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The need for practical anodes for rechargeable batteries calls for new materials with improved storage capacity and cycling stability compared to the materials of today. Materials capable of alloying with alkali metals have been long viewed as a promising pathway to address this challenge. Meanwhile, severe drawbacks associated with the use of such materials led to the development of suitable alternatives, in which governing the reaction mechanism in a battery combines conversion and alloying reactions. These materials feature a high storage capacity and long-term stability during electrochemical cycling. However, the development of these materials is impeded by a lack of fundamental structural understanding due to the complexity of the chemical transformations taking place during battery cycling. Furthermore, amorphization, which is prevalent in these materials, also severely limits the conventional methods of characterization. In the present work, we propose a computational methodology for understanding the structure and reaction mechanism of these conversion–alloying materials by using amorphous substoichiometric silicon nitride as a model system. The developed ReaxFF parameter set allowed exploring the atomistic structure of this material and verify the resulting models through a comparison of calculated and experimentally obtained pair distribution functions (PDFs). The developed methodology also allowed exploring the structural changes occurring during lithiation and delithiation, i.e., operation mechanism in a battery. The analysis of the atomistic structure demonstrates that initial lithiation results in the formation of a Si network, which later participates in further lithiation process. Delithiation also influences the evolution of the amorphous Si network facilitating separation of nitrogen-rich and Si-rich regions and, therefore, confirming the conversion mechanism proposed earlier. Furthermore, the proposed approach allows modeling not only changes at the atomistic scale but also predicting the experimental PDF at different stages of cycling which allows experimentally verifying the outcome of modeling.

Niobates, tantalates, and bismuthates have recently attracted attention as compounds to be used in photonics and energy-harvesting applications as substitutes for the relatively unstable lead-halide perovskites. Having the photocatalytic reaction for H2 production and CO2 conversion in mind, we perform first-principles materials design to determine the potential for electrochemistry of certain Nb-, Ta-, and Bi-based oxides/chalcogenides: NaMO(3−x)Qx (M = Nb, Ta and Q = S, Se), and ABiX2 and ABiX3 (A = Na, K and X = O, S). The analysis of the effective masses, alongside the determination of the actual electronic dimensionality, reveals that several compounds exhibit reduced electronic dimensionality despite possessing a higher structural dimensionality. The electrochemical performances of all compounds are then investigated by computing the band-edge alignment with respect to various redox levels, against the relevant photocatalysis reactions in aqueous media at different pH's. Our work highlights the effect of chalcogen incorporation in the electrochemical performance of this class of materials, and proposes some of the Bi-based chalcogenides, namely KBiO2 and KBiS2, as efficient photocatalysts over a wide range of pH conditions.

Data-driven approaches for materials design and selection have accelerated materials discovery along with the upsurge of machine learning applications. We report here a prediction-to-lab-scale synt...

The need for good research data management practices is becoming more recognized as a critical part of research. This is driven by the exponential rate at which data were obtained through high-throughput computations leading to the 5V challenge in Big Data, including volume, variety, velocity, veracity, and value. Poorly managed data often restricts its usability and accessibility, especially when various research groups and organizations collaborate on multidisciplinary research projects. This deters data users because they lack a basic understanding of the data's provenance and conditions of use. The material science community is no exception to the challenges of data deluge as it heralds its new paradigm of data-driven science. This paradigm uses artificial intelligence to accelerate materials discovery but requires massive datasets to perform effectively. Hence, there are efforts to standardize, curate, preserve, and disseminate these data in a way that is Findable, Accessible, Interoperable, and Reusable (FAIR). To understand the current state of data-driven material science, we surveyed researchers working on a small-scale research project and another within a large consortium. We analyze the current research status of each community and the challenges they face regarding the use and management of research data. This enables us to provide relevant recommendations to develop and/or procure an effective research data management system following the FAIR guiding principles. This work positions these recommendations on their urgency within the data-driven research life cycle.

