Local Trapping Research Articles

A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization.

Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO’s exploration phase is enhanced, and the SHO’s hunting mechanisms are modified with PSO’s velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization.

Image segmentation of Leaf Spot Diseases on Maize using multi-stage Cauchy-enabled grey wolf algorithm

Grey wolf optimizer (GWO) is a widespread metaphor-based algorithm based on the enhanced variants of velocity-free particle swarm optimizer with proven defects and shortcomings in performance. Regardless of the proven defect and lack of novelty in this algorithm, the GWO has a simple algorithm and it may face considerable unbalanced exploration and exploitation trends. However, GWO is easy to be utilized, and it has a low capacity to deal with multi-modal functions, and it quickly falls into the optima trap or fails to find the global optimal solution. To improve the shortcomings of the basic GWO, this paper proposes an improved GWO called multi-stage grey wolf optimizer (MGWO). By dividing the search process into three stages and using different population updating strategies at each stage, the MGWO’s optimization ability is improved while maintaining a certain convergence speed. The MGWO cannot easily fall into premature convergence and has a better ability to get rid of the local optima trap than GWO. Meanwhile, the MGWO achieves a better balance of exploration and exploitation and has a rough balance curve. Hence, the proposed MGWO can obtain a higher-quality solution. Based on verification on the thirty benchmark functions of IEEE CEC2017 as the objective functions, the simulation experiments in which MGWO compared with some swarm-based optimization algorithms and the balance and diversity analysis were conducted. The results verify the effectiveness and superiority of MGWO. Finally, the MGWO was applied to the multi-threshold image segmentation of Leaf Spot Diseases on Maize at four different threshold levels. The segmentation results were analysed by comparing each comparative algorithm’s PSNR, SSIM, and FSIM. The results proved that the MGWO has noticeable competitiveness, and it can be used as an effective optimizer for multi-threshold image segmentation.

Element doping-induced effects in Zn-doped CdTe quantum-dot system: Insights from an ultrafast dynamics perspective.

Element doping can have a profound impact on the photoelectrochemical properties of quantum dots (QDs); nevertheless, the hitherto known information in this regard is mainly from the steady-state characterizations and remains lacking input from the dynamics perspective. Herein, we present a systematic scrutiny of the element doping-induced effects in Zn-doped CdTe QDs. By means of steady-state/time-resolved/temperature-dependent photoluminescence spectroscopy and ultrafast transient absorption spectroscopy, we reveal that the slight Zn-doping in CdTe QDs can greatly affect the involved carrier relaxation dynamics through a density-of-state modification for both near-band-edge and localized surface trap states. Furthermore, such slight doping is found to be quite significant in modulating the photoreduction efficiency (of particular relation to the localized surface trap states) as well as altering the involved relaxation/reaction activation energy and phonon effect in this QDs system. This work enriches our fundamental understanding of the element doping-induced surface/interface effects, from the dynamics perspective in particular, and, hence, offers helpful guidance for QDs-based photoelectrochemical design and optimization.

Computable criteria for ballisticity of random walks in elliptic random environment

We consider random walks in i.i.d. elliptic random environments which are not uniformly elliptic. We introduce a computable condition in dimension d=2 and a general condition valid for dimensions d≥2 expressed in terms of the exit time from a box, which ensure that local trapping would not inhibit a ballistic behavior of the random walk. An important technical innovation related to our computable condition, is the introduction of a geometrical point of view to classify the way in which the random walk can become trapped, either in an edge, a wedge or a square. Furthermore, we prove that the general condition we introduce is sharp.

Training a Feedforward Neural Network Using Hybrid Gravitational Search Algorithm with Dynamic Multiswarm Particle Swarm Optimization.

One of the most well-known methods for solving real-world and complex optimization problems is the gravitational search algorithm (GSA). The gravitational search technique suffers from a sluggish convergence rate and weak local search capabilities while solving complicated optimization problems. A unique hybrid population-based strategy is designed to tackle the problem by combining dynamic multiswarm particle swarm optimization with gravitational search algorithm (GSADMSPSO). In this manuscript, GSADMSPSO is used as novel training techniques for Feedforward Neural Networks (FNNs) in order to test the algorithm's efficiency in decreasing the issues of local minima trapping and existing evolutionary learning methods' poor convergence rate. A novel method GSADMSPSO distributes the primary population of masses into smaller subswarms, according to the proposed algorithm, and also stabilizes them by offering a new neighborhood plan. At this time, each agent (particle) increases its position and velocity by using the suggested algorithm's global search capability. The fundamental concept is to combine GSA's ability with DMSPSO's to improve the performance of a given algorithm's exploration and exploitation. The suggested algorithm's performance on a range of well-known benchmark test functions, GSA, and its variations is compared. The results of the experiments suggest that the proposed method outperforms the other variants in terms of convergence speed and avoiding local minima; FNNs are being trained.

