Fundamental Material Properties Research Articles

Prediction of electron-solid interaction parameters using machine learning.

Electron backscattering coefficient and electron-stopping power are essential concepts in many disciplines, from radiation to materials science, semiconductor manufacturing, and space exploration. They enable precise calculations, measurements, and simulations of electron interactions with matter, which contribute to advancing science, technology, and safety in a variety of applications. The availability of these data is fundamental to scientific research to validate hypotheses, conduct experiments, and explore new theories. A relatively novel machine learning approach has demonstrated notable success in enhancing data quality and completeness, significantly contributing to the facilitation of data discovery. Using fundamental material property data, the stacking ensemble machine learning (EML) technique was established in this study to generate electron-solid interaction parameters for any target material over a wide range of energies. The final stacking EML was built using the base and meta learners bagging regressor (BR), K-nearest neighbors (k-NN), random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGB). In this study, two publicly available databases with a total of 4030 data points were used. Training datasets have 785 and 525 data points for electron backscattering coefficient and stopping power, respectively, whereas testing datasets contain 262 and 175 data points. Fivefeatures were used as input variables to train different individual algorithms and their combinations. On both the training and test datasets, the model was evaluated using different error metrics, including R-squared (R2), mean-absolute-error (MAE), root-mean-squared-error (RMSE), and mean-absolute-percentage-error (MAPE). Our model evaluation tests revealed that combining RF and XGB with a k-NN meta-learner outperformed other algorithms. The analysis of error metrics demonstrated a very close fit to all samples in each training dataset. Furthermore, predictions made by the model on unseen test data indicated accurate estimations of new backscattering and stopping power data. The developed model achieved high prediction accuracy for various target materials across the broad electron energy spectrum. The outcomes demonstrate the effectiveness of machine learning methodology and the chosen models' suitability for addressing substantial physics challenges.

Investigation of perovskite materials for solar cells using scanning tunneling microscopy.

The issue of energy scarcity has become more prominent due to the recent scientific and technological advancements. Consequently, there is an urgent need for research on sustainable and renewable resources. Solar energy, in particular, has emerged as a highly promising option because of its pollution-free and environment-friendly characteristics. Among the various solar energy technologies, perovskite solar cells have attracted much attention due to their lower cost and higher photoelectric conversion efficiency (PCE). However, the inherent instability of perovskite materials hinders the commercialization of such devices. The utilization of scanning tunneling microscopy/spectroscopy (STM/STS) can provide valuable insights into the fundamental properties of different perovskite materials at the atomic scale, which is crucial for addressing this challenge. In this review, we present the recent research progress of STM/STS analysis applied to various perovskites for solar cells, including halide perovskites, two-dimensional Ruddlesden-Popper perovskites, and oxide perovskites. This comprehensive overview aims to inspire new ideas and strategies for optimizing solar cells.

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

Two-Dimensional Materials in Nanomaterials: Properties, Applications, and Prospects

Recently, two-dimensional (2D) materials have garnered significant attention in nanomaterials research because of their unique physical and chemical properties. This paper examines the research background, core properties, main application areas, and future prospects of 2D materials. Through a systematic literature review and case studies, this paper summarizes the main advances and challenges in current 2D material research. The research methodology includes a literature review and case study analysis, aiming to uncover the potential of 2D materials in nanoelectronics, optoelectronic devices, energy storage, and biomedicine. The research questions address the fundamental properties of 2D materials, their practical applications, and strategies to overcome existing challenges. The results indicate that while 2D materials like graphene and transition metal dichalcogenides (TMDs) exhibit excellent electronic, optical, and mechanical properties, they still encounter significant challenges in material preparation, performance tuning, and environmental safety. This paper proposes potential solutions and predicts the future applications of 2D materials in emerging technologies. The conclusion asserts that with the continuous discovery of novel 2D materials and technological advancements, 2D materials will play a crucial role in science and industry.