Nowadays, most experiments to synthesize and test photocatalytic antimicrobial materials are based on trial and error. More often than not, the mechanism of action of the antimicrobial activity is unknown for a large spectrum of microorganisms. Here, we propose a scheme to speed up the design and optimization of photocatalytic antimicrobial surfaces tailored to give a balanced production of reactive oxygen species (ROS) upon illumination. Using an experiment-to-machine-learning scheme applied to a limited experimental dataset, we built a model that can predict the photocatalytic activity of materials for antimicrobial applications over a wide range of material compositions. This machine-learning-assisted strategy offers the opportunity to reduce the cost, labor, time, and precursors consumed during experiments that are based on trial and error. Our strategy may significantly accelerate the large-scale deployment of photocatalysts as a promising route to mitigate fomite transmission of pathogens (bacteria, viruses, fungi) in hospital settings and public places.

Forecasting the structural stability of hybrid organic/inorganic compounds, where polyatomic molecules replace atoms, is a challenging task; the composition space is vast, and the reference structure for the organic molecules is ambiguously defined. In this work, we use a range of machine-learning algorithms, constructed from state-of-the-art density functional theory data, to conduct a systematic analysis on the likelihood of a given cation to be housed in the perovskite structure. In particular, we consider both ABC3 chalcogenide (I-V-VI3) and halide (I-II-VII3) perovskites. We find that the effective atomic radius and the number of lone pairs residing on the A-site cation are sufficient features to describe the perovskite phase stability. Thus, the presented machine-learning approach provides an efficient way to map the phase stability of the vast class of compounds, including situations where a cation mixture replaces a single A-site cation. This work demonstrates that advanced electronic structure theory combined with machine-learning analysis can provide an efficient strategy superior to the conventional trial-and-error approach in materials design.

Automated metaphor detection is a challenging task to identify metaphorical expressions of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. To this end, we propose a novel metaphor detection model, namely metaphor-aware late interaction over BERT (MelBERT). Our model not only leverages contextualized word representation but also benefits from linguistic metaphor identification theories to distinguish between the contextual and literal meaning of words. Our empirical results demonstrate that MelBERT outperforms several strong baselines on four benchmark datasets, i.e., VUA-18, VUA-20, MOH-X, and TroFi., In Proceedings of 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics. 11 pages

Simple Summary A modified SIR model was applied to provide COVID-19 pandemic analysis and predictions for Gulf Cooperation Council countries, as well as representative countries in Europe and New York City. We estimated reported, infected, and unreported cases from cumulative reported cases and simulated data. We also estimated the basic reproduction rates at different phases of the pandemic. Outputs show that the modified SIR model fits very well with the outcome of the COVID-19 pandemic for the studied countries and could be generalized to other countries. The model prediction emphasizes the value of significant interventions in public health in regulating the epidemic taking into account that a constant fraction of the infected cases remain unreported during the pandemic. We report and analyze the effectiveness of preventive/intervention measures applied to the overall community to curb the severity of the pandemic. Our model could be used to support public health authorities with respect to post-outbreak reopening decisions, highlighting effective measures that need to be maintained, eased, or implemented to support safe reopening strategies in the GCC countries. Abstract Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

This paper presents the Bering Lab’s submission to the shared tasks of the 8th Workshop on Asian Translation (WAT 2021) on JPC2 and NICT-SAP. We participated in all tasks on JPC2 and IT domain tasks on NICT-SAP. Our approach for all tasks mainly focused on building NMT systems in domain-specific corpora. We crawled patent document pairs for English-Japanese, Chinese-Japanese, and Korean-Japanese. After cleaning noisy data, we built parallel corpus by aligning those sentences with the sentence-level similarity scores. Also, for SAP test data, we collected the OPUS dataset including three IT domain corpora. We then trained transformer on the collected dataset. Our submission ranked 1st in eight out of fourteen tasks, achieving up to an improvement of 2.87 for JPC2 and 8.79 for NICT-SAP in BLEU score .