Annealing Genetic GAN for Imbalanced Web Data Learning

Class imbalance is one of the most basic and important problems of web data. The key to overcoming the class imbalance problems is to increase the effective instances of the minority, that is, data augmentation. Generative Adversarial Networks (GANs), which have recently been successfully applied in the field of image generation, can be used for data augmentation because they can learn the data distribution given ample training data instances and generate more data. However, learning the distributions from the imbalanced data can make GANs easily get stuck in a local optimum. In this work, we propose a new training strategy called Annealing Genetic GAN (AGGAN), which incorporates simulated annealing genetic algorithm into the training process of GANs. And this can help GANs avoid the local optimum trapping problem, which easily occurs when the training set is imbalanced. Unlike existing GANs, which use a fixed adversarial learning objective alternately training a generator, we use multiple adversarial learning objectives to train a set of generators and use the Metropolis criterion in simulated annealing to decide whether the generator should update. More specifically, the Metropolis criterion accepts worse solutions with a certain probability, so it can make our AGGAN escape from the local optimum and find a better solution. Theory and mathematical analysis provide strong theoretical support for the proposed training strategy. And experiments on several datasets demonstrate that AGGAN achieves convincing ability to solve the class imbalanced problem and reduces the training problems inherent in existing GANs.

PAIDDE: A Permutation-Archive Information Directed Differential Evolution Algorithm

Evolutionary algorithms have show great successes in various real-world applications ranging in molecule to astronomy. As a mainstream evolutionary algorithm, differential evolution (DE) possesses the characteristics of simple algorithmic structure, easy implement, and efficient search performance. Nevertheless, it still suffers from the issues of local optimal trapping and premature of evolution problems. In this study, we innovatively improve the performance of DE by incorporating a full utilization of information feedback, which includes the population’s holistic information and the direction of differential vectors. The proposed permutation-archive information directed differential evolution (PAIDDE) algorithm is verified on a set of 29 benchmark numerical functions and 22 real-world optimization problems. Extensive experimental and statistical results show that PAIDDE can significantly outperform other 12 state-of-the-art algorithms in terms of solution qualities. Additionally, the computational complexity, solution distribution, convergence speed, search dynamics, and population diversity of PAIDDE are systematically analyzed. The source code of PAIDDE can be found at https://toyamaailab.github.io/sourcedata.html.

Solving the TSP by the AALHNN algorithm.

It is prone to get stuck in a local minimum when solving the Traveling Salesman Problem (TSP) by the traditional Hopfield neural network (HNN) and hard to converge to an efficient solution, resulting from the defect of the penalty method used by the HNN. In order to mend this defect, an accelerated augmented Lagrangian Hopfield neural network (AALHNN) algorithm was proposed in this paper. This algorithm gets out of the dilemma of penalty method by Lagrangian multiplier method, ensuring that the solution to the TSP is undoubtedly efficient. The second order factor added in the algorithm stabilizes the neural network dynamic model of the problem, thus improving the efficiency of solution. In this paper, when solving the TSP by AALHNN, some changes were made to the TSP models of Hopfield and Tank. Say, constraints of TSP are multiplied by Lagrange multipliers and augmented Lagrange multipliers respectively, The augmented Lagrange function composed of path length function can ensure robust convergence and escape from the local minimum trap. The Lagrange multipliers are updated by using nesterov acceleration technique. In addition, it was theoretically proved that the extremum obtained by this improved algorithm is the optimal solution of the initial problem and the approximate optimal solution of the TSP was successfully obtained several times in the simulation experiment. Compared with the traditional HNN, this method can ensure that it is effective for TSP solution and the solution to the TSP obtained is better.

Enhanced Heap-Based Optimizer Algorithm for Solving Team Formation燩roblem

Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO’s optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum.

An Optimal Microgrid Operations Planning Using Improved Archimedes Optimization Algorithm

More new energy sources have been incorporated into a microgrid model with parameter space growing exponentially, causing optimization scheduling as a nonlinear issue to become more complex and difficult to calculate. This study suggests an improved Archimedes optimization algorithm (IAOA) increases optimal performance for the microgrid operations planning issue. A multiobjective function about optimization planning issues is constructed with relevant economic costs and environmental profits for a microgrid community system (MCS). The IAOA is implemented based on the Archimedes optimization algorithm (AOA) by adding reverse learning and multi-directing strategies to avoid the local optimum trap when dealing with complicated situations. The experimental results of the suggested approach on the CEC2017 test suite and microgrid operations planning problem are compared to the various algorithms in the identical condition scenarios to evaluate the recommended approach performance. Compared findings reveal that the suggested IAOA outperforms the various algorithms in comparison, practical solution, and high feasibility.

