Non-fullerene acceptors (NFAs) are currently a major focus of research in the development of bulk-heterojunction organic solar cells (OSCs). In contrast to the widely used fullerene acceptors (FAs), the optical properties and electronic energy levels of NFAs can be readily tuned. NFA-based OSCs can also achieve greater thermal stability and photochemical stability, as well as longer device lifetimes, than their FA-based counterparts. Historically, the performance of NFA OSCs has lagged behind that of fullerene devices. However, recent developments have led to a rapid increase in power conversion efficiencies for NFA OSCs, with values now exceeding 13%, demonstrating the viability of using NFAs to replace FAs in next-generation high-performance OSCs. This Review discusses the important work that has led to this remarkable progress, focusing on the two most promising NFA classes to date: rylene diimide-based materials and materials based on fused aromatic cores with strong electron-accepting end groups. The key structure–property relationships, donor–acceptor matching criteria and aspects of device physics are discussed. Finally, we consider the remaining challenges and promising future directions for the NFA OSCs field. Non-fullerene acceptors have been widely used in organic solar cells over the past 3 years. This Review focuses on the two most promising classes of non-fullerene acceptors — rylene diimide-based materials and fused-ring electron acceptors — and discusses structure–property relationships, donor– acceptor matching criteria and device physics, as well as future research directions for the field.

Non-fullerene acceptors for organic solar cells

Two-photon excitation provides a means of activating chemical or physical processes with high spatial resolution in three dimensions and has made possible the development of three-dimensional fluorescence imaging, optical data storage, and lithographic microfabrication. These applications take advantage of the fact that the two-photon absorption probability depends quadratically on intensity, so under tight-focusing conditions, the absorption is confined at the focus to a volume of order λ3 (where λ is the laser wavelength). Any subsequent process, such as fluorescence or a photoinduced chemical reaction, is also localized in this small volume. Although three-dimensional data storage and microfabrication have been illustrated using two-photon-initiated polymerization of resins incorporating conventional ultraviolet-absorbing initiators, such photopolymer systems exhibit low photosensitivity as the initiators have small two-photon absorption cross-sections (δ). Consequently, this approach requires high laser power, and its widespread use remains impractical. Here we report on a class of π;-conjugated compounds that exhibit large δ (as high as 1, 250 × 10−50 cm4 s per photon) and enhanced two-photon sensitivity relative to ultraviolet initiators. Two-photon excitable resins based on these new initiators have been developed and used to demonstrate a scheme for three-dimensional data storage which permits fluorescent and refractive read-out, and the fabrication of three-dimensional micro-optical and micromechanical structures, including photonic-bandgap-type structures.

Two-photon polymerization initiators for three-dimensional optical data storage and microfabrication

Elements Of X Ray Diffraction

2005년 대한 생화학·분자생물학회 하계학술대회를 다녀와서

In recent times, surface water resource in the Western Region of Ghana has been found to be inadequate in supply and polluted by various anthropogenic activities. As a result of these problems, the demand for groundwater by the human populations in the peri-urban communities for domestic, municipal and irrigation purposes has increased without prior knowledge of its water quality. Water samples were collected from 14 public hand-dug wells during the rainy season in 2013 and investigated for total coliforms, Escherichia coli, mercury (Hg), arsenic (As), cadmium (Cd) and physicochemical parameters. Multivariate statistical analysis of the dataset and a linear stoichiometric plot of major ions were applied to group the water samples and to identify the main factors and sources of contamination. Hierarchal cluster analysis revealed four clusters from the hydrochemical variables (R-mode) and three clusters in the case of water samples (Q-mode) after z score standardization. Principal component analysis after a varimax rotation of the dataset indicated that the four factors extracted explained 93.3 % of the total variance, which highlighted salinity, toxic elements and hardness pollution as the dominant factors affecting groundwater quality. Cation exchange, mineral dissolution and silicate weathering influenced groundwater quality. The ranking order of major ions was Na+ > Ca2+ > K+ > Mg2+ and Cl− > SO4
 2− > HCO3
 −. Based on piper plot and the hydrogeology of the study area, sodium chloride (86 %), sodium hydrogen carbonate and sodium carbonate (14 %) water types were identified. Although E. coli were absent in the water samples, 36 % of the wells contained total coliforms (Enterobacter species) which exceeded the WHO guidelines limit of zero colony-forming unit (CFU)/100 mL of drinking water. With the exception of Hg, the concentration of As and Cd in 79 and 43 % of the water samples exceeded the WHO guideline limits of 10 and 3 μg/L for drinking water, respectively. Reported values in some areas in Nigeria, Malaysia and USA indicated that the maximum concentration of Cd was low and As was high in this study. Health risk assessment of Cd, As and Hg based on average daily dose, hazard quotient and cancer risk was determined. In conclusion, multiple natural processes and anthropogenic activities from non-point sources contributed significantly to groundwater salinization, hardness, toxic element and microbiological contamination of the study area. The outcome of this study can be used as a baseline data to prioritize areas for future sustainable development of public wells.

