Carbon nanotubes in TiO2 nanofiber photoelectrodes for high-performance perovskite solar cells

1D semiconducting oxides are unique structures that have been widely used for photovoltaic (PV) devices due to their capability to provide a direct pathway for charge transport. In addition, carbon nanotubes (CNTs) have played multifunctional roles in a range of PV cells because of their fascinating properties. Herein, the influence of CNTs on the PV performance of 1D titanium dioxide nanofiber (TiO2 NF) photoelectrode perovskite solar cells (PSCs) is systematically explored. Among the different types of CNTs, single‐walled CNTs (SWCNTs) incorporated in the TiO2 NF photoelectrode PSCs show a significant enhancement (≈40%) in the power conversion efficiency (PCE) as compared to control cells. SWCNTs incorporated in TiO2 NFs provide a fast electron transfer within the photoelectrode, resulting in an increase in the short‐circuit current (J sc) value. On the basis of our theoretical calculations, the improved open‐circuit voltage (V oc) of the cells can be attributed to a shift in energy level of the photoelectrodes after the introduction of SWCNTs. Furthermore, it is found that the incorporation of SWCNTs into TiO2 NFs reduces the hysteresis effect and improves the stability of the PSC devices. In this study, the best performing PSC device constructed with SWCNT structures achieves a PCE of 14.03%

Surface-Enhanced Raman Spectroscopy Using a Silver Nanostar Substrate for Neonicotinoid Pesticides Detection.

Surface-enhanced Raman spectroscopy (SERS) has been introduced to detect pesticides at low concentrations and in complex matrices to help developing countries monitor pesticides to keep their concentrations at safe levels in food and the environment. SERS is a surface-sensitive technique that enhances the Raman signal of molecules absorbed on metal nanostructure surfaces and provides vibrational information for sample identification and quantitation. In this work, we report the use of silver nanostars (AgNs) as SERS-active elements to detect four neonicotinoid pesticides (thiacloprid, imidacloprid, thiamethoxam and nitenpyram). The SERS substrates were prepared with multiple depositions of the nanostars using a self-assembly approach to give a dense coverage of the AgNs on a glass surface, which ultimately increased the availability of the spikes needed for SERS activity. The SERS substrates developed in this work show very high sensitivity and excellent reproducibility. Our research opens an avenue for the development of portable, field-based pesticide sensors, which will be critical for the effective monitoring of these important but potentially dangerous chemicals

Energy Interplay in Materials: Unlocking Next-Generation Synchronous Multisource Energy Conversion with Layered 2D Crystals.

Layered 2D crystals have unique properties and rich chemical and electronic diversity, with over 6000 2D crystals known and, in principle, millions of different stacked hybrid 2D crystals accessible. This diversity provides unique combinations of properties that can profoundly affect the future of energy conversion and harvesting devices. Notably, this includes catalysts, photovoltaics, superconductors, solar-fuel generators, and piezoelectric devices that will receive broad commercial uptake in the near future. However, the unique properties of layered 2D crystals are not limited to individual applications and they can achieve exceptional performance in multiple energy conversion applications synchronously. This synchronous multisource energy conversion (SMEC) has yet to be fully realized but offers a real game-changer in how devices will be produced and utilized in the future. This perspective highlights the energy interplay in materials and its impact on energy conversion, how SMEC devices can be realized, particularly through layered 2D crystals, and provides a vision of the future of effective environmental energy harvesting devices with layered 2D crystals

Domestication to Crop Improvement: Genetic Resources for Sorghum and Saccharum (Andropogoneae)

Background Both sorghum (Sorghum bicolor) and sugarcane (Saccharum officinarum) are members of the Andropogoneae tribe in the Poaceae and are each other's closest relatives amongst cultivated plants. Both are relatively recent domesticates and comparatively little of the genetic potential of these taxa and their wild relatives has been captured by breeding programmes to date. This review assesses the genetic gains made by plant breeders since domestication and the progress in the characterization of genetic resources and their utilization in crop improvement for these two related species. Genetic Resources The genome of sorghum has recently been sequenced providing a great boost to our knowledge of the evolution of grass genomes and the wealth of diversity within S. bicolor taxa. Molecular analysis of the Sorghum genus has identified close relatives of S. bicolor with novel traits, endosperm structure and composition that may be used to expand the cultivated gene pool. Mutant populations (including TILLING populations) provide a useful addition to genetic resources for this species. Sugarcane is a complex polyploid with a large and variable number of copies of each gene. The wild relatives of sugarcane represent a reservoir of genetic diversity for use in sugarcane improvement. Techniques for quantitative molecular analysis of gene or allele copy number in this genetically complex crop have been developed. SNP discovery and mapping in sugarcane has been advanced by the development of high-throughput techniques for ecoTILLING in sugarcane. Genetic linkage maps of the sugarcane genome are being improved for use in breeding selection. The improvement of both sorghum and sugarcane will be accelerated by the incorporation of more diverse germplasm into the domesticated gene pools using molecular tools and the improved knowledge of these genomes

