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1 T -TaS 2 undergoes successive phase transitions upon cooling and eventually enters an insulating state of mysterious origin. Some consider this state to be a band insulator with interlayer stacking order, yet others attribute it to Mott physics that support a quantum spin liquid state. Here, we determine the electronic and structural properties of 1 T -TaS 2 using angle-resolved photoemission spectroscopy and X-Ray diffraction. At low temperatures, the 2π/2c-periodic band dispersion, along with half-integer-indexed diffraction peaks along the c axis, unambiguously indicates that the ground state of 1 T -TaS 2 is a band insulator with interlayer dimerization. Upon heating, however, the system undergoes a transition into a Mott insulating state, which only exists in a narrow temperature window. Our results refute the idea of searching for quantum magnetism in 1 T -TaS 2 only at low temperatures, and highlight the competition between on-site Coulomb repulsion and interlayer hopping as a crucial aspect for understanding the material’s electronic properties. 1T-TaS2 possesses complex electronic phase behaviors in transition-metal di-chalcogenides, undergoing several charge-ordered phases before finally into an insulating state of unknown origin. Here, the authors determine its electronic and structural properties experimentally, revealing its origin.

Graphene is a two-dimensional material that offers a unique combination of low density, exceptional mechanical properties, large surface area and excellent electrical conductivity. Recent progress has produced bulk 3D assemblies of graphene, such as graphene aerogels, but they possess purely stochastic porous networks, which limit their performance compared with the potential of an engineered architecture. Here we report the fabrication of periodic graphene aerogel microlattices, possessing an engineered architecture via a 3D printing technique known as direct ink writing. The 3D printed graphene aerogels are lightweight, highly conductive and exhibit supercompressibility (up to 90% compressive strain). Moreover, the Young’s moduli of the 3D printed graphene aerogels show an order of magnitude improvement over bulk graphene materials with comparable geometric density and possess large surface areas. Adapting the 3D printing technique to graphene aerogels realizes the possibility of fabricating a myriad of complex aerogel architectures for a broad range of applications. Aerogels are ultra-lightweight porous materials that possess some remarkable properties. Here, the authors use a 3D printing technique to fabricate just such a material out of graphene, exhibiting large surface area, high conductivity and supercompressibility while maintaining good structural integrity.

The mitigation of air pollution in megacities remains a great challenge because of the complex sources and formation mechanisms of aerosol particles. The 2014 Asia-Pacific Economic Cooperation (APEC) summit in Beijing serves as a unique experiment to study the impacts of emission controls on aerosol composition, size distributions, and oxidation properties. Herein, a high-resolution time-of-flight aerosol mass spectrometer was deployed in urban Beijing for real-time measurements of size-resolved non-refractory submicron aerosol (NR-PM1) species from 14 October to 12 November 2014, along with a range of collocated measurements. The average (±σ) PM1 was 41.6 (±38.9) μg m−3 during APEC, which was decreased by 53 % compared with that before APEC. The aerosol composition showed substantial changes owing to emission controls during APEC. Secondary inorganic aerosol (SIA: sulfate + nitrate + ammonium) showed significant reductions of 62–69 %, whereas organics presented much smaller decreases (35 %). The results from the positive matrix factorization of organic aerosol (OA) indicated that highly oxidized secondary organic aerosol (SOA) showed decreases similar to those of SIA during APEC. However, primary organic aerosol (POA) from cooking, traffic, and biomass-burning sources were comparable to those before APEC, indicating the presence of strong local source emissions. The oxidation properties showed corresponding changes in response to OA composition. The average oxygen-to-carbon level during APEC was 0.36 (±0.10), which is lower than the 0.43 (±0.13) measured before APEC, demonstrating a decrease in the OA oxidation degree. The changes in size distributions of primary and secondary species varied during APEC. SIA and SOA showed significant reductions in large accumulation modes with peak diameters shifting from ~ 650 to 400 nm during APEC, whereas those of POA remained relatively unchanged. The changes in aerosol composition, size distributions, and oxidation degrees during the aging processes were further illustrated in a case study of a severe haze episode. Our results elucidated a complex response of aerosol chemistry to emission controls, which has significant implications that emission controls over regional scales can substantially reduce secondary particulates. However, stricter emission controls for local source emissions are needed for further mitigating air pollution in the megacity of Beijing.

Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.

