Crystallographic and Photophysical Analysis on Facet-Controlled Defect-Free Blue-Emitting Quantum Dots.

The burgeoning demand for commercializing self-luminescing quantum dot (QD) light-emitting diodes (LEDs) has stimulated extensive research into environmentally friendly and efficient QD materials. Hydrofluoric acid (HF) additive improves photoluminescence (PL) properties of blue-emitting ZnSeTe QDs, ultimately reaching a remarkable quantum yield (QY) of 97% with an ultranarrow peak width of 14 nm after sufficient HF addition. The improvement in optical properties of the QDs is accompanied by a morphology change of the particles, forming cubic-shaped defect-free ZnSeTe QDs characterized by a zinc blende (ZB) crystal structure. This treatment improves the QD-emitting properties by facilitating facet-specific growth, selectively exposing stabilized (100) facets, and reducing the lattice disorders. The facet-specific growth process gives rise to defect-free monodispersed cubic dots that exhibit remarkably narrow and homogeneous PL spectra. Meticulous time-resolved spectroscopic studies allow an understanding of the correlation between ZnSeTe QDs' particle shape and performance following HF addition. These investigations shed light on the intricacies of the growth mechanism and the factors influencing the PL efficiency of the resulting QDs. The findings significantly contribute to understanding the role of HF treatment in tailoring the optical properties of ZnSeTe QDs, thereby bringing it closer to the realization of highly efficient and bright QD-LEDs for various practical applications.

Atomistic insights into adhesion characteristics of tungsten on titanium nitride using steered molecular dynamics with machine learning interatomic potential.

As transistor integration accelerates and miniaturization progresses, improving the interfacial adhesion characteristics of complex metal interconnect has become a major issue in ensuring semiconductor device reliability. Therefore, it is becoming increasingly important to interpret the adhesive properties of metal interconnects at the atomic level, predict their adhesive strength and failure mode, and develop computational methods that can be universally applied regardless of interface properties. In this study, we propose a method for theoretically understanding adhesion characteristics through steering molecular dynamics simulations based on machine learning interatomic potentials. We utilized this method to investigate the adhesion characteristics of tungsten deposited on titanium nitride barrier metal (W/TiN) as a representative metal interconnect structure in devices. Pulling tests that pull two materials apart and sliding tests that pull them against each other in a shear direction were implemented to investigate the failure mode and adhesive strength depending on TiN facet orientation. We found that the W/TiN interface showed an adhesive failure where they separate from each other when tested with pulling force on Ti-rich (111) or (001) facets while cohesive failures occurred where W itself was destroyed on N-rich (111) facet. The adhesion strength was defined as the maximum force causing failure during the pulling test for consistent interpretation and the strengths of tungsten were predicted to be strongest when deposited onto N-rich (111) facet while weakest on Ti-rich (111) facet.

Constructing surface models of silicate glasses using molecular dynamics to understand the effect of pH on the hydration properties.

In this work, we use realistic silicate glass surface models, with molecular dynamics simulations, and present an algorithm for proper atomic partial charge assignment, consistent with measurable internal dipoles. The immersion energy is calculated for different silicate glass compositions in solutions of varying pH. We use molecular dynamics to elucidate the differences in the structure of water between mono- and divalent cations. The immersion energy of the glass surface is found to increase with an increase in ionic surface density and pH. This can be attributed to the stronger interaction between water and cations, as opposed to the interactions between water and silanol groups. The developed models and methods provide new insights into the structure of glass-solution interfaces and the effect of cation surface density in common nanoscale environments.

Characterization of Mechanical Degradation in Perfluoropolyether Film for Its Application to Antifingerprint Coatings.