We present IntelliCAT, an interactive translation interface with neural models that streamline the post-editing process on machine translation output. We leverage two quality estimation (QE) models at different granularities: sentence-level QE, to predict the quality of each machine-translated sentence, and word-level QE, to locate the parts of the machine-translated sentence that need correction. Additionally, we introduce a novel translation suggestion model conditioned on both the left and right contexts, providing alternatives for specific words or phrases for correction. Finally, with word alignments, IntelliCAT automatically preserves the original document's styles in the translated document. The experimental results show that post-editing based on the proposed QE and translation suggestions can significantly improve translation quality. Furthermore, a user study reveals that three features provided in IntelliCAT significantly accelerate the post-editing task, achieving a 52.9\% speedup in translation time compared to translating from scratch. The interface is publicly available at https://intellicat.beringlab.com/., Comment: ACL 2021 (system demonstration)

Substance Use Disorder (SUD) is a devastating disease that leads to significant mental and behavioral impairments. Its negative effects damage the homeless youth population more severely (as compared to stably housed counterparts) because of their high-risk behaviors. To assist policymakers in devising effective and accurate long-term strategies to mitigate SUD, it is necessary to critically analyze environmental, psychological, and other factors associated with SUD among homeless youth. Unfortunately, there is no definitive data-driven study on analyzing factors associated with SUD among homeless youth. While there have been a few prior studies in the past, they (i) do not analyze variation in the associated factors for SUD with geographical heterogeneity in their studies; and (ii) only consider a few contributing factors to SUD in relatively small samples. This work aims to fill this gap by making the following three contributions: (i) we use a real-world dataset collected from ~1,400 homeless youth (across six American states) to build accurate Machine Learning (ML) models for predicting the susceptibility of homeless youth to SUD; (ii) we find a representative set of factors associated with SUD among this population by analyzing feature importance values associated with our ML models; and (iii) we investigate the effect of geographical heterogeneity on the factors associated with SUD. Our results show that our system using adaptively boosted decision trees achieves the best predictive accuracy out of several algorithms on the SUD prediction task, achieving an Area Under the ROC Curve of 0.85. Further, among other things, we also find that both Post-Traumatic Stress Disorder (PTSD) and depression are very strongly associated with SUD among homeless youth because of their propensity to self-medicate to alleviate stress. This work is done in collaboration with social work scientists, who are currently evaluating the results for potential future deployment.

The past decade has witnessed the rapid introduction of organic-inorganic hybrid compounds in photovoltaic applications. Motivated by the strong demand for stable and nontoxic materials in this class, we report a theoretical study on the structural, elastic, electronic, thermodynamic and dielectric properties of alkali-metal-based bismuth ternary chalcogenides. In particular, we employ state-of-the-art density functional theory to explore the potential of $A\mathrm{Bi}{X}_{2}$ and $A\mathrm{Bi}{X}_{3}$ ($A=\mathrm{Na}$, K and $X=$ O, S) as light-absorbing media. All the compounds under investigation are found to be thermodynamically and mechanically stable, with a semiconductor band structure. The Kohn-Sham band gaps range between 0.80 eV and 1.80 eV, when calculated with semilocal functionals, values that increase to 1.24--2.47 eV with hybrid ones. Although all but ${\mathrm{NaBiO}}_{2}$ and ${\mathrm{KBiO}}_{2}$ are indirect band-gap semiconductors, the onset of the imaginary part of their dielectric functions, the optical gap, is only marginally larger than the quasiparticle gap. This is due to the generally flat nature of both the conduction and the valence bands. We then expect these compounds to absorb light in the upper part of the visible spectrum. In all cases the valence band is dominated by $\mathrm{O}\text{\ensuremath{-}}p$ and S-$p$ orbitals and the conduction one by Bi-$p$, suggesting the possibility of excitons with low binding energy. The only exceptions are ${\mathrm{NaBiO}}_{2}$ and ${\mathrm{KBiO}}_{2}$ for which the $\mathrm{O}\text{\ensuremath{-}}p$ states dominate the density of states at both sides of the band gap.

Nowadays, materials design efforts to obtain nontoxic and cost-effective photoactive semiconductors with a given chemical composition face the challenge of the coexistence of more than one configur...

Among the three task dimensions Robinson (2001a) proposed as the standards for task classification in his Cog- nition Hypothesis, task complexity was...

