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

The intelligence to synchronously identify multiple spectral signatures in a lithium-ion battery electrode (LIB) would facilitate the usage of analytical technique for inline quality control and product development. Here, we present an analytical framework (AF) to automatically identify the existing spectral signatures in the hyperspectral Raman dataset of LIB electrodes. The AF is entirely automated and requires fewer or almost no human assistance. The end-to-end pipeline of AF own the following features; (i) intelligently pre-processing the hyperspectral Raman dataset to eliminate the cosmic noise and baseline, (ii) extract all the reliable spectral signatures from the hyperspectral dataset and assign the class labels, (iii) training a neural network (NN) on to the precisely “labelled” spectral signature, and finally, examined the interoperability/reusability of already trained NN on to the newly measured dataset taken from the same LIB specimen or completely different LIB specimen for inline real-time analytics. Furthermore, we demonstrate that it is possible to quantitatively assess the capacity degradation of LIB via a capacity retention coefficient that can be calculated by comparing the LMO signatures extracted by the analytical framework (AF). The present approach is suited for real-time vibrational spectroscopy based industrial applications; multicomponent chemical reactions, chromatographic, spectroscopic mixtures, and environmental monitoring.

It still remains a crucial challenge to actively control carbon nanotube (CNT) structure such as the alignment, area density, diameter, length, chirality, and number of walls. Here, we synthesize an ultradense forest of CNTs of a uniform internal diameter by the plasma-enhanced chemical vapor deposition (PECVD) method using hollow nanoparticles (HNPs) modified with ligand as a catalyst. The diameters of the HNPs and internal cavities in the HNPs are uniform. A monolayer of densely packed HNPs is self-assembled on a silicon substrate by spin coating. HNPs shrink via the collapse of the internal cavities and phase transition from iron oxide to metallic iron in hydrogen plasma during the PECVD process. Agglomeration of catalytic NPs is avoided on account of the shrinkage of the NPs and ligand attached to the NPs. Diffusion of NPs into the substrate, which would inactivate the growth of CNTs, is also avoided on account of the ligand. As a result, an ultradense forest of triple-walled CNTs of a uniform internal diameter is successfully synthesized. The area density of the grown CNTs is as high as 0.6 × 10(12) cm(-2). Finally, the activity of the catalytic NPs and the NP/carbon interactions during the growth process of CNTs are investigated and discussed. We believe that the present approach may make a great contribution to the development of an innovative synthetic method for CNTs with selective properties.

No abstract

No abstract