Fundamentals and engineering of defects

Rational modulation of surface reconstruction in the oxygen evolution reaction (OER) utilizing defect engineering to form efficient catalytic activity centers is a topical interest in the field of catalysis. The introduction of point defects has been demonstrated to be an effective strategy to regulate the electronic configuration of electrocatalysts, but the influence of more complex planar defects (e.g., twins and stacking faults), on their intrinsic activity is still not fully understood. This study harnesses ultrasonic cavitation for rapid and controlled introduction of different types of defects in the FeCoNi/FeAl2O4 hybrid coating, optimizing OER catalytic activity. Theoretical calculations and experiments demonstrate that the different defects optimize the coordination environment and facilitate the activation of surface reconstruction into true catalytic activity centers at lower potentials. Moreover, it demonstrates exceptional durability, maintaining stable oxygen production at a high current density of 300 mA cm-2 for over 120 hours. This work not only presents a novel pathway for designing advanced electrocatalysts but also deepens our understanding of defect-engineered catalytic mechanisms, showcasing the potential for rapid and efficient enhancement of electrocatalytic performance.

Defect engineering in metal oxides is an effective approach for improving advanced oxidation processes. Herein, we report that regulating the defect types present on SnO2 enables deconvolution of their distinct effects on organic oxidation. Nitrogen annealing created E′δ center and non-bridging oxygen hole center (NBOHC) defects, while optimum hydrogenation introduced oxygen vacancies, significantly enhancing catalytic oxidation performance. Based on spectroscopic analysis, extended hydrogenation times passivated NBOHCs and formed new types of defects, such as electrons trapped in oxygen vacancies, which are less catalytically active in comparison with NBOHCs. DFT indicated that oxygen vacancies lower the energy barrier for oxygen activation as well as activation of the C-H bonds in formic acid, corroborating the experimental results of enhanced catalytic activity in samples with optimized defect concentrations. The current work advances understanding of the roles different defects play in enhancing organic oxidation in the ongoing search for efficient materials for oxidation reactions.

Iron oxides and oxyhydroxides play an important role in minimizing the mobility of redox-sensitive elements in engineered and natural environments. For the radionuclide technetium-99 (Tc), these phases hold promise as primary hosts for increasing Tc loading into glass waste form matrices, or as secondary sinks during the long-term storage of nuclear materials. Recent experiments show that the inverse spinel, magnetite [Fe(II)Fe(III)2O4], can incorporate Tc(IV) into its octahedral sublattice. In that same class of materials, trevorite [Ni(II)Fe(III)2O4] is also being investigated for its ability to host Tc(IV). However, questions remain regarding the most energetically favorable charge-compensation mechanism for Tc(IV) incorporation in each structure, which will affect Tc behavior under changing waste processing or storage conditions. Here, quantum-mechanical methods were used to evaluate incorporation energies and optimized lattice bonding environments for three different, charge-balanced Tc(IV) incorporation mechanisms in magnetite and trevorite (∼5 wt % Tc). For both phases, the removal of two octahedral Fe(II) or Ni(II) ions upon the addition of Tc(IV) in an octahedral site is the most stable mechanism, relative to the creation of octahedral Fe(III) defects or increasing octahedral Fe(II) content. Following hydration-energy corrections, Tc(IV) incorporation into magnetite is energetically favorable while an energy barrier exists for trevorite.