Atmospheric particles of biological origin, also referred to as bioaerosols or primary biological aerosol particles (PBAP), are important to various human health and environmental systems. There has been a recent steep increase in the frequency of published studies utilizing commercial instrumentation based on ultraviolet laser/light-induced fluorescence (UV-LIF), such as the WIBS (wideband integrated bioaerosol sensor) or UV-APS (ultraviolet aerodynamic particle sizer), for bioaerosol detection both outdoors and in the built environment. Significant work over several decades supported the development of the general technologies, but efforts to systematically characterize the operation of new commercial sensors have remained lacking. Specifically, there have been gaps in the understanding of how different classes of biological and non-biological particles can influence the detection ability of LIF instrumentation. Here we present a systematic characterization of the WIBS-4A instrument using 69 types of aerosol materials, including a representative list of pollen, fungal spores, and bacteria as well as the most important groups of non-biological materials reported to exhibit interfering fluorescent properties. Broad separation can be seen between the biological and non-biological particles directly using the five WIBS output parameters and by taking advantage of the particle classification analysis introduced by Perring et al. (2015). We highlight the importance that particle size plays on observed fluorescence properties and thus in the Perring-style particle classification. We also discuss several particle analysis strategies, including the commonly used fluorescence threshold defined as the mean instrument background (forced trigger; FT) plus 3 standard deviations (σ) of the measurement. Changing the particle fluorescence threshold was shown to have a significant impact on fluorescence fraction and particle type classification. We conclude that raising the fluorescence threshold from FT + 3σ to FT + 9σ does little to reduce the relative fraction of biological material considered fluorescent but can significantly reduce the interference from mineral dust and other non-biological aerosols. We discuss examples of highly fluorescent interfering particles, such as brown carbon, diesel soot, and cotton fibers, and how these may impact WIBS analysis and data interpretation in various indoor and outdoor environments. The performance of the particle asymmetry factor (AF) reported by the instrument was assessed across particle types as a function of particle size, and comments on the reliability of this parameter are given. A comprehensive online supplement is provided, which includes size distributions broken down by fluorescent particle type for all 69 aerosol materials and comparing threshold strategies. Lastly, the study was designed to propose analysis strategies that may be useful to the broader community of UV-LIF instrumentation users in order to promote deeper discussions about how best to continue improving UV-LIF instrumentation and results.

The megacity of Beijing has experienced frequent severe fine particle pollution during the last decade. Although the sources and formation mechanisms of aerosol particles have been extensively investigated on the basis of ground measurements, real-time characterization of aerosol particle composition and sources above the urban canopy in Beijing is rare. In this study, we conducted real-time measurements of non-refractory submicron aerosol (NR-PM1) composition at 260 m at the Beijing 325 m meteorological tower (BMT) from 10 October to 12 November 2014, by using an aerosol chemical speciation monitor (ACSM) along with synchronous measurements of size-resolved NR-PM1 composition near ground level using a high-resolution time-of-flight aerosol mass spectrometer (HR–ToF–AMS). The NR-PM1 composition above the urban canopy was dominated by organics (46 %), followed by nitrate (27 %) and sulfate (13 %). The high contribution of nitrate and high NO3− / SO42− mass ratios illustrates an important role of nitrate in particulate matter (PM) pollution during the study period. The organic aerosol (OA) was mainly composed of secondary OA (SOA), accounting for 61 % on an average. Different from that measured at the ground site, primary OA (POA) correlated moderately with SOA, likely suggesting a high contribution from regional transport above the urban canopy. The Asia–Pacific Economic Cooperation (APEC) summit with strict emission controls provides a unique opportunity to study the impacts of emission controls on aerosol chemistry. All aerosol species were shown to have significant decreases of 40–80 % during APEC from those measured before APEC, suggesting that emission controls over regional scales substantially reduced PM levels. However, the bulk aerosol composition was relatively similar before and during APEC as a result of synergetic controls of aerosol precursors. In addition to emission controls, the routine circulations of mountain–valley breezes were also found to play an important role in alleviating PM levels and achieving the \"APEC blue\" effect. The evolution of vertical differences between 260 m and the ground level was also investigated. Our results show complex vertical differences during the formation and evolution of severe haze episodes that are closely related to aerosol sources and boundary-layer dynamics.

