TEMPO Driven Mild and Modular Route to Functionalized Microparticles

The synthesis of crosslinked polymeric microspheres (3.8–15.0 µm) via (2,2,6,6‐tetramethylpiperidin‐1‐yl)oxyl (TEMPO) initiated thiol‐ene dispersion polymerization under ambient conditions is reported for the first time. The initiating ability of TEMPO for the thiol‐ene reaction is validated by electron paramagnetic resonance (EPR) and 1H nuclear magnetic resonance (NMR) spectroscopy on model reactions between 1‐octadecanethiol and two electron deficient enes, n‐butylacrylate and divinyl sulfone. Critically, the TEMPO resonance observed in the EPR spectra decreases with time when TEMPO is mixed with thiol and an electron deficient ene. The 1H NMR spectra demonstrate formation of up to 90% of thioether under ambient conditions. Based on these model reactions, a variety of crosslinked polymeric microspheres are synthesized with excellent morphological stability using poly(vinyl pyrrolidone) as surfactant. The ability of the microspheres for a second TEMPO initiated thiol‐ene reaction is demonstrated by the ligation of fluorescein‐5‐maleimide (an ene) to the microspheres' surface containing excess of thiol functionality and by ligation of cysteine (containing a thiol group) to the microspheres' surface containing an excess of ene functionality. The synthesized polymeric microspheres are characterized using scanning electron microscopy, differential scanning calorimetry, Fourier‐transform infrared spectroscopy, zeta potential, and X‐ray photoelectron spectroscopy

TEMPO driven mild and modular route to functionalized microparticles

The synthesis of crosslinked polymeric microspheres (3.8–15.0 µm) via (2,2,6,6‐tetramethylpiperidin‐1‐yl)oxyl (TEMPO) initiated thiol‐ene dispersion polymerization under ambient conditions is reported for the first time. The initiating ability of TEMPO for the thiol‐ene reaction is validated by electron paramagnetic resonance (EPR) and 1H nuclear magnetic resonance (NMR) spectroscopy on model reactions between 1‐octadecanethiol and two electron deficient enes, n‐butylacrylate and divinyl sulfone. Critically, the TEMPO resonance observed in the EPR spectra decreases with time when TEMPO is mixed with thiol and an electron deficient ene. The 1H NMR spectra demonstrate formation of up to 90% of thioether under ambient conditions. Based on these model reactions, a variety of crosslinked polymeric microspheres are synthesized with excellent morphological stability using poly(vinyl pyrrolidone) as surfactant. The ability of the microspheres for a second TEMPO initiated thiol‐ene reaction is demonstrated by the ligation of fluorescein‐5‐maleimide (an ene) to the microspheres' surface containing excess of thiol functionality and by ligation of cysteine (containing a thiol group) to the microspheres' surface containing an excess of ene functionality. The synthesized polymeric microspheres are characterized using scanning electron microscopy, differential scanning calorimetry, Fourier‐transform infrared spectroscopy, zeta potential, and X‐ray photoelectron spectroscopy

Cu-Zn coupled heterojunction photocatalyst for dye degradation: Performance evaluation based on the quantum yield and figure of merit

This study reports the production of a heterojunction copper oxide-zinc oxide nano-photocatalyst based on lemon leaf extract, which acted as both a stabilizer and a capping and reducing agent. The fabricated photocatalyst had a smooth surface with numerous functional groups, and its energy band gap was measured to be 3.14 eV, which is suitable for photocatalytic applications. Toxic Congo Red dye was used as a model pollutant to investigate the photocatalytic degradation performance of the proposed CuO-ZnO nano-photocatalyst. The photocatalyst exhibited a rapid degradation rate under sunlight irradiation, reducing the concentration of the dye by almost 70 % within 50 min and exhibited pseudo-first-order kinetics. The performance of the photocatalyst was also evaluated based on its quantum yield and figure of merit, which were found to be 6.63 x 10−9 molecules photon−1and 3.31 x 10−4, respectively. In addition, the proposed catalyst displayed good stability for up to 5 cycles. Based on these results, the fabricated CuO-ZnO nano-photocatalyst outperformed previously reported photocatalysts. The mechanisms associated with the dye degradation were also explained by an interfacial charge transfer reaction

A framework for maternal physical activities and health monitoring using wearable sensors

We propose a physical activity recognition and monitoring framework based on wearable sensors during maternity. A physical activity can either create or prevent health issues during a given stage of pregnancy depending on its intensity. Thus, it becomes very important to provide continuous feedback by recognizing a physical activity and its intensity. However, such continuous monitoring is very challenging during the whole period of maternity. In addition, maintaining a record of each physical activity, and the time for which it was performed, is also a non-trivial task. We aim at such problems by first recognizing a physical activity via the data of wearable sensors that are put on various parts of body. We avoid the use of smartphones for such task due to the inconvenience caused by wearing it for activities such as “eating”. In our proposed framework, a module worn on body consists of three sensors: a 3-axis accelerometer, 3-axis gyroscope, and temperature sensor. The time-series data from these sensors are sent to a Raspberry-PI via Bluetooth Low Energy (BLE). Various statistical measures (features) of this data are then calculated and represented in features vectors. These feature vectors are then used to train a supervised machine learning algorithm called classifier for the recognition of physical activity from the sensors data. Based on such recognition, the proposed framework sends a message to the care-taker in case of unfavorable situation. We evaluated a number of well-known classifiers on various features developed from overlapped and non-overlapped window size of time-series data. Our novel dataset consists of 10 physical activities performed by 61 subjects at various stages of maternity. On the current dataset, we achieve the highest recognition rate of 89% which is encouraging for a monitoring and feedback system

Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed

Yield is one of the most important agronomic traits for the breeding of rapeseed (Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits

Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs