ABSTRACT
This study theoretically demonstrated an insight for designing a novel tunable plasmonic biosensor, which was created by simply stacking a twisted bilayer graphene (TBG) superlattice onto a plasmonic gold thin film. To achieve ultrasensitive biosensing, the plasmonic biosensor was modulated by Goos-Hänchen (GH) shift. Interestingly, our proposed biosensor exhibited tunable biosensing ability, largely depending on the twisted angle. When the relative twisted angle was optimized to be 55.3°, such a configuration: 44 nm Au film/1-TBG superlattice could produce an ultralow reflectivity of 2.2038 × 10-9and ultra-large GH shift of 4.4785 × 104μm. For a small refractive index (RI) increment of 0.0012 RIU (refractive index unit) in sensing interface, the optimal configuration could offer an ultra-high GH shift detection sensitivity of 3.9570 × 107μm/RIU. More importantly, the optimal plasmonic configuration demonstrated a theoretical possibility of quantitatively monitoring severe acute respiratory syndrome coronavirus (SARS-CoV-2) and human hemoglobin. Considering an extremely small RI change as little as 3 × 10-7RIU, a good linear response between detection concentration of SARS-CoV-2 and changes in differential GH shift was studied. For SARS-CoV-2, a linear detection interval was obtained from 0 to 2 nM. For human hemoglobin, a linear detection range was achieved from 0 to 0.002 g/L. Our work will be important to develop novel TBG-enhanced biosensors for quantitatively detecting microorganisms and biomolecules in biomedical application. © 2023 the author(s), published by De Gruyter, Berlin/Boston 2023.
ABSTRACT
In this study, we report a one-pot, green, cost-efficient, and fast synthesis of plant-based sulfur and nitrogen self-co-doped carbon quantum dots (S,N-CQDs). By 4-min microwave treatment of onion and cabbage juices as renewable, cheap, and green carbon sources and self-passivation agents, blue emissive S,N-CQDs have been synthesized (λex/λem of 340/418 nm) with a fluorescence quantum yield of 15.2%. A full characterization of the natural biomass-derived quantum dots proved the self-doping with nitrogen and sulfur. The S,N-CQDs showed high efficiency as a fluorescence probe for sensitive determination of nitazoxanide (NTZ), that recently found wide applicability as a repurposed drug for COVID-19, over the concentration range of 0.25–50.0 μM with LOD of 0.07 μM. The nanoprobe has been successfully applied for NTZ determination in pharmaceutical samples with excellent % recovery of 98.14 ± 0.42. Furthermore, the S,N-CQDs proved excellent performance as a sensitive fluorescence nanoprobe for determination of hemoglobin (Hb) over the concentration range of 36.3–907.5 nM with a minimum detectability of 10.30 nM. The probe has been applied for the determination of Hb in blood samples showing excellent agreement with the results documented by a medical laboratory. The greenness of the developed probe has been positively investigated by different greenness metrics and software. The green character of the proposed analytical methods originates from the synthesis of S,N-CQDs from sustainable, widely available, and cheap plants via low energy/low cost microwave-assisted technique. Omission of organic solvents and harsh chemicals beside dependence on mix-and-read analytical approach corroborate the method greenness. The obtained results demonstrated the substantial potential of the synthesized green, safe, cheap, and sustainable S,N-CQDs for pharmaceutical and biological applications. © 2022 Elsevier B.V.
ABSTRACT
In many cases, especially at the beginning of epidemic disaster, it is very important to be able to determine the severity of illness of a given patient. Picking up the severe status will help in directing the effort in a proper way. At the beginning, the number of classified status and the available data are limited, so, in such situation, one needs a system that can be trained based on limited data to give a trusted result. The current work focuses on the importance of the bioscience in differentiation between recovered patients and mortalities. Even with limited data, the decision trees (DT) was able to distinguish between recovered patients and mortalities with accuracy of 94%. Shallow dense network achieved accuracy of 75%. However, when a 10-fold technique was followed with the same data, the net achieved 99% of accuracy. The used data in this work was collected from King Faisal hospital in Taif city under a formal permission from the health ministry. PCA analysis confirmed that there are two parameters that have the greatest ability to differentiate between recovered patients and mortalities. ROC curve reveals that the parameters that can differentiate between recovered patients and mortalities are calcium and hemoglobin. The shallow net gives an accuracy of 92% when trained using calcium and hemoglobin only. This paper shows that with a suitable choosing of the parameters a small decision tree or shallow net can be trained quickly to decide which patient needs more attention so as to use the hospitals resources in a more reasonable way during the pandemic. All codes and data can be accessed from the following link “codes and data” © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.