Herein, we present the synthesis of mono-dispersed C-QDs via single-step thermal decomposition process using the fennel seeds (Foeniculum vulgare). As synthesized C-QDs have excellent colloidal, photo-stability, environmental stability (pH) and do not require any additional surface passivation step to improve the fluorescence. The C-QDs show excellent PL activity and excitation-independent emission. Synthesis of excitation-independent C-QDs, to the best of our knowledge, using natural carbon source via pyrolysis process has never been achieved before. The effect of reaction time and temperature on pyrolysis provides insight into the synthesis of C-QDs. We used Machine-learning techniques (ML) such as PCA, MCR-ALS, and NMF-ARD-SO in order to provide a plausible explanation for the origin of the PL mechanism of as-synthesized C-QDs. ML techniques are capable of handling and analyzing the large PL data-set, and institutively recommend the best excitation wavelength for PL analysis. Mono-disperse C-QDs are highly desirable and have a range of potential applications in bio-sensing, cellular imaging, LED, solar cell, supercapacitor, printing, and sensors.

Herein, we present the rapid synthesis of mono-dispersed carbon quantum dots (c-QDs) via a singlestep microwave plasma-enhanced decomposition (MpeD) process. Highly-crystalline c-QDs were synthesized in a matter of 5 min using the fenugreek seeds as a sustainable carbon source. It is the first report, to the best of our knowledge, where c-QDs were synthesized using MpeD via natural carbon precursor. Synthesis of c-QDs requires no external temperature other than hydrogen (H 2) plasma. plasma containing the high-energy electrons and activated hydrogen ions predominantly provide the required energy directly into the reaction volume, thus maximizing the atom economy. c-QDs shows excellent photoluminescence (pL) activity along with the dual-mode of excitation-dependent pL emission (blue and redshift). We investigate the reason behind the dual-mode of excitation-dependent PL. To prove the efficacy of the MPED process, C-QDs were also derived from fenugreek seeds using the traditional synthesis process, highlighting their respective size-distribution, crystallinity, quantum yield, and PL. Notably, C-QDs synthesis via MPED was 97.2% faster than the traditional thermal decomposition process. to the best of our knowledge, the present methodology to synthesize c-QDs via natural source employing MPED is three times faster and far more energy-efficient than reported so far. Additionally, the application of C-QDs to produce the florescent lysozyme protein crystals "hybrid bio-nano crystals" is also discussed. Such a guest-host strategy can be exploited to develop diverse and complex "bio-nano systems". The florescent lysozyme protein crystals could provide a platform for the development of novel next-generation polychrome luminescent crystals. Recently, carbon quantum dots (C-QDs) have gained much attention due to the unique characteristics, notably, alluring fluorescence, chemical-stability, water-solubility, and magnificent photostability properties. C-QDs, owning such properties, have found numerous applications in optoelectronics, bio-imaging, energy-harvesting, and ingenious sensing. Predominantly, C-QDs synthesis is broadly classified into "top-down" and "bottom-up" approaches 1,2. In the top-down approach, C-QDs are synthesized via employing the arc discharge, laser ablation, and chemical oxidation techniques that essentially disintegrate the large graphitic carbon materials into smaller ones 3-5. Alternatively, in the bottom-up approach, C-QDs are synthesized what is known as chemical synthesis such as; the thermal decomposition 6 , hydrothermal 7 , electrochemical oxidation 8 , and microwave pyrolysis 9-12. The majority of the synthesis processes are usually energy consuming, pretty cumbersome, and demand expensive carbon sources that are often toxic 12-16. Concerning C-QDs synthesis, conventional methods such as thermal

Natural product-derived carbon dots (CDs) have been widely studied as environmentally friendly materials for various applications, such as for bioimaging and as sensors, catalysts, and solar cells. Electroluminescence (EL) is one of the most desirable characteristic of CDs for optical devices in display and lighting applications. EL devices with CDs possess a layered structure, in which CDs serve as a middle emission layer sandwiched by transport layers and electrodes. Electrons and holes are injected into the active CD emission layer by an external bias voltage and give rise to EL through radiative recombination. However, the EL of natural product-derived CDs has not been achieved yet owing to issues such as difficulty of mass production and quenching in a dry state. Here, we report for the first time the EL of natural product-derived CDs, which were synthesized from fenugreek seeds via an easy single-step pyrolysis process. The CDs were highly dispersible in solvents and could thus be used as an emissive layer in a light-emitting diode by spin-coating a highly concentrated CD solution between the organic hole and electron transport layers on an indium tin oxide-coated glass substrate. The CDs maintained sufficient dispersion and emissive yields in both the solution (ethanol) and dry states. Furthermore, the LED comprising the CDs exhibited blue−green EL with a spectral peak at 507 nm and maximum luminance of 115.4 cd m −2 . The value of luminance is comparable with that of EL from some CDs synthesized from a chemical carbon source. Our results highlight the great potential of natural product-derived CDs as an environmentally friendly material for LEDs.

Synchronously detecting multiple Raman spectral signatures in two-dimensional/three-dimensional (2D/3D) hyperspectral Raman analysis is a daunting challenge. The underlying reasons notwithstanding the enormous volume of the data and also the complexities involved in the end-to-end Raman analytics pipeline: baseline removal, cosmic noise elimination, and extraction of trusted spectral signatures and abundance maps. Elimination of cosmic noise is the bottleneck in the entire Raman analytics pipeline. Unless this issue is addressed, the realization of autonomous Raman analytics is impractical. Here, we present a learner-predictor strategy-based “automated hyperspectral Raman analysis framework” to rapidly fingerprint the molecular variations in the hyperspectral 2D/3D Raman dataset. We introduce the spectrum angle mapper (SAM) technique to eradicate the cosmic noise from the hyperspectral Raman dataset. The learner-predictor strategy eludes the necessity of human inference, and analytics can be done in autonomous mode. The learner owns the ability to learn; it automatically eliminates the baseline and cosmic noise from the Raman dataset, extracts the predominant spectral signatures, and renders the respective abundance maps. In a nutshell, the learner precisely learned the spectral features space during the hyperspectral Raman analysis. Afterward, the learned spectral features space was translated into a neural network (LNN) model. In the predictor, machine-learned intelligence (LNN) is utilized to predict the alternate batch specimen’s abundance maps in real time. The qualitative/quantitative evaluation of abundance maps implicitly lays the foundation for monitoring the offline/inline industrial qualitative/quantitative quality control (QA/QC) process. The present strategy is best suited for 2D/3D/four-dimensional (4D) hyperspectral Raman spectroscopic techniques. The proposed ML framework is intuitive because it obviates human intelligence, sophisticated computational hardware, and solely a personal computer is enough for the end-to-end pipeline.