Characterization of food waste-driven carbon dot focusing on chemical structural, electron relaxation behavior and Fe3+ selective sensing

In the study, carbon dot (CD) with high fluorescence properties was obtained via one-step hydrothermal carbonization of food model and sandwich leftover, respectively. The data in the article represent the change of the chemical structure and PL properties of the food waste-driven carbon dot (FWCDs). In higher carbonization temperature, pyridinic N and graphitic N were increased while amino N and pyrrolic N was decreased. The lifetime was increased with the increase of temperature. The CD prepared from sandwich leftover showed the dependency of the emission on the exciting wavelength and excellent Fe3+ sensitivity without significant change of lifetime. It also had a pH-sensitive fluorescence feature and good stability in NaCl solutions. For more insight, please see Food waste-driven N-doped carbon dots: Applications for Fe3+ sensing and cell imaging Ahn et al., 2019.


Data
Nanomaterials with fluorescence properties including carbon dots are having great attention due to its wide application area such as metal ion sensing and biological imaging [1e3]. Herein, we synthesized carbon dots with food waste-driven cat feed stock and sandwich leftover. Fig. 1 shows the chemical structure changes of FWCDs along the carbonization temperature. As the temperature increased, the peak for eOH bonding was decreased while the peaks for CeO and C¼O bonding were increased (Fig. 1a). Fig. 1b shows the ratio of each nitrogen speciation in the synthesized CDs, which indicate the structure of N-containing aromatic compounds were more developed along the temperature. The TEM images of synthesized CDs their size distribution can be seen in Fig. 1 of [1].
The Fig. 2 shows the lifetime decay curves of the CDs and were interpreted in terms of a triexponential function: Where A i and t i are the amplitude and the decay times of lifetimes, respectively. All the curves were well-fitted with c 2 value below 1.1.
Radiative lifetime was can be calculated from the average lifetime (t av ) and the fluorescence quantum yield (4) using Equation (2). The lifetime and the radiative/non-radiative recombination rate are finally obtained from the radiative and non-radiative lifetimes using Equations (3) and (4) (

Value of the data
This data can help for the understanding of structural change of CD during hydrothermal carbonization of complex mixture including food waste.
The changes of the chemical structure and the electron relaxation behavior along the carbonization temperature is beneficial to study the photoluminescence mechanism of carbon dots. The data exhibit the possibility of the prepared carbon dot for Fe 3þ sensing with high selectivity in the presence of other metal ion.  where 4 ¼ fluorescence quantum yield, t av ¼ average lifetime, t r ¼ radiative lifetime, t nr ¼ nonradiative lifetime, k r ¼ radiative recombination rate constant and k nr ¼ non-radiative recombination rate constant. The functional groups of waste model and CDs were summarized in Table 1. Fig. 3 shows the image of used sandwich leftover and PL spectra of synthesized FWCDs. The TEM images of FWCDs can be seen in Fig. 5a of [1].
The FWCDs showed the selective sensing capability for Fe 3þ . In Fig. 4a, the fluorescence intensity of FWCDs solutions was significantly decreased in the presence of Fe 3þ while other metal ions insignificantly influenced on the PL. The quenching mechanism of FWCDs was characterized by Timecorrelated single photon counting (TCSPC), UV-vis spectrometer and PL spectrometer. Fig. 4b shows the fluorescence decays of the FWCDs quenched by Fe 3þ . The obtained values were summarized in Table 2. The average lifetime of FWCDs was slightly increased in Fe 3þ solutions, However, the lifetime decay of FWCDs in Fe 3þ solution was not changed along Fe 3þ concentration. The identical lifetime indicates that the energy transfer between Fe 3þ and FWCDs did not occur in the quenching process, known as Inner Filter Effect (IFE) [4]. In Fig. 4c, the typical feature of IFE behavior was shown by the overlapping curves of the absorption band of Fe 3þ in UV spectra and emission or excitation bands of FWCDs in PL spectra. Table 3 are the list of comparing the detection of Fe 3þ with carbon dots prepared from various biomass-based sources. Fig. 5 exhibits the FWCDs had a pH-sensitive fluorescence feature and good stability in NaCl solutions.      Table 3 Comparison of limit of detection (LOD) and linear detection range for Fe 3þ of carbon dots prepared from various biomass-based sources.

Synthesis of carbon dots
Cat feed stocks (Catsrang, Dajoo industry) produced from the organic waste were used for the synthesis of CDs along the temperature. The feed stocks were ground to fine powder and dried at 65 C for 24 h. After the powder was mixed with 50ml distilled water for 7 wt%, hydrothermal carbonization of the source was conducted at 110, 150 and 180 C for 24 h. The obtained solution was purified by filtering (0.5 mm PTFE membrane) and dialysis (Biotech CD dialysis tubing, 0.5e1.0 kDa, Spectrum Labs.). The carbon dots obtained at 110, 150 and 180 C were noted as CD110, CD150 and CD180, respectively. Hamburger sandwich leftover was synthesized at 180 C to food waste-driven carbon dots.

Selectivity test for Fe 3þ
The all metal salts in this experiment were purchased from Sigma Aldrich. And they was used as received.
For metal quenching test, the all metal salts were dissolved at concentration of 500 mM and mixed with FWCDs solution (10 mg/ml). The PL intensities were measured after 30 min by excitation wavelength of 340 nm.

Characterization
X-ray photoelectron spectroscopy (XPS, K-alpha, Thermo Scientific) were carried out to characterize the chemical structure of the samples. Fluorescence lifetimes were obtained by the time-correlated single photon counting method (TCSPC, Fluo Time 200 instrument, Picoquant). An excitation source was used 342 nm pulsed LED with repetition rate of 5 MHz. The decay profiles were analyzed by FluoFit Pro software using exponential fitting models through deconvolution with instrumental response functions (IRF).