Semiconductor materials form the technological backbone of modern microelectronics and optoelectronics, enabling continuous advancements in computing, communication, sensing, and energy conversion. As device dimensions approach atomic scales and performance demands intensify, conventional silicon-based technologies face fundamental physical and material limitations. Consequently, current research has shifted toward the exploration of novel semiconductor materials, advanced fabrication techniques, and heterostructure engineering to sustain progress in device efficiency, speed, and integration density. Emerging materials such as wide-bandgap semiconductors, compound III-V materials, two-dimensional layered semiconductors, and perovskite-based systems have demonstrated significant potential for next-generation electronic and photonic applications. These materials offer superior electrical mobility, tunable bandgaps, enhanced thermal stability, and strong light-matter interactions, making them suitable for high-frequency transistors, power electronics, light-emitting devices, and photodetectors. In parallel, advancements in nanofabrication, epitaxial growth, and defect engineering have enabled precise control over material properties at the nanoscale, further expanding application possibilities. However, challenges related to material integration, scalability, interface stability, and long-term reliability continue to impede large-scale commercialization. Current research trends increasingly emphasize material compatibility with existing manufacturing infrastructure, sustainable processing methods, and performance optimization through computational modeling and machine learning approaches. This article provides a focused overview of recent research trends in semiconductor materials for microelectronics and optoelectronics, highlighting key material systems, technological drivers, and unresolved challenges. By synthesizing recent developments, the research aims to clarify the evolving research landscape and identify directions that may enable future breakthroughs in semiconductor device technologies and integrated optoelectronic systems across industrial and scientific domains.

Electrocatalytic CO2 reduction is a promising strategy for converting the greenhouse gas CO2 into high value-added products and achieving carbon neutrality. The rational design of electrocatalysts for CO2 reduction is of great significance. Defect chemistry is an important category for enhancing the intrinsic catalytic performance of electrocatalysts. Defect engineering breaks the catalytic inertia inherent in perfect structures by imparting unique electronic structures and physicochemical properties to electrocatalysts, thereby improving catalytic activity. Recently, various defective nanomaterials have been studied and show great potential in electrocatalytic CO2 reduction. There is an urgent need to gain insight into the effect of defects on catalytic performance. Here, we summarized the recent research advances on the design of various types of defects, including carbon-based materials (intrinsic defects, heteroatom doping and single-metal-atom sites) and metal compounds (vacancies, grain boundaries, and lattice defects). The major challenges and prospects of defect chemistry in electrocatalytic CO2 reduction are also proposed. This review is expected to be instructive in the development of defect engineering for CO2 reduction catalysts.

Defect engineered magnetism induction and electronic structure modulation in monolayer MoS2

The slow charge dynamics and large activation energy of CO2 severely hinder the efficiency of CO2 photoreduction. Defect engineering is a well-established strategy, while the function of common zero-dimensional defects is always restricted to promoting surface adsorption. In this work, a gradient layer of tungsten vacancies with a thickness of 3-4nm is created across Bi2 WO6 nanosheets. This gradient layerenables the formation of an inner-to-outer tandem homojunction with an internal electric field, which provides a strong driving force for the migration of photoelectrons from the bulk to the surface. Meanwhile, W vacancies change the coordination environment around O and W atoms, leading to an alteration in the basic sites and the mode of CO2 adsorption from weak/strong adsorption to moderate adsorption, which ultimately decreases the formation barrier of the key intermediate *COOH and facilitates the conversion thermodynamics for CO2 . Without any cocatalyst and sacrificial reagent, W-vacant Bi2 WO6 shows an outstanding photocatalytic CO2 reduction performance with a CO production rate of 30.62µmolg-1 h-1 , being one of the best catalysts in similar reaction systems. This study reveals that gradient vacancies as a new type of defect will show huge potential in regulating charge dynamics and catalytic reaction thermodynamics.

Defect engineering in the MOSLED structure by ion implantation

The development of high-performing cathode materials for aqueous zinc-ion batteries (ZIBs) is highly important for the future large-scale energy storage. Owing to the distinctive framework structure, diversity of valences, and high electrochemical activity, spinel materials have been widely investigated and used for aqueous ZIBs. However, the stubborn issues of low electrical conductivity and sluggish kinetics plague their smooth applications in aqueous ZIBs, which stimulates the development of effective strategies to address these issues. This review highlights the recent advances of spinel-based cathode materials that include the configuration of aqueous ZIBs and corresponding reaction mechanisms. Subsequently, the classifications of spinel materials and their properties are also discussed. Then, the review mainly summarizes the effective strategies for elevating their electrochemical performance, including their morphology and structure design, defect engineering, heteroatom doping, and coupling with a conductive support. In the final section, several sound prospects in this fervent field are also proposed for future research and applications.