Total coliforms, arsenic and cadmium exposure through drinking water in the Western Region of Ghana: application of multivariate statistical technique to groundwater quality

Triphenylamine based dyes for dye sensitized solar cells: A review

Achievement of High Voc of 1.02 V for P3HT-Based Organic Solar Cell Using a Benzotriazole-Containing Non-Fullerene Acceptor

This article is written to provide an up-to-date review of porphyrin-based materials used in organic solar cells (OSCs). During the past two decades, OSCs have been the subject of extensive research and significant efforts have been devoted to developing low-cost OSCs, and they are not far from commercialization. Porphyrin and its analogues have been successfully applied to different optoelectronic devices, especially attaining remarkable fame when applied in dye sensitized solar cells. Despite the initial failures of their application in OSCs, porphyrins still attract much attention because of their structural versatility and recently realized significant improvement. In this review, we focus on summarizing the recent progress in porphyrin-based photovoltaic materials, including polymers, dyads, triads, small-molecules, and so on. We hope this paper could provide an in-depth study on the structure–property–performance relationship and provide a guideline for the further development of porphyrin-based and even other photovoltaic materials.

Recent progress in porphyrin-based materials for organic solar cells

Machine learning (ML) is a field of computer science that uses algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods. Owing to the chemical versatility of organic building blocks, a large number of organic semi-conductors have been used for organic solar cells. Selecting a suitable organic semi-conductor is like searching for a needle in a haystack. Data-driven science, the fourth paradigm of science, has the potential to guide experimentalists to discover and develop new high-performance materials. The last decade has seen impressive progress in materials informatics and data science; however, data-driven molecular design of organic solar cell materials is still challenging. The data-analysis capability of machine learning methods is well known. This review is written about the use of machine learning methods for organic solar cell research. In this review, we have outlined the basics of machine learning and common procedures for applying machine learning. A brief introduction on different classes of machine learning algorithms as well as related software and tools is provided. Then, the current research status of machine learning in organic solar cells is reviewed. We have discussed the challenges in anticipating the data driven material design, such as the complexity metric of organic solar cells, diversity of chemical structures and necessary programming ability. We have also proposed some suggestions that can enhance the usefulness of machine learning for organic solar cell research enterprises.

Machine learning for high performance organic solar cells: current scenario and future prospects

The power conversion efficiency (PCE) of organic solar cells (OSCs) is increasing continuously, however, commercialization is far from being achieved due to the very high synthetic cost of materials and toxic solvents. Poly(3-hexylthiophene) (P3HT) is the cheapest donor, however, its PCE has remained relatively low for a long time. Recently, a PCE of over 9% has been reported. This study was performed with the aim of predicting the performance of P3HT based organic solar cells through statistical data fit, bypassing the complexity of OSC devices. For this purpose, we have used machine learning to explore data from previously reported devices. Molecular descriptors were used to train machine learning models. We have identified the descriptors that have a positive impact on the PCE. Various machine learning models are used to classify non-fullerene acceptors (NFAs) on the basis of their PCEs. We have also developed various regression models to predict the PCE. The support vector machine showed the best predictive capability. Machine learning models were also trained to predict the energy levels. Over 3000 NFAs were designed using high performing and easily synthesizable building blocks. For virtual screening of new molecules, energy levels were predicted through the already trained model. Acceptors with suitable energy levels matching with those of P3HT were selected. The PCEs of these selected acceptors were also predicted. 87 acceptors with >7.5% PCE were selected. Green solvents were selected on the basis of Hansen solubility parameters predicted using machine learning. Green solvent-containing organic solar cells have better scope of commercialization due to their environment-friendly nature. This study will pave the way for the cheap and fast design of materials for efficient P3HT-based organic solar cells. This study will help to select potential candidates and speed up the breakthroughs.

A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection

Asif Mahmood

Papers

Triphenylamine based dyes for dye sensitized solar cells: A review

Achievement of High Voc of 1.02 V for P3HT-Based Organic Solar Cell Using a Benzotriazole-Containing Non-Fullerene Acceptor

Recent progress in porphyrin-based materials for organic solar cells

Machine learning for high performance organic solar cells: current scenario and future prospects

A time and resource efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT-based organic solar cells and green solvent selection