Enhancing the mechanical durability of antifingerprint films is critical for its industrial application on touch-screen devices to withstand friction damage from repeated rubbing in daily usage. Using reactive molecular dynamics simulations, we herein implement adhesion, mechanical, and deposition tests to investigate two durability-determining factors: intrachain and interchain strength, which affect the structural stability of the antifingerprint film (perfluoropolyether) on silica. From the intrachain perspective, it is found that the Si-C bond in the polymer chain is the weakest, and therefore prone to dissociation and potentially forming a C-O bond. This behavior is demonstrated consistently, regardless of the cross-linking density between polymer chains. For the interchain interaction, increasing the chain length enhances the mechanical properties of the film. Furthermore, the chain deposition test, mimicking the experimental coating process, demonstrates that placing shorter chains first to the surface of silica and then depositing longer chains is an ideal way to improve the interchain interaction in the film structure. The current study reveals a clear pathway to optimize the configuration of the polymer chain as well as its film structure to prolong the product life of the coated antifingerprint film.

Mechanistic understanding of intergranular cracking in NCM cathode material: mesoscale simulation with three-dimensional microstructure.

Intergranular cracking in the agglomerated form of secondary particles has been regarded as a major cause for mechanical degradation in layered oxide cathode materials for Li-ion batteries, but its detailed mechanistic origin linked to the mechanical properties of these materials is still unknown. In this study, a mesoscale simulation based on the description of the interaction between primary particles is established by combining the model of the shifted-force Lennard-Jones potential and granular Hertzian model to construct the microstructure of secondary particles of cathode materials. The optimized parameters for each model are developed to compute the mechanical properties based on the response from nano-indentation and uniaxial tensile tests. Furthermore, the adhesion between the primary particles is modified to examine their sensitivity to different modes of deformations. The results show that under tension, an increase in adhesion can significantly strengthen the structure along with increase in brittleness, whereas the response from the localized compression (nano-indentation) is shown to be much less sensitive. In addition, the structural changes during repeated volume expansion/contraction induced from electrochemical cycling are investigated. The results indicate that enhancing particle adhesion can prevent the propagation of intergranular cracking.

Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materials.

Optimizing synthesis parameters is the key to successfully design ideal Ni-rich cathode materials that satisfy principal electrochemical specifications. We herein implement machine learning algorithms using 330 experimental datasets, obtained from a controlled environment for reliability, to construct a predictive model. First, correlation values showed that the calcination temperature and the size of the particles are determining factors for achieving a long cycle life. Then, we compared the accuracy of seven different machine learning algorithms for predicting the initial capacity, capacity retention rate, and amount of residual Li. Remarkable predictive capability was obtained with the average value of coefficient of determinant, R2 = 0.833, from the extremely randomized tree with adaptive boosting algorithm. Furthermore, we propose a reverse engineering framework to search for experimental parameters that satisfy the target electrochemical specification. The proposed results were validated by experiments. The current results demonstrate that machine learning has great potential to accelerate the optimization process for the commercialization of cathode materials.

High-Performance and Industrially Feasible Ni-Rich Layered Cathode Materials by Integrating Coherent Interphase.

For developing the industrially feasible Ni-rich layered oxide cathode with extended cycle life, it is necessary to mitigate both the mechanical degradation due to intergranular cracking between primary particles and gas generation from the reaction between the electrolyte and residual Li in the cathode. To simultaneously resolve these two issues, we herein propose a simple but novel method to reinforce the primary particles in LiNi0.91Co0.06Mn0.03O2 by providing a Li-reactive material, whose spinel interphase is coherent with the surface of the cathode. The modified structure significantly outperforms analogous bare samples: they conserve more than 90% of the initial capacity after 50 cycles and also exhibit a greater rate capability. By tracking the same particle location during cycling, we confirmed that the current method significantly reduces crack generation because the provided coating material can penetrate inside the grain boundary of the secondary particle and help maintain the volume of the primary particle. Finally, first-principles calculations were implemented to determine the role of this spinel material, i.e., having intrinsically isotropic lattice parameters, superior mechanical properties, and only a small volume change during delithiation. We believe that the proposed method is straightforward and cost-effective; hence, it is directly applicable for the mass production of Ni-rich cathode material to enable its commercialization.