The immense potential of colossal permittivity (CP) materials for use in modern microelectronics as well as for high-energy-density storage applications has propelled much recent research and development. Despite the discovery of several new classes of CP materials, the development of such materials with the required high performance is still a highly challenging task. Here, we propose a new electron-pinned, defect-dipole route to ideal CP behaviour, where hopping electrons are localized by designated lattice defect states to generate giant defect-dipoles and result in high-performance CP materials. We present a concrete example, (Nb+In) co-doped TiO₂ rutile, that exhibits a largely temperature- and frequency-independent colossal permittivity (> 10(4)) as well as a low dielectric loss (mostly < 0.05) over a very broad temperature range from 80 to 450 K. A systematic defect analysis coupled with density functional theory modelling suggests that 'triangular' In₂(3+)Vo(••)Ti(3+) and 'diamond' shaped Nb₂(5+)Ti(3+)A(Ti) (A = Ti(3+)/In(3+)/Ti(4+)) defect complexes are strongly correlated, giving rise to large defect-dipole clusters containing highly localized electrons that are together responsible for the excellent CP properties observed in co-doped TiO₂. This combined experimental and theoretical work opens up a promising feasible route to the systematic development of new high-performance CP materials via defect engineering.

Metal–organic frameworks (MOFs) represent a category of crystalline porous materials that have attracted considerable interest owing to their structural flexibility and capacity to undergo reversible transformations in response to external stimuli. This review examines the underlying mechanisms that facilitate these structural changes, the various types of stimuli capable of inducing them, and the wide-ranging applications that capitalize on these dynamic properties. The discussion elucidates that the flexibility inherent in MOFs derives from several factors, including the characteristics of the metal cluster, the coordination geometry, the rigidity or flexibility of the organic linkers, and the overall topology of the framework. It is emphasized that minor alterations in these molecular-level attributes can result in significant variations in the structural behavior of MOFs, encompassing phenomena such as breathing, gate opening, and both negative and positive thermal expansion. Moreover, the review explores the experimental and computational methodologies employed to investigate MOF flexibility, incorporating in situ characterization techniques and molecular modeling approaches. It highlights how the interplay between framework design, guest interactions, and defect engineering can be strategically utilized to develop stimuli-responsive MOFs with customized functionalities suitable for applications in areas such as gas separation, gas storage, and sensing. In conclusion, the review underscores the importance of comprehending and manipulating structural transformations in MOFs, a consideration that has emerged as a key element in contemporary materials science and engineering.