In the quest for non-toxic and stable perovskites for solar cells, we conduct a systematic study of the effect of chalcogen content in oxychalcogenide perovskite using density functional theory (DFT) and quasi-particle perturbation theory. We explore the changes in the electronic structure due to the substitution of O atoms in NaNbO 3 and NaTaO 3 perovskite structures with various chalcogen (S, Se, Te) at different concentrations. Interestingly, the introduction of the chalcogen atoms results in a drastic reduction of the electronic band gap, making some of them fall within the visible range of the solar spectrum. In addition, our analysis of the electronic structure shows that the optical transition becomes direct as a result of the strong hybridization between the orbitals of the transition metal and those of the chalcogen ion, in contrast to the indirect band feature of NaNbO 3 and NaTaO 3 . We identify candidates that possess a high theoretical solar conversion efficiency approaching the Shockley-Queisser limit suitable for thin-film solar cells development. The present work serves as a guideline for experimental efforts by identifying the chalcogen content that should be targeted during the synthetic route of thermodynamically stable and strongly photoactive absorbers for oxychalcogenide perovskites in thin-film solar cells.

Mixing cations has been a successful strategy in perovskite synthesis by solution-processing, delivering improvements in the thermodynamic stability as well as in the lattice parameter control. Unfortunately, the relation between a given cation mixture and the associated structural deformation is not well-established, a fact that hinders an adequate identification of the optimum chemical compositions. Such difficulty arises since local distortion and microscopic disorder influence structural stability and also determine phase segregation. Hence, the search for an optimum composition is currently based on experimental trial and error, a tedious and high-cost process. Here, we report on a machine-learning-reinforced cubic-phase-perovskite stability predictor that has been constructed over an extensive dataset of first-principles calculations. Such a predictor allows us to determine the cubic phase stability at a given cation mixture regardless of the various cations’ pair and concentration, even assessing very dilute concentrations, a notoriously challenging task for first-principles calculations. In particular, we construct machine learning models, predicting multiple target quantities such as the enthalpy of mixing and various octahedral distortions. It is then the combination of these targets that guide the laboratory synthesis. Our theoretical analysis is also validated by the experimental synthesis and characterization of methylammonium–dimethylammonium-mixed perovskite thin films, demonstrating the ability of the stability predictor to drive the chemical design of this class of materials.

Given the surging growth of artificial-intelligence-inspired computational methods in materials science, experimental laboratories around the globe have become open to adopting data-driven approaches for materials discovery. The field witnesses emerging machine-learning models trained over databases, of which data are collected from high-throughput experimentation or first-principles calculation. Here, we address the impediment of constructing a highly accurate predictor for perovskite bandgap when the inorganic network undergoes the deformation. The predictor is trained on a dataset of first-principles calculations of pure and mixed-cation hybrid perovskites. We investigate the impact of the inclusion/exclusion of structural deformation features by training the model carefully. A high level of accuracy could be achieved with a scrupulous investigation of the input features. Our analysis emphasizes how important the feature selection is for the construction of the predictive model as we challenge the robustness of our machine learning predictor in a lab validation setup.

Recent years have witnessed a growing effort in engineering and tuning the properties of hybrid halide perovskites as light absorbers. These have led to the successful enhancement of their stability, a feature that is often counterbalanced by a reduction of their power-conversion efficiency. In order to provide a systematic analysis of the structure–property relationships of this class of compounds we have performed density functional theory calculations exploring fully inorganic ABC3 chalcogenide (I–V–VI3), halide (I–II–VII3) and hybrid perovskites. Special attention has been given to structures featuring three-dimensional BC6 octahedral networks because of their efficient carrier transport properties. In particular we have carefully analyzed the role of BC6 octahedral deformations, rotations and tilts in the thermodynamic stability and optical properties of the compounds. By using machine learning algorithms we have estimated the relations between the octahedral deformation and the bandgap, and established a similarity map among all the calculated compounds.

