional groups, electronic structures, optical
absorption and emission, and redox properties. The information from the theoretical
aspects can be aid to design the new and unique materials to sensing the specific
biomolecules with higher selectivity and sensitivity. We believe that theoretical
simulation and modelling studies of fluorescence-based biosensors will have a
promising future.
References
1. L.A. Curtiss, P.C. Redfern, K. Raghavachari, Assessment of Gaussian-3 and densityfunctional theories on the G3/05 test set of experimental energies. J. Chem. Phys. 123, 124107
(2005)
2. R.K. Raju, A. Ramraj, I.H. Hillier, M.A. Vincent, N.A. Burton, Carbohydrate-aromatic pi
interactions: A test of density functionals and the DFT-D method. Phys. Chem. Chem. Phys.
11, 3411–3416 (2009)