Ambient Fabrication of Organic–Inorganic Hybrid Perovskite Solar Cells

Organic–inorganic hybrid perovskite solar cells (PSCs) have attracted significant attention in recent years due to their high‐power conversion efficiency, simple fabrication, and low material cost. However, due to their high sensitivity to moisture and oxygen, high efficiency PSCs are mainly constructed in an inert environment. This has led to significant concerns associated with the long‐term stability and manufacturing costs, which are some of the major limitations for the commercialization of this cutting‐edge technology. Over the past few years, excellent progress in fabricating PSCs in ambient conditions has been made. These advancements have drawn considerable research interest in the photovoltaic community and shown great promise for the successful commercialization of efficient and stable PSCs. In this review, after providing an overview to the influence of an ambient fabrication environment on perovskite films, recent advances in fabricating efficient and stable PSCs in ambient conditions are discussed. Along with discussing the underlying challenges and limitations, the most appropriate strategies to fabricate efficient PSCs under ambient conditions are summarized along with multiple roadmaps to assist in the future development of this technology

Ambient fabrication of organic-inorganic hybrid Perovskite solar cells

Organic–inorganic hybrid perovskite solar cells (PSCs) have attracted significant attention in recent years due to their high‐power conversion efficiency, simple fabrication, and low material cost. However, due to their high sensitivity to moisture and oxygen, high efficiency PSCs are mainly constructed in an inert environment. This has led to significant concerns associated with the long‐term stability and manufacturing costs, which are some of the major limitations for the commercialization of this cutting‐edge technology. Over the past few years, excellent progress in fabricating PSCs in ambient conditions has been made. These advancements have drawn considerable research interest in the photovoltaic community and shown great promise for the successful commercialization of efficient and stable PSCs. In this review, after providing an overview to the influence of an ambient fabrication environment on perovskite films, recent advances in fabricating efficient and stable PSCs in ambient conditions are discussed. Along with discussing the underlying challenges and limitations, the most appropriate strategies to fabricate efficient PSCs under ambient conditions are summarized along with multiple roadmaps to assist in the future development of this technology

Microwave-assisted synthesis of black phosphorus quantum dots: Efficient electrocatalyst for oxygen evolution reaction

A simple and efficient approach to produce high quality black phosphorus quantum dots (BPQDs) in a common organic solvent using a microwave technique is developed in this work. This novel approach produces a stable dispersion of crystalline BPQDs with an average lateral size of 2.95 nm and thickness of 3.59 nm. We demonstrated that the as-prepared BPQDs can be an efficient electrocatalyst for the oxygen evolution reaction (OER). Our BPQDs without any supporting catalyst exhibited an impressive electrocatalytic activity for OER with an overpotential of 450 mV at 10 mA cm-2, whilst the traditional CoOx electrocatalyst showed an overpotential of 480 mV. By integrating our BPQDs with CoOx, we achieved an outstanding electrocatalytic OER performance with an overpotential of 360 mV at 10 mA cm-2, a low Tafel slope of 58.5 mV dec-1 and excellent stability, which was even comparable to the commercial IrO2 and RuO2 systems. This work introduces a promising protocol to prepare scalable BPQDs for real-world applications including electrocatalysis

Efficient and Fast Synthesis of Few-Layer Black Phosphorus via Microwave-Assisted Liquid-Phase Exfoliation

High‐quality, few‐layer black‐phosphorus (BP) flakes are prepared in a common organic solvent with very short processing times using microwave‐assisted liquid‐phase exfoliation. A comprehensive range of analysis, combined with density‐functional theory calculations, confirms that the product prepared using the microwave technique is few‐layer BP with small‐ and large‐area flakes. The suspended exfoliated BP sheets show excellent stability, while samples dispersed onto silicon from the suspensions exhibit low oxidation levels after several days in ambient conditions. This straightforward synthesis method is facile, efficient, and extremely fast, and does not involve use of any surfactant or ultrasonication steps and will facilitate future development of phosphorene research

Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discovery

The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can be often extremely time consuming. We describe a time and resource-efficient machine learning approach to create a large dataset of structural properties of van der Waals layered structures. In particular, we focus on the interlayer energy and the elastic constant of layered materials composed of two different 2-dimensional (2D) structures, that are important for novel solid lubricant and super-lubricant materials. We show that machine learning models can recapitulate results of computationally expansive approaches (i.e. density functional theory) with high accuracy

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

© 2020 Wiley-VCH GmbH The screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and accurate tool to predict the properties of novel materials, it still demands extensive and expensive state-of-the-art computational resources. Additionally, they can often be extremely time consuming. A time and resource efficient machine learning approach to create a dataset of structural properties of 18 million van der Waals layered structures is described. In particular, the authors focus on the interlayer energy and the elastic constant of layered materials composed of two different 2D structures that are important for novel solid lubricant and super-lubricant materials. It is shown that machine learning models can predict results of computationally expansive approaches (i.e., density functional theory) with high accuracy