This study was performed to examine the stability of retinol contained within oil-in-water (O/W) emulsions under UV and during storage at different temperatures. O/W emulsions were prepared using different emulsifiers and oil concentrations. The stability of the retinol contained in the O/W emulsions was investigated by measuring the percentage of residual retinol in the samples after UV exposure and storage at different temperatures (4, 25, and 40 °C). The oil concentration of the emulsion had a greater impact on UV stability than the type of emulsifier used, whereas the storage stability at different temperatures was affected by both the choice of emulsifier and the oil concentration. The storage stability of the retinol contained in the O/W emulsions may be related to the lipid oxidation properties of the emulsions rather than the latter’s physical stability. Experiments with EDTA and different oil types were performed to confirm this theory.

The study reported here examined the effects of cognitive complexity on the quality of summary writing among 50 Korean college students in the context of genre repetition. The cognitive complexity was varied in terms of text type (either expository or argumentative) and in terms of length (either limited to 70 words or not limited). These variables were adopted to test Robinson"s Cognition Hypothesis (CH) (2001a, 2002, 2003a, 2005a, 2007a). CH proposes that there are certain factors that influence cognitive complexity. The manipulation of these factors in a task leads to diverse demands. A range of studies have considered the effects of task complexity on learners" language production in terms of whether it becomes more complex, accurate, and/or fluent. However, little research has been conducted on summary quality from the perspective of the CH. Four independent variables were selected to reflect summary quality, namely copied items(CO), inaccurate ideas(IA), main idea selection(SE), and creative idea invention(I). The frequency of each variable was measured in summaries written under four conditions with different cognitive demands. The 50 participants were divided into four groups, each of which wrote summaries under a different cognitive load. The participants in every group had to write six summaries in the same genre. The first and the sixth summaries of each participant were analyzed to investigate summary quality and the effects of task repetition. A repeated-measures ANOVA was conducted to determine the effects of repetition, and ANCOVA was used to control initial score differences related to the effects of text type and length limit. The results showed that task repetition significantly influenced SE out of four summary quality factors. The results suggest that differences in cognitive complexity have an effect on summary quality.

The intramolecular magnetic exchange coupling of edge terminated zigzag graphene nanoribbon (ZGNR) was studied with density functional theory calculations. In order to examine the applicability of the spin alternation rule and a classification scheme for radicals and couplers on functionalized graphene nanoribbons, we investigated the magnetic behaviors of pristine zigzag graphene nanoribbon with eight zigzag chains (8-ZGNR) and 8-ZGNRs terminated with trimethylenemethane (TMM) and 6-oxoverdazyl (OVER) radicals,that is, TMM-ZGNR-TMM (TZT), OVER-ZGNR-OVER (OZO), and TMM-ZGNR-OVER (TZO). As expected, only ZGNR terminated with different group radicals on each edge (TZO) had a ferromagnetic (high-spin) ground state with an energy gap of 39 meV/supercell (321.57 cm–1) relative to the low-spin state. This strongly supports the validity of the spin alternation rule and the classification scheme for radicals and couplers on extensively conjugated large graphene nanoribbons. TZT and OZO were found to have an antif...

In Korea, most rainfall events occur during summer which then leads to an increasing concern regarding high influx of non-point source pollutants since the pollutant loadings from these non-point sources are very significant. In particular, the first flush of roof-harvested rainfall is said to contain the most highest concentration of nutrients and heavy metals. Accordingly, it is important to develope the possible water quality management options in treating the contaminants and considering reclaimed water reuse. The rain garden could be one of suitable alternatives in addressing this issue. In this study, the development of an effective adsorption media and its application to a lab-scale rain garden was tested to evaluate the removal rate of various nutrient and organic matter (TN, TP, CODcr), and heavy metals (Cu, Cd, Pb). Results showed that carbonized peatmoss produced at higher temperature have better adsorption capacity as compared to the one produced at a lower temperature. When the carbonized peatmoss was applied as rain garden media, the highest removal of TN, TP, and CODcr was observed compared to no carbonized peatmoss applied rain garden. Therefore, this study showed that the carbonized peatmoss would be effectively applied to the rain garden for removing nutrients and heavy metals from roof-harvested rainwater.

