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Many studies were conducted to find possible strategies for reducing the urban heat island (UHI) effect during the hot summer months. One of the largest contributors to UHI is the role that paved surfaces play in the warming of urban areas. Solar-reflective cool pavements stay cooler in the sun than traditional pavements. Pavement reflectance can be enhanced by using a reflective surface coating. The use of heat-reflective coatings to combat the effects of pavements on UHI was pre-viously studied but no consistent conclusions were drawn. To find a conclusive solution, this work focuses on the abilities of heat-reflective pavement coatings to reduce UHI in varying weather conditions. Within this context, both concrete and asphalt samples were subject to a series of per-formance tests when applied to a heat-reflective coating, under the influence of normal, windy, and humid conditions. During these tests, the samples were heated with a halogen lamp and the surface temperature profile was measured using an infrared thermal camera. The air temperature was recorded with a thermometer, and the body temperature at multiple depths of the samples was measured using thermocouples. The results from these tests show that the effectiveness of the heat-reflective coating varies under different weather conditions. For instance, the coated samples were about 1 °C cooler for concrete and nearly 5 °C cooler for asphalt, on average. However, this temperature difference was reduced significantly under windy conditions. As such, the findings from this work conclude that the heat-reflective coatings can effectively cool down the pavement by increasing the surface albedo, and thus might be a viable solution to mitigate UHI impacts in the city/urban areas. 

Graphene has proven to be useful in biosensing applications. However, one of the main hurdles with printed graphene-based electrodes is achieving repeatable electrochemical performance from one printed electrode to another. We have developed a consistent fabrication process to control the sheet resistance of inkjet-printed graphene electrodes, thereby accomplishing repeatable electrochemical performance. Herein, we investigated the electrochemical properties of multilayered graphene (MLG) electrodes fully inkjet-printed (IJP) on flexible Kapton substrates. The electrodes were fabricated by inkjet printing three materials – (1) a conductive silver ink for electrical contact, (2) an insulating dielectric ink, and (3) MLG ink as the sensing material. The selected materials and fabrication methods provided great control over the ink rheology and material deposition, which enabled stable and repeatable electrochemical response: bending tests revealed the electrochemical behavior of these sensors remained consistent over 1000 bend cycles. Due to the abundance of structural defects ( e.g. , edge defects) present in the exfoliated graphene platelets, cyclic voltammetry (CV) of the graphene electrodes showed good electron transfer ( k = 1.125 × 10 −2 cm s −1 ) with a detection limit (0.01 mM) for the ferric/ferrocyanide redox couple, [Fe(CN) 6 ] −3/−4 , which is comparable or superior to modified graphene or graphene oxide-based sensors. Additionally, the potentiometric response of the electrodes displayed good sensitivity over the pH range of 4–10. Moreover, a fully IJP three-electrode device (MLG, platinum, and Ag/AgCl) also showed quasi-reversibility compared to a single IJP MLG electrode device. These findings demonstrate significant promise for scalable fabrication of a flexible, low cost, and fully-IJP wearable sensor system needed for space, military, and commercial biosensing applications. 

Nanoscale oxide-based negative electrodes are of great interest for lithium ion batteries due to their high energy density, power density and enhanced safety. In this work, we conducted a case study on mesoporous TiO 2 nanoparticle negative electrodes with uniform size and varying crystallinity in order to investigate the trend in the electrochemical properties of oxide-based nanoscale negative electrodes with varying crystallinity. Mesoporous solid spherical TiO 2 nanoparticles with a uniform particle size and varying crystallinity, i.e. , amorphous TiO 2 (A-TiO 2 ), partially crystalline TiO 2 (PC-TiO 2 ) and fully crystalline TiO 2 (FC-TiO 2 ) nanoparticles were studied. At low current rate (quasi steady-state), the specific capacity of the samples drops with the decrease of crystallinity. Ex situ synchrotron pair distribution function analysis reveals that the 1D zigzag Li ion diffusion pathway becomes expanded with the increase of crystallinity, which promotes ion mobility and charge storage. At high current rates (away from equilibrium states), however, the A-TiO 2 sample demonstrates slightly larger capacity than the FC-TiO 2 sample, both of which show larger capacities than that of the PC-TiO 2 sample. Both A-TiO 2 and FC-TiO 2 samples exhibit higher capacitive contribution to the charge storage and larger Li + diffusivity than those of the PC-TiO 2 sample, which explains their better rate capability. Moreover, the larger Li + diffusivity of the A-TiO 2 sample leads to the slightly larger specific capacity than the FC-TiO 2 sample at the highest current rate. 

The predictive capabilities of computational materials science today derive from overlapping advances in simulation tools, modeling techniques, and best practices. We outline this ecosystem of molecular simulations by explaining how important contributions in each of these areas have fed into each other. The combined output of these tools, techniques, and practices is the ability for researchers to advance understanding by efficiently combining simple models with powerful software. As specific examples, we show how the prediction of organic photovoltaic morphologies have improved by orders of magnitude over the last decade, and how the processing of reacting epoxy thermosets can now be investigated with million-particle models. We discuss these two materials systems and the training of materials simulators through the lens of cognitive load theory. For students, the broad view of ecosystem components should facilitate understanding how the key parts relate to each other first, followed by targeted exploration. In this way, the paper is organized in loose analogy to a coarse-grained model: The main components provide basic framing and accelerated sampling from which deeper research is better contextualized. For mentors, this paper is organized to provide a snapshot in time of the current simulation ecosystem and an on-ramp for simulation experts into the literature on pedagogical practice. 

We develop an optimized force-field for poly(3-hexylthiophene) (P3HT) and demonstrate its utility for predicting thermodynamic self-assembly. In particular, we consider short oligomer chains, model electrostatics and solvent implicitly, and coarsely model solvent evaporation. We quantify the performance of our model to determine what the optimal system sizes are for exploring self-assembly at combinations of state variables. We perform molecular dynamics simulations to predict the self-assembly of P3HT at ∼350 combinations of temperature and solvent quality. Our structural calculations predict that the highest degrees of order are obtained with good solvents just below the melting temperature. We find our model produces the most accurate structural predictions to date, as measured by agreement with grazing incident X-ray scattering experiments.