This thesis aims (i) to better understand near-field interactions in magnetic colloids, (ii) to study the particle distribution pattern effects on the magnetic response of magnetically labeled ceramic particles, and (iii) to apply these learnings to process ceramic-polymer composites with anisotropic micro-structures enabling a better understanding of fundamental structure-property relationships of fracture toughness in anisotropic composites. This work first focuses on predicting the magnetostatic interactions of super- paramagnetic particles in a transient simulation by applying machine learning to this field for the first time. Conventional predictive models for magnetostatic interactions include the widely applied and analytic dipole model, a matrix-based mutual dipole model and a numerically intensive but precise finite element model. Due to the accuracy and computational challenges of the near- field magnetic interaction with high susceptibility particles, and inspired by the success of machine learning approach in complex problems such as solid mechanics and hydrodynamics, we hypothesized that machine learning would deliver an accurate and fast predictive model that would enable large scale simulations. Simple neural network machine learning models showed limitations. To overcome this, we combined the fast computation advantage of dipole model (low fidelity data) with the enhanced accuracy of finite element modeling (high fidelity data). A multi-fidelity neural network model is built on this database and found to predict magnetostatic interactions in simple particle systems extremely well. However, this effort uncovered some limitations and concerns with the machine learning approach. The accuracy, benefits and limitations of this approach are discussed in this work. One of the motivations for developing a predictive machine learning code was to be able to model ceramic microparticles that are coated with tens of thousands of magnetic nanoparticles that have found many applications in composites, active optics, and micro- rheology. These labeled ceramics dramatically orient in applied magnetic fields with surprising speed. The discrete model developed in this thesis work is applied to model these massive multi-body systems and compared to previously proposed continuum analytic models that treat the labelled particles as magnetic ellipsoidal shells. In our discrete model, we employ the Monte Carlo method to find the average response of randomized assemblies of tens of thousands of magnetic nanoparticles adsorbed to the ceramic surface. The combined discrete model and Monte Carlo approach brings into the calculation the correct first principles of magnetostatic interactions between magnetic nanoparticles in the coating layer. This approach, therefore, provides a means for measuring the influence on the magnetic response of the platelet brought on the distribution pattern of the nanoparticles that can be experimentally tuned through controlled aggregation. These developed methodologies allow us to probe many of our hypotheses able these magnetic nanoparticle labelled ceramics including the root cause of their dramatic responses. The magnetic response of the labelled ceramic platelets can be applied to manipulate the micro-structure of ceramic-polymer composites to create fully aligned composites that exhibit toughness with sufficient enough control to establish new fundamental understandings in structure-property relationships in this field. The thesis turns attention to these systems and discusses processing, structure, and property paradigms within this class of materials. First, the process of manufacturing alumina reinforced acrylate polymer composites is discussed. Next, the experimental characterization of their mechanical properties is detailed. Finally, details about resultant fracture toughness properties are discussed including the crack kinking behavior in thin samples with anisotropic fracture energy. The effects of non-singular stress at the front of the crack tip associated with the stabilizing/destabilizing effect on the crack kinking will be discussed under the linear elastic fracture mechanics framework using plane stress assumptions. A phase-field simulation result carried out by collaborators will also be presented. Ultimately, we will show that the fracture energy anisotropy and the non-singular stress that can be modulated by the process zone size will work together to predict the existence of an anisotropic threshold for crack kinking and how this threshold is strongly dependent on load distribution.

There is good reason to believe that the properties of semiconducting metal oxides for catalytic applications can be improved when designed according to the principles of microelectronic devices. Nevertheless, defect engineering of polycrystalline TiO2 is complicated by native charged point defects and grain boundaries that alter bulk carrier concentration in a manner that depends on synthesis and post-treatment protocols. These influences are difficult to decouple with standard electrical characterization methods, which typically induce rectifying Schottky barriers to wide-bandgap semiconductors like TiO2. Here, TiO2 donor carrier concentration (Nd) is investigated as a function of film thickness and annealing time using a rigorous Schottky diode-based electrical characterization approach. Nd scales inversely with film thickness due to a reduction in the concentration of electrically active grain boundaries in the bulk. Annealing of polycrystalline TiO2 at 550 °C induces film densification that reduces the void volume surface area available for charge trapping. Strategies for defect engineering polycrystalline metal oxides must focus on tuning electrical activity of grain boundaries and intergranular voids for successful control and manipulation of Nd.

Advances in process and fabrication enable the relentless downscaling into the nanometer regime and open the way to a new era of device structures and materials. Approaching the atomic scale, individual imperfections such as point defects, which are inevitably present in bulk and interface systems, and their distributed properties start to become a decisive factor for device reliability and start to dominate the device performance. Keeping pace with the technological progress on the theoretical simulation side, hence, requires reliable approaches based on fundamental physics such as ab initio calculations. Largely driven by the beat of Moore’s law itself and the increase of computational resources, atomistic first-principles calculations are routinely used nowadays to predict structural and electronic properties. Nevertheless, understanding the underlying model together with a careful analysis of the results is essential to draw meaningful conclusions. Particularly in the field of device reliability, these advancements have enabled an improved understanding of intrinsic and extrinsic point defects in various materials such as Si/SiO2/HfO2 or SiC/SiO2 stacks. Due to the predictive power and accuracy of density-functional-based calculations characteristic defect properties such as thermodynamic and optical transition levels as well as their interaction with the surrounding can be directly connected to experimental observations. Hence, it is possible to assess how vacancy, interstitial, antisite, substitutional, and other defect types impact and perturb the device performance. Moreover, these results provide vital input for the development of modern predictive TCAD models.

Defect engineering of graphynes for energy storage and conversion