Research summary: Among the most difficult firm strategic choices is the trade‐off between making a long‐term commitment or holding off on investment in the face of uncertainty. To operationalize strategic management theory under demand, technological and competitive uncertainty, we develop a Strategic Net Present Value (NPV) framework that integrates real options and game theory to quantify value components and interactions at the interface between NPV, real options, and strategic games. Our approach results in new propositions clarifying the way learning‐experience conditions, technological uncertainty, and proprietary information interact to tilt the balance in the interplay between wait‐and‐see flexibility and strategic commitment. As such, Strategic NPV adds to our understanding of the conditions where NPV, real options, or strategic thinking are more relevant. Managerial summary: This study develops and elucidates implementation of a new valuation construct, “Strategic Net Present Value (NPV),” that integrates real options and game theory to more accurately portray strategic decisions underlying management theory. Among the most difficult firm strategic choices in capital intensive industries, such as energy, mining, chip manufacturing, and infrastructure development, is the trade‐off between making a long‐term commitment or holding off on investment in the face of demand, technological, and competitive uncertainties. The study provides new insights on the way various conditions, such as learning‐experience effects, technological uncertainty, and proprietary information, interact to tilt the balance in the interplay between commitment and wait‐and‐see flexibility. As such, Strategic NPV adds to our understanding of when NPV, real options, or strategic thinking matter more critically for decision making. Copyright © 2017 John Wiley & Sons, Ltd.

Despite ML’s impressive performance in commercial applications, several unique challenges exist when applying ML in materials science applications. In such a context, the contributions of this work are twofold. First, we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material data. Specifically, we show that with imbalanced data, standard methods for assessing quality of ML models break down and lead to misleading conclusions. Furthermore, we find that the model’s own confidence score cannot be trusted and model introspection methods (using simpler models) do not help as they result in loss of predictive performance (reliability-explainability trade-off). Second, to overcome these challenges, we propose a general-purpose explainable and reliable machine-learning framework. Specifically, we propose a generic pipeline that employs an ensemble of simpler models to reliably predict material properties. We also propose a transfer learning technique and show that the performance loss due to models’ simplicity can be overcome by exploiting correlations among different material properties. A new evaluation metric and a trust score to better quantify the confidence in the predictions are also proposed. To improve the interpretability, we add a rationale generator component to our framework which provides both model-level and decision-level explanations. Finally, we demonstrate the versatility of our technique on two applications: (1) predicting properties of crystalline compounds and (2) identifying potentially stable solar cell materials. We also point to some outstanding issues yet to be resolved for a successful application of ML in material science.

In this paper, we rigorously study the problem of cost optimisation of hybrid (mixed) institutional incentives, which are a plan of actions involving the use of reward and punishment by an external decision-maker, for maximising the level (or guaranteeing at least a certain level) of cooperative behaviour in a well-mixed, finite population of self-regarding individuals who interact via cooperation dilemmas (Donation Game or Public Goods Game). We show that a mixed incentive scheme can offer a more cost-efficient approach for providing incentives while ensuring the same level or standard of cooperation in the long-run. We establish the asymptotic behaviour (namely neutral drift, strong selection, and infinite-population limits). We prove the existence of a phase transition, obtaining the critical threshold of the strength of selection at which the monotonicity of the cost function changes and providing an algorithm for finding the optimal value of the individual incentive cost. Our analytical results are illustrated with numerical investigations. Overall, our analysis provides novel theoretical insights into the design of cost-efficient institutional incentive mechanisms for promoting the evolution of cooperation in stochastic systems.

The main objective of this study is to investigate the formation and evolution mechanism of the regional haze in megacity Beijing by analyzing the process of a severe haze that occurred 20–27 September 2011. Mass concentration and size distribution of aerosol particles as well as aerosol optical properties were concurrently measured at the Beijing urban atmospheric environment monitoring station. Gaseous pollutants (SO2, NO-NO2-NOx, O3, CO) and meteorological parameters (wind speed, wind direction, and relative humidity) were simultaneously monitored. Meanwhile, aerosol spatial distribution and the height of planetary boundary layer (PBL) were retrieved from the signal of satellite and LIDAR (light detection and ranging). Concentrations of NO, NO2, SO2, O3, and CO observed during 23–27 September had exceeded the national ambient air quality standards for residents. The mass concentration of PM2.5 gradually accumulated during the measurement and reached at 220 μg m−3 on 26 September, and the corresponding atmospheric visibility was only 1.1 km. The daily averaged AOD in Beijing increased from ~ 0.16 at λ = 500 nm on 22 September and reached ~ 3.5 on 26 September. The key factors that affected the formation and evolution of this haze episode were stable anti-cyclone synoptic conditions at the surface, decreasing of the height of PBL, heavy pollution emissions from urban area, number and size evolution of aerosols, and hygroscopic growth for aerosol scattering. This case study may provide valuable information for the public to recognize the formation mechanism of the regional haze event over the megacity, which is also useful for the government to adopt scientific approach to forecast and eliminate the occurrence of regional haze in China.