Monolayer-scale AlN/GaN digital alloys grown by plasma-assisted molecular beam epitaxy

The efficiency of usual AlGaN based deep ultraviolet light-emitting devices is still quite low. The difficulties are basically originated from the fundamental material properties of AlGaN. This work has adopted monolayer-scale (AlN)m/(GaN)n ordered digital alloys (DAs) as alternatives to AlGaN random alloys, m and n are the numbers of monolayers. X-ray diffraction scans have demonstrated clear satellite peaks, verifying good periodicity of AlN/GaN DAs grown by molecular beam epitaxy (MBE), and transmission electron microscopy results have revealed atomically sharp and smooth interfaces and quite precise m:n values agreeing well with designs. The electron densities of Si-doped (AlN)m/(GaN)n DAs with high equivalent Al compositions are significantly higher than those of conventional AlGaN:Si random alloys grown in the same MBE system. Si dopant ionization energies in DAs are only 2–5 meV, much lower than that for usual random alloys. The red shift of the light emission for DAs with thinner AlN barriers has suggested strong coupling between the GaN wells and thus formation of a miniband in a vertical direction. The results have demonstrated the potential of the (AlN)m/(GaN)n DAs as electronically functional alternatives for various device applications.

Dynamic-to-static switch of hydrogen bonds induces a metal-insulator transition in an organic-inorganic superlattice.

Hydrogen bonds profoundly influence the fundamental chemical, physical and biological properties of molecules and materials. Owing to their relatively weaker interactions compared to other chemical bonds, hydrogen bonds alone are generally insufficient to induce substantial changes in electrical properties, thus imposing severe constraints on their applications in related devices. Here we report a metal-insulator transition controlled by hydrogen bonds for an organic-inorganic (1,3-diaminopropane)0.5SnSe2 superlattice that exhibits a colossal on-off ratio of 107 in electrical resistivity. The key to inducing the transition is a change in the amino group's hydrogen-bonding structure from dynamic to static. In the dynamic state, thermally activated free rotation continuously breaks and forms transient hydrogen bonds with adjacent Se anions. In the static state, the amino group forms three fixed-angle positions, each separated by 120°. Our findings contribute to the understanding of electrical phenomena in organic-inorganic hybrid materials and may be used for the design of future molecule-based electronic materials.

Survey of Applicable Methods for Determining Viscoelastic Effects in Ferroelectric and Antiferroelectric Chiral Liquid Crystals.

Viscosity, elasticity, and viscoelastic properties are one of the most fundamental properties of liquid crystalline materials; the main problem in determining these properties is the multitude of physical parameters needed to determine the values of elasticity and viscosity constants. In this paper, a number of different measurement methods for the complete characterization of viscoelastic properties for smectic liquid crystalline materials and their mixtures are analyzed, both theoretically and experimentally. The way in which viscoelastic material constants are determined depends mainly on the application/purpose of the materials under study. The subject of this work was to review the methods used to determine viscoelastic effects in ferroelectric and antiferroelectric chiral liquid crystals, their mixtures, composite materials, and even in dielectric systems, which would bear the hallmark of a universal method allowing the application of sufficiently low electric fields. In the case of chiral liquid crystals with ferroelectric and antiferroelectric phases and their subphases, the following assumption applies: fulfilment of Hooke's law (in the case of elastic coefficients) and preservation of laminar flow (in the case of viscosity coefficients).

(Invited) Computational Framework and Insights Towards Electrolyte Design for Redox Flow Batteries

Achieving a fully renewable energy future requires the development of grid-scale energy storage technologies, and non-aqueous redox flow batteries (NRFBs) hold considerable potential.1 However, the redox-active materials (RAMs) currently in consideration as lead candidates for NRFBs exhibit shortcomings, including low solubility, impaired transport properties, and low reduction potentials. Utilizing theory and computation is invaluable in predicting the structure-property relationship of RAM as NRFB active materials. The numerous components within NRFB electrolytes, spanning solvents, active materials, and supporting salts, suggest that material discovery could experience a substantial acceleration through computational and machine-learning approaches.This presentation will provide a progress update on our efforts towards a computational framework involving quantum mechanical (QM), molecular dynamics (MD), and machine learning methods to predict fundamental chemical and physicochemical properties of redox-active materials for energy storage systems. We used an extremely stable, bio-inspired vanadium(4+) bis-hydroxyiminodiacetic acid (VBH) as the scaffold to explore the large parameter space of NRFB systems.2–4 First, we show our framework based on density functional theory (DFT) for calculating solubilities, incorporating both lattice and solvation energies, that can be calibrated and used with synthetic verifications. We found that lattice free energy, which has previously been neglected, has a significant role in tuning electrolyte solubility. Second, we describe our MD simulations on the concentration and composition dependence of transport properties, such as viscosity, using DFT-derived force field bonded parameters for the novel [VBH]2- anion to understand the interactions between the ions and the solvent. Third, we demonstrate that QM molecular design could finely adjust the reduction potential. These fundamental investigations would offer insights into electrolyte design and how molecular properties dictate the macroscopic properties that contribute to specific performance aspects of advanced battery electrolytes.1 Y. Huang, S. Gu, Y. Yan and S. F. Y. Li, Current Opinion in Chemical Engineering, 2015, 8, 105–113.2 H. Huang, R. Howland, E. Agar, M. Nourani, J. A. Golen and P. J. Cappillino, J. Mater. Chem. A, 2017, 5, 11586–11591.3 S. K. Pahari, T. C. Gokoglan, B. R. B. Visayas, J. Woehl, J. A. Golen, R. Howland, M. L. Mayes, E. Agar and P. J. Cappillino, RSC Adv., 2021, 11, 5432–5443.4 B. R. B. Visayas, S. K. Pahari, T. C. Gokoglan, J. A. Golen, E. Agar, P. J. Cappillino and M. L. Mayes, Chem. Sci., 2021, 12, 15892–15907.

Accurate Determination of the Uniaxial Complex Refractive Index and the Optical Band Gap of Polymer Thin Films to Correlate Their Absorption Strength and Onset of Absorption.

The advanced development of optoelectronic devices requires a methodical knowledge of the fundamental material properties of the key active components. Systematic investigations and correlations of such basic optical properties can lead to new insights for the design of more potent materials. In this perspective, we provide a systematic overview of the uniaxial anisotropic complex refractive indices and the absorption coefficients obtained by ellipsometry as well as the optical band gap energies derived from Tauc plots of six selected solution-processed polymer thin films. While the optical band gap energies are intentionally distributed over the visible spectral range, we found that the absorption strength of all polymer samples are grouped in a random distribution within a rather uniform range of values.

Infrared photometry and spectrometry of satellites and debris at UKIRT

The characterization of deep space debris poses a significant challenge in Space Domain Awareness (SDA). Multi-color photometry and the resultant color indices offer the potential to rapidly discriminate between debris and intact space objects such as rocket bodies and satellites. These multi-color techniques can also identify anomalous members of objects in certain groups and cue higher fidelity data collections and studies. However, multi-color photometry can be difficult to interpret, as the effects of phase and rotation become conflated with the more fundamental material properties of the satellite. Additionally, the broad astronomical photometric bands may not identify key spectral features that can be diagnostic for SDA applications. With our recent observational campaign we have been able to collect 5-color photometry in the near-IR with WFCAM as well as overlapping near-IR spectra with the UKIRT 1–5 μm Imager-Spectrometer (UIST). On a small set of objects, we also have mid-IR spectrophotometry with the Mid-IR esCHELLE (Michelle) imaging spectrograph.Our previous measurements with the United Kingdom Infrared Telescope (UKIRT) Wide Field Camera (WFCAM) characterized a wide range of space objects with the goal of developing techniques to rapidly discriminate between different classes of objects and to identify anomalous members of these groups. The survey has produced a comprehensive database of 5-color photometry in the Z, Y, J, H, and K bands, analogous to the bands that are anticipated to be exploited by future U.S. ground-based SDA systems. Our current data set includes:(a)United States Centaur rocket bodies (RBs),(b)Molniya communication satellites including the −1 K, 1T, −2, and −3 variants,(c)Russian FREGAT and SL-6 upper stage RBs in Molniya orbits,(d)Russian Breeze-M rocket bodies disposed of in GEO-crossing graveyard like orbits, and the Angara-5/Breeze-M mass simulator, also disposed of in a near-GEO orbit,(e)Intact payloads selected from satellites using the Boeing HS-376 buses—including four different generations of solar panel technology.Many of the objects we have studied have significant orbital inclination or drift in the GEO belt. The interpretation of photometry of these objects is especially difficult as phase angle can no longer be considered simply as a single-dimensional quantity. During 2023, we have endeavored to expand our phase angle coverage of a handful of objects and comprehensively sample brightness and color in both components of phase angle. In this paper we present exemplary “phase-phase” diagrams demonstrating this technique and highlight some of the practical and observational difficulties in achieving comprehensive phase angle coverage and interpreting these results.

Heavy metals immobilization of LDH@biochar-containing cementitious materials: Effectiveness and mechanisms

The utilization of tailings for producing solid waste-based concrete can effectively mitigate the environmental consequences stemming from tailings accumulation. However, the presence of high concentrations of heavy metal ions in certain tailings can deleteriously affect the microstructure of the cement matrix. This study investigated the in-situ immobilization performance of Pb2+ and Mn2+ by calcium-aluminum layered double hydroxides (CaAl-LDH) and LDH@biochar blended in cement-based materials. The equilibrium adsorption capacity of CaAl-LDH/LDH@biochar for heavy metals in aqueous solution was assessed by adsorption kinetics, while the effect of pH value on the removal rate of heavy metals was also studied. The workability and mechanical properties of cement mortar are significantly improved with the addition of 4 % LDH@biochar. LDHs@biochar can effectively reduce the deteriorating effects of heavy metal ions on the fundamental properties of cement materials. Compared with CaAl-LDH, the leaching concentrations for Pb2+ and Mn2+ in LDH@biochar at 28 d is decreased by 59 % and 39 %, respectively. The stable adsorption of Pb2+ and Mn2+ by LDH@biochar is attributed to multiple mechanisms, including interlayer anion exchange effect, chemical adsorption of surface functional groups and electrostatic attraction. This study provide valuable insights for enhancing the efficiency of heavy metal immobilization in cement-based materials.

Multiscale evaluation of asphalt binder-aggregate interface exposed to sodium chloride deicer

ABSTRACT Sodium chloride (NaCl) is widely used in roadways’ winter maintenance operations. Despite its effectiveness, NaCl solution is known to accelerate the moisture-induced damage process in asphalt mixes. This study aimed to identify the mechanisms involved in the deterioration of adhesive and cohesive bonds in asphalt and aggregate interfaces due to the NaCl deicer and freeze–thaw (F-T) action. A multiscale material characterisation approach and evaluation of fundamental intrinsic material properties were applied to achieve this goal. Adhesive properties of aggregate-binder systems subjected to aqueous NaCl concentrations and F-T cycles were characterised by conducting binder bond strength tests. The surface free energy technique was also employed to evaluate the impact of salt concentration and moisture on the different energy parameters and moisture-induced damage potential. Furthermore, the influence of salt concentration and F-T cycles on the surface nanostructure and adhesion of the asphalt binder was evaluated using atomic force microscopy. The multiscale characterisation approach presented in this study provided significant insights into the physio-chemical mechanisms involved in the deterioration of the bonds in aggregate-binder systems exposed to different aqueous NaCl concentrations. A fundamental understanding of the damage mechanisms is necessary to develop counteracting measures that extend the life of asphalt pavements in cold climates.

Electronic Properties of Group-III Nitride Semiconductors and Device Structures Probed by THz Optical Hall Effect.

Group-III nitrides have transformed solid-state lighting and are strategically positioned to revolutionize high-power and high-frequency electronics. To drive this development forward, a deep understanding of fundamental material properties, such as charge carrier behavior, is essential and can also unveil new and unforeseen applications. This underscores the necessity for novel characterization tools to study group-III nitride materials and devices. The optical Hall effect (OHE) emerges as a contactless method for exploring the transport and electronic properties of semiconductor materials, simultaneously offering insights into their dielectric function. This non-destructive technique employs spectroscopic ellipsometry at long wavelengths in the presence of a magnetic field and provides quantitative information on the charge carrier density, sign, mobility, and effective mass of individual layers in multilayer structures and bulk materials. In this paper, we explore the use of terahertz (THz) OHE to study the charge carrier properties in group-III nitride heterostructures and bulk material. Examples include graded AlGaN channel high-electron-mobility transistor (HEMT) structures for high-linearity devices, highlighting the different grading profiles and their impact on the two-dimensional electron gas (2DEG) properties. Next, we demonstrate the sensitivity of the THz OHE to distinguish the 2DEG anisotropic mobility parameters in N-polar GaN/AlGaN HEMTs and show that this anisotropy is induced by the step-like surface morphology. Finally, we present the temperature-dependent results on the charge carrier properties of 2DEG and bulk electrons in GaN with a focus on the effective mass parameter and review the effective mass parameters reported in the literature. These studies showcase the capabilities of the THz OHE for advancing the understanding and development of group-III materials and devices.

Fostering sustainability: The environmental advantages of natural fiber composite materials – a mini review

In recent decades, natural fiber reinforced composites (NFRCs) have become an attractive substitute for conventional materials such as glass fiber and have attracted considerable interest from researchers and academics, particularly in the context of environmental protection. Environmental factors and their impact on the fundamental properties of renewable materials are becoming an increasingly popular area of study, particularly natural fibers and their composites. While this area of research is still expanding, natural fiber-reinforced polymer composites (NFRCs) have found widespread use in a variety of engineering contexts. Natural fibers (NFs) such as pineapple leaf (PALF), bamboo, abaca, coconut fibers, jute, banana, flax, hemp, sisal, kenaf, and others have many desirable properties, but their development and use present researchers with a number of obstacles. These fibers have attracted attention due to their various advantageous properties, such as lightness, economy, biodegradability, remarkable specific strength, and competitive mechanical properties, which make them promising candidates for use as biomaterials. As a result, they can serve as alternative materials to traditional composite fibers such as glass, aramid, and carbon in various applications. In addition, natural fibers have attracted the interest of an increasing number of researchers because they are readily available in nature and as by-products of agricultural and food systems, contributing to the improvement of the environmental ecosystem. This interest coincides with the search for environmentally friendly materials to replace synthetic fibers used in the construction, automotive, and packaging industries. The use of natural fibers is not only logical but also practical, as their fibrous form can be easily extracted and strengthened by chemical, physical, or enzymatic treatments. This article provides a brief overview of NFRCs, looking at their chemical, physical, and mechanical properties. It also highlights some of the significant advances associated with NFRCs from an economic, environmental, and sustainability perspective. Additionally, it provides a concise discussion of their diverse applications, all with a focus on their positive impact on the environment.

Advancements in polyol synthesis: expanding chemical horizons and Néel temperature tuning of CoO nanoparticles

The polyol synthesis of CoO nanoparticles (NPs) is typically conducted by dissolving and heating cobalt acetate tetrahydrate and water in diethylene glycol (DEG). This process yields aggregates of approximately 100 nm made of partially aligned primary crystals. However, the synthesis demands careful temperature control to allow the nucleation of CoO while simultaneously preventing reduction, caused by the activity of DEG. This restriction hinders the flexibility to freely adjust synthesis conditions, impeding the ability to obtain particles with varied morpho-structural properties, which, in turn, directly impact chemical and physical attributes. In this context, the growth of CoO NPs in polyol was studied focusing on the effect of the polyol chain length and the synthesis temperature at two different water/cations ratios. During this investigation, we found that longer polyol chains remove the previous limits of the method, allowing the tuning of aggregate size (20–150 nm), shape (spherical-octahedral), and crystalline length (8–35 nm). Regarding the characterization, our focus revolved around investigating the magnetic properties inherent in the synthesized products. From this point of view, two pivotal findings emerged. Firstly, we identified small quantities of a layered hydroxide ferromagnetic intermediate, which acted as interference in our measurements. This intermediate exhibited magnetic properties consistent with features observed in other publications on CoO produced in systems compatible with the intermediate formation. Optimal synthetic conditions that prevent the impurity from forming were found. This resolution clarifies several ambiguities existing in literature about CoO low-temperature magnetic behavior. Secondly, a regular relationship of the NPs' TN with their crystallite size was found, allowing us to regulate TN over ~ 80 K. For the first time, a branching was found in this structure-dependent magnetic feature, with samples of spheroidal morphology consistently having lower magnetic temperatures, when compared to samples with faceted/octahedral shape, providing compelling evidence for a novel physical parameter influencing the TN of a material. These two findings contribute to the understanding of the fundamental properties of CoO and antiferromagnetic materials.

Effect of particle size on optical and electrical properties of thin films – a simulation approach

This paper presents a simulated investigation of the relationship between surface roughness and particle size as well as the resulting impact of the both on the thin film’s physical and optical properties. Non-porous thin films are created through computer simulation for a range of particle sizes following the random deposition with a surface relaxation approach. For the deposited thin films different fundamental parameters like transmittance, reflectance, absorption coefficients, frequency dependent dielectric constants, loss angle, refractive indices, optical band gap have all been calculated and in this process of calculation the fundamental values of two different materials; zinc oxide (ZnO and cadmium selenide (CdSe) are taken. It has been seen that when the particle sizes are comparable the calculated values of optical band gap of both ZnO and CdSe matches well with the experimental values of the same reported by others. This is probably the first effort to develop a direct relation between the particle size with different fundamental optical properties of different materials through simple simulation which agrees well with the experimental result. This would definitely help the experimental researchers in the field of material science to design their experiments as per the desired requirements.

