There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Herein, a fluorescence turn off-on nanosensor has been successfully developed using functionalized N-doped carbon dots (N-CDs) as the label-free sensing probe for the ultrasensitive detection of Cu2+ ions first and then ciprofloxacin (CIP), one of the most commonly used antibiotics for disease control, in the presence of bipyridine The homogeneous and narrowly distributed N-CDs with a mean size of 57 nm and a high quantum yield of 84% are fabricated via the hydrothermal method in the presence of citric acid and ethylenediamine as the carbon and nitrogen sources, respectively The Cu2+ ions serve as both analyte and fluorescence quenchers in the sensing platform of N-CDs, and a good linear response to Cu2+ in the range of 001-035 μM with a limit of detection (LOD) of 0076 nM is observed Then, 035 μM Cu2+ is used as the fluorescence quencher of N-CDs to build up the fluorescence turn off-on sensing probe for the detection of CIP using bipyridine (bipy) as the linker for CIP and Cu2+ ions The addition of CIP to the bipy-Cu@N-CD composites triggers the formation of CIP-bipy-Cu conjugate as well as the release of N-CDs, resulting in the recovery of fluorescence intensity after 6 min of incubation The sensing probe exhibits a two-phase linear response to CIP in the concentration range of 005-1 and 1-50 μM with a LOD of 04 nM In addition, the bipy-Cu@N-CD probe shows high sensitivity toward CIP over the 19 other interferences Good recovery of 96-110% is also observed when 01-09 μM CIP is spiked into the real samples Results obtained in this study clearly demonstrate a newly developed sensing platform with rapid detection of metal ions and antibiotics, which can open an avenue to develop highly efficient and robust sensing probes for the detection of metal ions, organic metabolites, and biomarkers in biological applications

Bipyridine- and Copper-Functionalized N-doped Carbon Dots for Fluorescence Turn Off–On Detection of Ciprofloxacin

Bee pollen is made by honey bees (Apis Mellifera) from the pollen of plants and flowers and represents an apiary product enriched in essential amino acids, polyphenols, omega-3, and omega-6 fatty acids. This study investigated the botanical origin, micronutrient profile, and antioxidant activity of bee pollen samples (n = 10) harvested in Lucca and Massa Carrara (Tuscany, Italy) between 2016 and 2017. The palynological analysis showed that bee pollen samples were composed of nine botanical families. Front-face fluorescence spectroscopy was performed on bee pollen samples in bulk, without any treatment, and in ethanol extracts to determine the characteristic fluorescent profile and, to identify the main chemical compounds with biological activity. The main chemical compounds detected were polyphenols (mainly flavonoids and phenolic acids), hydro-soluble vitamins (B2, B3, B6, and B9), amino acids, and pigments. Furthermore, the antioxidant activity was investigated, and one of the two Viburnum pollens resulted in the highest polyphenols and flavonoids content (20.15 ± 0.15 mg GAE/g fw and 23.46 ± 0.08 mg CE/g fw, respectively). However, Prunus and Eucalyptus families showed the highest in vitro (190.27 ± 8.30 µmol Fe2+/g) and ex vivo (54.61 ± 8.51 CAA unit) antioxidant capacity, respectively. These results suggested that Tuscan bee pollen, depending on the botanical family, is rich in essential nutrients and potential nutraceutical product.

https://www.mdpi.com/2076-3921/9/10/1001/pdf

Antioxidant, Nutraceutical Properties, and Fluorescence Spectral Profiles of Bee Pollen Samples from Different Botanical Origins.