Intrinsic origin of intra-granular cracking in Ni-rich layered oxide cathode materials.

Mechanical degradation phenomena in layered oxide cathode materials during electrochemical cycling have limited their long-term usage because they deteriorate the structural stability and result in a poor capacity retention rate. Among them, intra-granular cracking inside primary particles progressively degrades the performance of the cathode but comprehensive understanding of its intrinsic origin is still lacking. In this study, the mechanical properties of the primary particle in a Ni-rich layered oxide cathode material (LiNi0.8Co0.1Mn0.1O2) are investigated under tensile and compressive deformation towards both in-plane and out-of-plane directions within the density functional theory framework. The Young's modulus and maximum strength values indicate that the pristine structure is more vulnerable to tensile deformation than compression. In addition, delithiation significantly deteriorates the mechanical properties regardless of the direction of deformation. In particular, a substantial degree of anisotropy is observed, indicating that the mechanical properties in the out-of-plane direction are much weaker than those in the in-plane direction. Particular weakness in that direction is further confirmed using heterogeneously delithiated structures as well as by calculating the accumulated mechanical stress values inside during delithiation. A comparison of the mechanical properties of the structure with a lower Ni content (Ni = 33%) demonstrates that the Ni-rich material is slightly weaker and hence its intra-granular cracking could become accelerated during cycling.

Computational approaches for investigating interfacial adhesion phenomena of polyimide on silica glass.

This manuscript provides a comprehensive study of adhesion behavior and its governing mechanisms when polyimide undergoes various modes of detachment from silica glass. Within the framework of steered molecular dynamics, we develop three different adhesion measurement techniques: pulling, peeling, and sliding. Such computational methodologies can be applied to investigate heterogeneous materials with differing interfacial adhesion modes. Here, a novel hybrid potential involving a combination of the INTERFACE force field in conjunction with ReaxFF and including Coulombic and Lennard-Jones interactions is employed to study such interfaces. The studies indicate that the pulling test requires the largest force and the shortest distance to detachment as the interfacial area is separated instantaneously, while the peeling test is observed to exhibit the largest distance for detachment because it separates via line-by-line adhesion. Two kinds of polyimides, aromatic and aliphatic type, are considered to demonstrate the rigidity dependent adhesion properties. The aromatic polyimide, which is more rigid due to the stronger charge transfer complex between chains, requires a greater force but a smaller distance at detachment than the aliphatic polyimide for all of the three methodologies.

Theoretical Prediction of Surface Stability and Morphology of LiNiO2 Cathode for Li Ion Batteries.

Ni-rich layered oxides are considered to be a promising cathode material with high capacity, and their surface structure should be extensively explored to understand the complex associated phenomena. We investigated the surface stability and morphology of LiNiO2 as a representative of these materials by using density functional theory calculations. The results reveal that the Li-exposed surfaces have lower energies than the oxygen surfaces, irrespective of the facets, and the Ni-exposed ones are the least stable. The equilibrium morphology can vary from truncated trigonal bipyramid to truncated egg shape, according to the chemical potential, whose range is confined by the phase diagram. Moreover, the electrochemical window of stable facets is found to strongly depend on the surface elements rather than the facet directions. Contrary to the stable Li surfaces, oxygen exposure on the surface considerably lowers the Fermi level to the level of electrolyte, thereby accelerating oxidative decomposition of the electrolyte on the cathode surface.

Improved electrochemical properties of LiNi0.91Co0.06Mn0.03O2 cathode material via Li-reactive coating with metal phosphates.

Ni-rich layered oxides are promising cathode materials due to their high capacities. However, their synthesis process retains a large amount of Li residue on the surface, which is a main source of gas generation during operation of the battery. In this study, combined with simulation and experiment, we propose the optimal metal phosphate coating materials for removing residual Li from the surface of the Ni-rich layered oxide cathode material LiNi0.91Co0.06Mn0.03O2. First-principles-based screening process for 16 metal phosphates is performed to identify an ideal coating material that is highly reactive to Li2O. By constructing the phase diagram, we obtain the equilibrium phases from the reaction of coating materials and Li2O, based on a database using a DFT hybrid functional. Experimental verification for this approach is accomplished with Mn3(PO4)2, Co3(PO4)2, Fe3(PO4)2, and TiPO4. The Li-removing capabilities of these materials are comparable to the calculated results. In addition, electrochemical performances up to 50 charge/discharge cycles show that Mn-, Co-, Fe-phosphate materials are superior to an uncoated sample in terms of preventing capacity fading behavior, while TiPO4 shows poor initial capacity and rapid reduction of capacity during cycling. Finally, Li-containing equilibrium phases examined from XRD analysis are in agreement with the simulation results.

Computational Screening for Design of Optimal Coating Materials to Suppress Gas Evolution in Li-Ion Battery Cathodes.

Ni-rich layered oxides are attractive materials owing to their potentially high capacity for cathode applications. However, when used as cathodes in Li-ion batteries, they contain a large amount of Li residues, which degrade the electrochemical properties because they are the source of gas generation inside the battery. Here, we propose a computational approach to designing optimal coating materials that prevent gas evolution by removing residual Li from the surface of the battery cathode. To discover promising coating materials, the reactions of 16 metal phosphates (MPs) and 45 metal oxides (MOs) with the Li residues, LiOH, and Li2CO3 are examined within a thermodynamic framework. A materials database is constructed according to density functional theory using a hybrid functional, and the reaction products are obtained according to the phases in thermodynamic equilibrium in the phase diagram. In addition, the gravimetric efficiency is calculated to identify coating materials that can eliminate Li residues with a minimal weight of the coating material. Overall, more MP and MO materials react with LiOH than with Li2CO3. Specifically, MPs exhibit better reactivity to both Li residues, whereas MOs react more with LiOH. The reaction products, such as Li-containing phosphates or oxides, are also obtained to identify the phases on the surface of a cathode after coating. On the basis of the Pareto-front analysis, P2O5 could be an optimal material for the reaction with both Li residuals. Finally, the reactivity of the coating materials containing 3d/4d transition metal elements is better than that of materials containing other types of elements.

A first-principles study of the preventive effects of Al and Mg doping on the degradation in LiNi0.8Co0.1Mn0.1O2 cathode materials.

First-principles calculations have been used to investigate the effects of Al and Mg doping on the prevention of degradation phenomena in Li(Ni0.8Co0.1Mn0.1)O2 cathode materials. Specifically, we have examined the effects of dopants on the suppression of oxygen evolution and cation disordering, as well as their correlation. It is found that Al doping can suppress the formation of oxygen vacancies effectively, while Mg doping prevents the cation disordering behaviors, i.e., excess Ni and Li/Ni exchange, and Ni migration. This study also demonstrates that formation of oxygen vacancies can facilitate the construction of the cation disordering, and vice versa. Delithiation can increase the probabilities of formation of all defect types, especially oxygen vacancies. When oxygen vacancies are present, Ni can migrate to the Li site during delithiation. However, Al and Mg doping can inhibit Ni migration, even in structures with preformed oxygen defects. The analysis of atomic charge variations during delithiation demonstrates that the degree of oxidation behavior in oxygen atoms is alleviated in the case of Al doping, indicating the enhanced oxygen stability in this structure. In addition, changes in the lattice parameters during delithiation are suppressed in the Mg-doped structure, which suggests that Mg doping may improve the lattice stability.

Dynamics and mechanism of flame retardants in polymer matrixes: experiment and simulation.

