A casting strategy to produce 3D bulk monolithic carbon and N-doped carbon nanosheets with high surface area and low volume

Ghotbi, Mohammad Yeganeh; Javanmard, Arash; Soleimani, Hassan

doi:10.1016/j.micromeso.2019.109791

Cited by 2 publications

(1 citation statement)

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“…Besides, the continuous small peaks ranging of 1400–1600 cm –1 of prepared nanosilicons could be ascribed to the aromatic ring. , Moreover, X-ray photoelectron spectroscopy was used to further study chemical bonds of the as-synthesized nanosilicons. As illustrated in Figure D, peaks at ∼284.37, ∼285.29, and ∼288.72 eV of TPE- COOH -SiO 2 NPs could be assigned to CC bond, CC bond, and O–CO group of TPE- COOH , respectively. − Besides, peaks at ∼284.58 and ∼286.26 eV of TPE- NH 2 -SiO 2 NPs could be attributed to CC bond and C–N bond of TPE- NH 2 , respectively. − Moreover, peaks at ∼284.18, ∼284.99, ∼286.07, and ∼291.87 eV of TPE- CHO -SiO 2 NPs were because of CC bond, CC bond, CO bond, and C–C of TPE- CHO , respectively. − Meanwhile, peaks of ∼284.42 and ∼285.47 eV of TPE-SiO 2 NPs could be attributed to C–C/CC bond and CC bond of TPE, respectively. , Additionally, the peak of ∼400.36 eV of TPE- NH 2 -SiO 2 NPs in N 1s spectra could be assigned to – NH 2 of TEP- NH 2 (Figure E) . Subsequently, fluorescent behaviors of nanosilicons were investigated by fluorescence lifetime and fluorescence spectrum.…”

Section: Resultsmentioning

confidence: 97%

Machine-Learning-Assisted Aggregation-Induced Emissive Nanosilicon-Based Sensor Array for Point-of-Care Identification of Multiple Foodborne Pathogens

Li,

Cui,

Wang

et al. 2024

Anal. Chem.

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How timely identification and determination of pathogen species in pathogen-contaminated foods are responsible for rapid and accurate treatments for food safety accidents. Herein, we synthesize four aggregation-induced emissive nanosilicons with different surface potentials and hydrophobicities by encapsulating four tetraphenylethylene derivatives differing in functional groups. The prepared nanosilicons are utilized as receptors to develop a nanosensor array according to their distinctive interactions with pathogens for the rapid and simultaneous discrimination of pathogens. By coupling with machine-learning algorithms, the proposed nanosensor array achieves high performance in identifying eight pathogens within 1 h with high overall accuracy (93.75–100%). Meanwhile, Cronobacter sakazakii and Listeria monocytogenes are taken as model bacteria for the quantitative evaluation of the developed nanosensor array, which can successfully distinguish the concentration of C. sakazakii and L. monocytogenes at more than 103 and 102 CFU mL–1, respectively, and their mixed samples at 105 CFU mL–1 through the artificial neural network. Moreover, eight pathogens at 1 × 104 CFU mL–1 in milk can be successfully identified by the developed nanosensor array, indicating its feasibility in monitoring food hazards.

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