In the quest for nontoxic and stable perovskites for solar cells, we have conducted a systematic study of the effect of chalcogen content in oxychalcogenide perovskite by using DFT and quasi-particle perturbation theory. We explored the changes in the electronic structure due to the substitution of O atoms in NaNbO

We investigated the possibility of band structure engineering of pyroxene silicates with chemical formula A+1 B+3 Si2 O6 by proper cation substitution. Typically, band gaps of naturally formed pyroxene silicates such as NaAlSi2 O6 are quite high (≈5 eV). Therefore, it is important to find a way to reduce band gaps for these materials below 3 eV to make them usable for optoelectronic applications operating at visible light range of the spectrum. Using first-principles calculations, we found that appropriate substitutions of both A+ and B3+ cations can reduce the band gaps of these materials to as low as 1.31 eV. We also discuss how the band gap in this class of materials is affected by cation radii, electronegativity of constituent elements, spin-orbit coupling, and structural modifications. In particular, the replacement of Al3+ in NaAlSi2 O6 by another trivalent cation Tl3+ results in the largest band-gap reduction and emergence of intermediate bands. We also found that all considered materials are still thermodynamically stable. This work provides a design approach for new environmentally benign and abundant materials for use in photovoltaics and optoelectronic devices.

Near-infrared spectroscopy and multivariate analysis techniques were employed to nondestructively evaluate the rancidity of perilla seed oil by developing prediction models for the acid and peroxide values. The acid, peroxide value, and transmittance spectra of perilla seed oil stored in two different environments for 96 and 144 h were obtained and used to develop prediction models for different storage conditions and time periods. Preprocessing methods were applied to the transmittance spectra of perilla seed oil, and multivariate analysis techniques, such as principal component regression (PCR), partial least squares regression (PLSR), and artificial neural network (ANN) modeling, were employed to develop the models. Titration analysis shows that the free fatty acids in an oil oxidation process were more affected by relative humidity than temperature, whereas peroxides in an oil oxidation process were more significantly affected by temperature than relative humidity for the two different environments in this study. Also, the prediction results of ANN models for both acid and peroxide values were the highest among the developed models. These results suggest that the proposed near-infrared spectroscopy technique with multivariate analysis can be used for the nondestructive evaluation of the rancidity of perilla seed oil, especially the acid and peroxide values.

We performed computational studies on the removal of an epoxide group from oxidized zigzag graphene nanoribbons (ZGNRs). Using density functional theory (DFT) calculations, we investigated the reaction mechanisms for the two competing processes, migration and reduction of an epoxide group in the middle of a narrow ZGNR. We found that the relative magnitudes of the barriers for migration and reduction depend on the width of the ZGNR. The reaction barrier for the reduction of an epoxide by CO decreased as the width of the ZGNR increased, while the barrier for migration showed the opposite trend. Moreover, the transition state energies for migration and reduction decreased upon the applied electric field perpendicular to the surface of the ZGNR. Our results illustrate that the removal of an epoxide from a ZGNR by reduction with CO can be facilitated by the application of an external electric field. For narrow ZGNRs, epoxide migration to neighboring sites may compete with the reduction reaction.

E-mail: sshin@snu.ac.kr (S. Shin); sisid1@snu.ac.kr (C. Shin)Received October 25, 2013, Accepted December 11, 2013Time-dependent formulations of the reactive scattering theory based on the wavepacket correlation functionswith the Moller wavepackets for the electronically nonadiabatic reactions are presented. The calculations ofstate-to-state reactive probabilities for the quasi-Jahn-Teller scattering model system were performed. Theconical intersection (CI) effects are investigated by comparing the results of the two-surface nonadiabaticcalculations and the single surface adiabatic approximation. It was found that the results of the two-surfacenonadiabatic calculations show interesting features in the reaction probability due to the conical intersection.Single surface adiabatic calculations with extended Born-Oppenheimer approximation using simplewavepacket phase factor was found to be able to reproduce the CI effect semi-quantitatively, while the singlesurface calculations with the usual adiabatic approximation cannot describe the scattering process for the Jahn-Teller model correctly. Key Words : Wavepacket correlation function, Nonadiabatic reaction, Conical intersectionIntroduction Reactive scattering studies have been successful for 3-and 4-atom bimolecular reactions.