Recent Advanced Ultra‐Wide Bandgap β‐Ga2O3 Material and Device Technologies

AbstractGallium oxide (Ga2O3) is an emerging ultra‐wide bandgap (UWBG) semiconductor material that has gained significant attention in the field of high voltage and high frequency power electronics. Its noteworthy attributes include a large bandgap (Eg) of 4.8 eV, high theoretical critical breakdown field strength (EC) of 8 MV cm−1, and saturation velocity (νs) of 2 × 107 cm s−1, as well as high Baliga figures of merit (BFOM) of 3000. In addition, Ga2O3 has the advantages of large‐size substrates that can be achieved by low‐cost melt‐grown techniques. This review provides a partial overview of pivotal milestones and recent advancements in the Ga2O3 material growth and device performance. It begins with a discussion of the fundamental material properties of Ga2O3, followed by a description of substrate growth and epitaxial techniques for Ga2O3. Subsequently, the contact technologies between Ga2O3 and other materials are fully elucidated. Moreover, this article also culminates with a detailed analysis of Ga2O3‐based high voltage and high frequency power devices. Some challenges and solutions, such as the lack of p‐type doping, low thermal conductivity, and low mobility are also presented and investigated in this review.

Controlled Self-Assembly of Nanoscale Superstructures in Phase-Change Ge-Sb-Te Nanowires.

Controlled growth of semiconductor nanowires with atomic precision offers the potential to tune the material properties for integration into scalable functional devices. Despite significant progress in understanding the nanowire growth mechanism, definitive control over atomic positions of its constituents, structure, and morphology via self-assembly remains challenging. Here, we demonstrate an exquisite control over synthesis of cation-ordered nanoscale superstructures in Ge-Sb-Te nanowires with the ability to deterministically vary the nanowire growth direction, crystal facets, and periodicity of cation ordering by tuning the relative precursor flux during synthesis. Furthermore, the role of anisotropy on material properties in cation-ordered nanowire superstructures is illustrated by fabricating phase-change memory (PCM) devices, which show significantly different growth direction dependent amorphization current density. This level of control in synthesizing chemically ordered nanoscale superstructures holds potential to precisely modulate fundamental material properties such as the electronic and thermal transport, which may have implications for PCM, thermoelectrics, and other nanoelectronic devices.

Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades

Leading edge erosion of wind turbine blades is a major contributor to wind farm energy yield losses and maintenance costs. Presented is a multidisciplinary framework for predicting rain erosion lifetimes of wind turbine blades. Key aim is assessing the sensitivity of lifetime predictions to: modeling aspects (material erosion model, blade aerodynamics), input data and/or their preprocessing (joint frequency distribution of wind speed and droplet size based on synchronous site-specific measurements versus frequency distribution generated with partly site-agnostic modeling standards, wind speed records of nacelle anemometer or extrapolated at hub height from met masts), and environmental conditions (UV weathering). The analyses consider a Northwest England onshore site where a utility-scale turbine is operational, focus on a reference 5 MW turbine assumed operational at the site, and use a typical leading edge coating material. It is found that the largest variations in erosion lifetime predictions are due to material erosion model (based on rain erosion test data or fundamental material properties) and wind and rain model (measurement-based joint wind speed and droplet size distribution or standard-based modeled distribution). The use of joint wind and rain distribution also enables identifying wind/rain states with highest erosion potential, knowledge paramount to deploying erosion-safe turbine control.

Observation of negative surface and interface energies of quantum dots

Surface energy is a fundamental property of materials and is particularly important in describing nanomaterials where atoms or molecules at the surface constitute a large fraction of the material. Traditionally, surface energy is considered to be a positive quantity, where atoms or molecules at the surface are less thermodynamically stable than their counterparts in the interior of the material because they have fewer bonds or interactions at the surface. Using calorimetric methods, we show that the surface energy is negative in some prototypical colloidal semiconductor nanocrystals, or quantum dots with organic ligand coatings. This implies that the surface atoms are more thermodynamically stable than those on the interior due to the strong bonds between these atoms and surfactant molecules, or ligands, that coat their surface. In addition, we extend this work to core/shell indium phosphide/zinc sulfide nanocrystals and show that the interfacial energy between these materials is highly thermodynamically favorable in spite of their large lattice mismatch. This work challenges many of the assumptions that have guided thinking about colloidal nanomaterial thermodynamics, investigates the fundamental stability of many technologically relevant colloidal nanomaterials, and paves the way for future experimental and theoretical work on nanocrystal thermodynamics.