We investigate the dynamics and the mechanism of flame retardants in polycarbonate matrixes to explore for a way of designing efficient and environment-friendly flame retardants. The high phosphorus content of organic phosphates has been considered as a requirement for efficient flame retardants. We show, however, that one can enhance the efficiency of flame retardants even with a relatively low phosphorus content by tuning the dynamics and the intermolecular interactions of flame retardants. This would enable one to design bulkier flame retardants that should be less volatile and less harmful in indoor environments. UL94 flammability tests indicate that even though the phosphorus content of 2,4-di-tert-butylphenyl diphenyl phosphate (DDP) is much smaller with two bulky tertiary butyl groups than that of triphenyl phosphate (TPP), DDP should be as efficient of a flame retardant as TPP, which is a widely used flame retardant. On the other hand, the 2-tert-butylphenyl diphenyl phosphate (2-tBuDP), with a lower phosphorus content than TPP but with a greater phosphorus content than DDP, is less efficient as a flame retardant than both DDP and TPP. Dynamic secondary ion mass spectrometry and molecular dynamics simulations reveal that the diffusion of DDP is slower by an order of magnitude at low temperature than that of TPP but becomes comparable to that of TPP at the ignition temperature. This implies that DDP should be much less volatile than TPP at low temperature, which is confirmed by thermogravimetric analysis. We also find from Fourier transform infrared spectroscopy that Fries rearrangement and char formation are suppressed more by DDP than by TPP. The low volatility and the suppressed char formation of DDP suggest that the enhanced flame retardancy of DDP should be attributed to its slow diffusivity at room temperature and yet sufficiently high diffusivity at high temperature.

Modeling on the size dependent properties of InP quantum dots: a hybrid functional study.

Theoretical calculations based on density functional theory were performed to provide better understanding of the size dependent electronic properties of InP quantum dots (QDs). Using a hybrid functional approach, we suggest a reliable analytical equation to describe the change of energy band gap as a function of size. Synthesizing colloidal InP QDs with 2-4 nm diameter and measuring their optical properties was also carried out. It was found that the theoretical band gaps showed a linear dependence on the inverse size of QDs and gave energy band gaps almost identical to the experimental values.

Peierls distortion as a route to high thermoelectric performance in In(4)Se(3-delta) crystals.

Thermoelectric energy harvesting-the transformation of waste heat into useful electricity-is of great interest for energy sustainability. The main obstacle is the low thermoelectric efficiency of materials for converting heat to electricity, quantified by the thermoelectric figure of merit, ZT. The best available n-type materials for use in mid-temperature (500-900 K) thermoelectric generators have a relatively low ZT of 1 or less, and so there is much interest in finding avenues for increasing this figure of merit. Here we report a binary crystalline n-type material, In(4)Se(3-delta), which achieves the ZT value of 1.48 at 705 K-very high for a bulk material. Using high-resolution transmission electron microscopy, electron diffraction, and first-principles calculations, we demonstrate that this material supports a charge density wave instability which is responsible for the large anisotropy observed in the electric and thermal transport. The high ZT value is the result of the high Seebeck coefficient and the low thermal conductivity in the plane of the charge density wave. Our results suggest a new direction in the search for high-performance thermoelectric materials, exploiting intrinsic nanostructural bulk properties induced by charge density waves.

Nonadiabatic dynamics of charge transfer in diatomic anion clusters.

We have studied the photodissociation and recombination dynamics of the diatomic anions X(2)(-) and XY(-) designed to mimic I(2)(-) and ICl(-), respectively, by using a one-electron model in size-selected N(2)O clusters. The one-electron model is composed of two nuclei and an extra electron moving in a two-dimensional plane including the two nuclei. The main purpose of this study is to explain the salient features of various dynamical processes of molecular ions in clusters using a simple theoretical model. For heteronuclear diatomic anions, a mass disparity and asymmetric electron affinity between the X and Y atoms lead to different phenomena from the homonuclear case. The XY(-) anion shows efficient recombination for a smaller cluster size due to the effect of collision-mediated energy transfer and an inherent potential wall on excited state at asymptotic region, while the recombination for the X(2)(-) anion is due to rearrangement of solvent configuration and faster nonadiabatic transitions. The results of the present study illustrate the microscopic details of the electronically nonadiabatic processes which control the photodissociation dynamics of molecular ions in clusters.