The purpose of this study is to monitor the rainwater flowing from the roof of buildings and to maximize the effectiveness of the rainwater storage. This study also analyses the changes in rainwater characteristics before and after subsequent chlorination disinfection. The stored rainwater was disinfected by chlorine and then analyzed for COD, TN, TP, enteric bacteria, and general microbial population changes over time. There was an observed 99% reduction of enteric bacteria and common microbes within two weeks after chlorine injection. Thus, chlorine disinfection of rainwater improves water quality for long-term storage and future use.

Although roof-top runoff water has been considered as an alternative water resource, the harvested rainwater needs to be treated for further use because it usually contains various contaminants such as heavy metals and microbes. The degree of the harvested rainwater quality depends upon its end use such as drinking water and irrigation. Especially, when harvested rainwater is to be used as gray water, a cost effective treatment system is required. Accordingly, the main purpose of this study was to examine the adsorption characteristics of peat, cost-effective biosorbent, for various inorganic pollutants such as ammonium, copper, cadmium and lead from roof-top runoff water. As part of efforts to investigate the sorption properties of peat, batch isotherm tests were carried out under various pH conditions. The characterization of peat was carried out with powder X-ray diffraction, Brunauer–Emmett–Teller, and scanning electron microscope measurements. Both heat-treated peat and non-treated peat appeared to have high sorption capacity for all inorganic contaminants (NH4 +, Cu2+, Cd2+, and Pb2+). An interesting finding is that the amount of NH4 + sorbed on the sorbents was slightly higher compared to the other cations. Also, the sorption capacity of the peat sorbents increased with an increase of pH. On the other hand, kinetic data were well described by pseudo-second kinetic model, indicating that removal mechanism of cations by peat-derived sorbents is likely due to chemisorptions. The results of this study suggested that peat-derived porous materials can be used as effective sorbents for removal of cationic inorganic contaminants from harvested rainwater.

Correction for ‘Exploring new approaches towards the formability of mixed-ion perovskites by DFT and machine learning’ by Heesoo Park et al., Phys. Chem. Chem. Phys., 2019, DOI: 10.1039/c8cp06528d.

The influence of pyridine adsorption and the applied electric field on the band structure and metallicity of zigzag graphene nanoribbons (ZGNRs) was investigated by using density functional theory (DFT) calculations. The semiconducting ZGNRs became half-metallic or remained semiconducting depending on the configuration of N–C covalent bonds between pyridine and the outermost C atom of the ZGNRs. In addition, the band gap of the α- and β-spin states of the ZGNRs could be tuned by noncovalent bonds. This effect was enhanced when BF3 was introduced simultaneously at the opposite edge. The applied external electric field also modulated the band structures of the ZGNRs, making them half-metallic or semiconducting to some extent. These features suggest that the well-arranged adsorption of pyridine and BF3 could be used to tune the band structures of nanoscale electronic devices based on graphene.

Container molecules such as carcerand, hemicarcerand, and self-assembled molecular capsule have been characterized as molecular scavengers, molecular storages, molecular reactors and controlled-releasing systems. Various heterobridged hemicarceplexes in which the fourth bridging unit differs from the other three bridging units were reported by Cram et al. and the fourth bridging unit has been used to adopt an additional binding site or to connect with another hemicarcerand to obtain covalently linked dimeric hemicarceplexes. The characteristics of container molecules can be accumulated when they are assembled to highly ordered supramolecular systems. Dimeric container system could duplex the functions of monomeric container molecule and a wellordered multiple container system would result in a new high density information storage system. Metal coordination has become an important synthetic strategy for the self-assembly of high-ordered and welldefined supramolecular architectures because it allows well defined geometry, coordination number, and a range of binding strengths. Recently the interesting guest’s size and shape selectivities of cyanohemicarcerand 1 was reported. 8 But the stability of Pd(II) or Pt(II)-coordinated dimeric assembly 1-ML2-1 was too weak to be observed by H NMR spectrometry. Here we report on the synthesis of hemicarcerand 4 which has a metal coordinating p-pyridylphenyl unit on a pillar and its formation of dimeric self-assemblies 5a and 5b by Pd(II) and Pt(II)-coordination, respectively.