Development of amine-functionalized fluorescent silica nanoparticles from coal fly ash as a sustainable source for nanofertilizer

Abstract Scaling up the synthesis of fluorescent silica nanoparticles to meet the current demand in diverse applications involves technological limitations. The present study relates to the hydrothermal synthesis of water-soluble, crystalline, blue-emitting amine-functionalized silica nanoparticles from coal fly ash sustainably and economically. This study used tertiary amine (trimethylamine) to prepare amine-functionalized fluorescent silica nanoparticles, enhancing fluorescence quantum yield and nitrogen content for nanofertilizer application. The TEM and FESEM studies show that the silica nanoparticles have a spherical morphology with an average diameter of 4.0 nm. The x-ray photoelectron and Fourier transform infrared spectroscopy studies reveal the presence of the amine group at the surface of silica nanoparticles. The silica nanoparticles exhibit blue fluorescence with an emission maximum of 454 nm at 370 nm excitation and show excitation-dependent emission properties in the aqueous medium. With the perfect spectral overlap between silica nanoparticle emission (donor) and chlorophyll absorption (acceptor), fluorescent silica nanoparticles enhance plant photosynthesis rate by resonance energy transfer. This process accelerates the photosynthesis rate to improve the individual plant’s quality and growth. These findings suggested that the fly ash-derived functionalized silica nanoparticles could be employed as nanofertilizers and novel delivery agents

Parallelizing Quantum-Classical Workloads: Profiling the Impact of Splitting Techniques

Quantum computers are the next evolution of computing hardware. Quantum devices are being exposed through the same familiar cloud platforms used for classical computers, and enabling seamless execution of hybrid applications that combine quantum and classical components. Quantum devices vary in features, e.g., number of qubits, quantum volume, CLOPS, noise profile, queuing delays and resource cost. So, it may be useful to split hybrid workloads with either large quantum circuits or large number of quantum circuits, into smaller units. In this paper, we profile two workload splitting techniques on IBM's Quantum Cloud: (1) Circuit parallelization, to split one large circuit into multiple smaller ones, and (2) Data parallelization to split a large number of circuits run on one hardware to smaller batches of circuits run on different hardware. These can improve the utilization of heterogenous quantum hardware, but involve trade-offs. We evaluate these techniques on two key algorithmic classes: Variational Quantum Eigensolver (VQE) and Quantum Support Vector Machine (QSVM), and measure the impact on circuit execution times, pre- and post-processing overhead, and quality of the result relative to a baseline without parallelization. Results are obtained on real hardware and complemented by simulations. We see that (1) VQE with circuit cutting is ~39\% better in ground state estimation than the uncut version, and (2) QSVM that combines data parallelization with reduced feature set yields upto 3x improvement in quantum workload execution time and reduces quantum resource use by 3x, while providing comparable accuracy. Error mitigation can improve the accuracy by ~7\% and resource foot-print by ~4\% compared to the best case among the considered scenarios.Comment: 10+1 page