The physical stability of plant-based drinks and the analysis methods thereof

Honey is a frequent target of adulteration through inappropriate production practices and origin mislabelling. Current methods for the detection of adulterated honey are time and labor consuming, require highly skilled personnel, and lengthy sample preparation. Fluorescence spectroscopy overcomes such drawbacks, as it is fast and noncontact and requires minimal sample preparation. In this paper, the application of fluorescence spectroscopy coupled with statistical tools for the detection of adulterated honey is demonstrated. For this purpose, fluorescence excitation-emission matrices were measured for 99 samples of different types of natural honey and 15 adulterated honey samples (in 3 technical replicas for each sample). Statistical t-test showed that significant differences between fluorescence of natural and adulterated honey samples exist in 5 spectral regions: (1) excitation: 240–265 nm, emission: 370–495 nm; (2) excitation: 280–320 nm, emission: 390–470 nm; (3) excitation: 260–285 nm, emission: 320–370 nm; (4) excitation: 310–360 nm, emission: 370–470 nm; and (5) excitation: 375–435 nm, emission: 440–520 nm, in which majority of fluorescence comes from the aromatic amino acids, phenolic compounds, and fluorescent Maillard reaction products. Principal component analysis confirmed these findings and showed that 90% of variance in fluorescence is accumulated in the first two principal components, which can be used for the discrimination of fake honey samples. The classification of honey from fluorescence data is demonstrated with a linear discriminant analysis (LDA). When subjected to LDA, total fluorescence intensities of selected spectral regions provided classification of honey (natural or adulterated) with 100% accuracy. In addition, it is demonstrated that intensities of honey emissions in each of these spectral regions may serve as criteria for the discrimination between natural and fake honey.

/pdf/detection-of-adulterated-honey-by-fluorescence-excitation-c5p56j9zbi.pdf

Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices

Parallel factor analysis (PARAFAC) of food fluorescence has found many applications in food science, such as in non-contact and non-destructive food characterization, the detection of food adulteration, and the authentication of geographical and botanical origins of food products. This Chapter presents a theoretical background of the PARAFAC method and a step-by-step guide for the practical use of PARAFAC to model fluorescence excitation-emission matrices and interpret the results. For this purpose, several examples of its use in applications of food fluorescence are presented. PARAFAC can decompose complex excitation-emission matrices into emission and excitation spectra of individual components that contribute to the fluorescence of the investigated sample. These components originate from fluorophores; for this reason, Sect. 8.2 of this Chapter is devoted to the description of fluorophores present in food products. Finally, an extensive overview of literature reports on the use of PARAFAC for modeling food fluorescence is provided. Emphasis is given on the measured EEM spectral ranges, the components used for the PARAFAC modeling, and the intended research aim. This Chapter also presents the use of second-order calibration of PARAFAC scores for the quantitative determination of concentrations of fluorophores.

Modeling Food Fluorescence with PARAFAC

Detection of Cu2+ ions in aqueous solution via emission quenching of colloidal EuPO4 ultrasmall nanoparticles

The interaction of partially reduced graphene oxide (prGO) and Huh7.5.1 liver cancer cells was investigated by means of DUV fluorescence bioimaging. The prGO sample was obtained by the reduction (to a certain extent) of the initially prepared graphene oxide (GO) nanosheets with hydrazine. The fluorescence of the GO nanosheets increases with time of the reduction due to a change in ratio of the sp2 and sp3 carbon sites and the prGO sample was extracted from the dispersion after 6 min, when the intensity of the fluorescence reached its maximum. The reduction process was left to proceed further to saturation until highly reduced graphene oxide (denoted here as rGO) was obtained. GO, prGO and rGO samples were investigated by structural (scanning electron microscopy (SEM), scanning transmission electron microscopy coupled with energy dispersive spectrometry (STEM-EDS)) and spectroscopic (UV–vis, photoluminescence (PL), Raman) methods. After that, Huh7.5.1 cells were incubated with GO, prGO and rGO nanosheets and used in bioimaging studies, which were performed on DISCO beamline of synchrotron SOLEIL. It was found that the prGO significantly enhanced the fluorescence of the cells and increased the intensity of the signal by ~2.5 times. Time-lapse fluorescence microscopy experiments showed that fluorescence dynamics strongly depends on the type of nanosheets used. The obtained prGO nanostructure can be easily conjugated with aromatic ring containing drugs, which opens a possibility for its applications in fluorescence microscopy monitored drug delivery.

/pdf/duv-fluorescence-bioimaging-study-of-the-interaction-of-22i8smmnfl.pdf

DUV fluorescence bioimaging study of the interaction of partially reduced graphene oxide and liver cancer cells

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Lea Lenhardt Acković

Author Tools

Chat about Author

Papers

Detection of Adulterated Honey by Fluorescence Excitation-Emission Matrices

Modeling Food Fluorescence with PARAFAC

Detection of Cu2+ ions in aqueous solution via emission quenching of colloidal EuPO4 ultrasmall nanoparticles

DUV fluorescence bioimaging study of the interaction of partially reduced graphene oxide and liver cancer cells