A compositional function hybridization of PSO and GWO for solving well placement optimisation problem

Advances in technology and optimisation are helping to improve decision making in the oil and gas industry. However, most of the traditional metaheuristic algorithms applied in well placement optimisation problems suffer from extensive parameter experimentations and local optimum trapping issues. This couples with the complex and heterogeneous nature of hydrocarbon reservoirs and increased decision variables poses severe simulation process demands. This study considered a functional composition integration approach to formulate a robust hybrid metaheuristic algorithm called HGWO-PSO. The HGWO-PSO leverages on the strengths of Grey Wolf Optimiser (GWO) and Particle Swarm Optimisation (PSO) and the Clerc's parameter setting considerations. A rigorous approach which enforces regulatory agreed minimum well spacing was incorporated in the optimisation process. Reservoir models ranging from unimodal to multimodal spatial systems were used as examples to test the explorative and exploitative capabilities of the algorithms. In this paper we show the performance curve and statistical analysis of HGWO-PSO as a well placement algorithm and compares its performance with that of standalone PSO, and GWO and the traditional Genetic Algorithm (GA). Results revealed that the HGWO-PSO demonstrated comparative performances in terms of exploration and exploitation obtaining the best optimal solutions which give highest contractor's NPVs in majority of cases considered. Again, the means and standard deviations for HGWO-PSO among the various runs showed consistent and efficient performance. The Wilcoxon signed rank test conducted gave very low p-values suggesting uniqueness of HGWO-PSO from the other metaheuristic variants. Additionally, the computational speed of HGWO-PSO was relatively better as compared to the individual GWO and PSO in most the test cases. The simulation results for all test cases confirm that implementation of HGWO-PSO can cause considerable improvement in locations of wells even in heterogeneous reservoirs.

Collision-Free Motion Planning of a Six-Link Manipulator Used in a Citrus Picking Robot

This paper presents the results of a motion planning algorithm that has been used in an intelligent citrus-picking robot consisting of a six-link manipulator. The real-time performance of a motion planning algorithm is urgently required by the picking robot. Within the artificial potential field (APF) method, the motion planning of the picking manipulator was basically solved. However, the real-time requirement of the picking robot had not been totally satisfied by APF because of some native defects, such as the large number of calculations used to map forces into torques by the Jacobian matrix, local minimum trap, and target not reachable problem, which greatly reduce motion planning efficiency and real-time performance of citrus-picking robots. To circumvent those problems, this paper proposed some novel methods that improved the mathematical models of APF and directly calculates the attractive torques in the joint space. By using the latter approach, the calculation time and the total joint error were separately reduced by 54.89% and 45.41% compared with APF. Finally, the novel algorithm is presented and demonstrated with some illustrative examples of the citrus picking robot, both offline during the design phase as well as online during a realistic picking test.

An Improved Quantum-Inspired Gravitational Search Algorithm to Optimize the Facial Features

The optimization of the features is vital to effectively detecting facial expressions. This research work has optimized the facial features by employing the improved quantum-inspired gravitation search algorithm (IQI-GSA). The improvement to the quantum-inspired gravitational search algorithm (QIGSA) is conducted to handle the local optima trapping. The QIGSA is the amalgamation of the quantum computing and gravitational search algorithm that owns the overall strong global search ability to handle the optimization problems in comparison with the gravitational search algorithm. In spite of global searching ability, the QIGSA can be trapped in local optima in the later iterations. This work has adapted the IQI-GSA approach to handle the local optima, stochastic characteristics and maintaining balance among the exploration and exploitation. The IQI-GSA is utilized for the optimized features selection from the set of extracted features using the LGBP (a hybrid approach of local binary patterns with the Gabor filter) method. The system performance is analyzed for the application of automated facial expressions recognition with the classification technique of deep convolutional neural network (DCNN). The extensive experimentation evaluation is conducted on the benchmark datasets of Japanese Female Facial Expression (JAFFE), Radboud Faces Database (RaFD) and Karolinska Directed Emotional Faces (KDEF). To determine the effectiveness of the proposed facial expression recognition system, the results are also evaluated for the feature optimization with GSA and QIGSA. The evaluation results clearly demonstrate the outperformed performance of the considered system with IQI-GSA in comparison with GSA, QIGSA and existing techniques available for the experimentation on utilized datasets.

Defect Tolerance Mechanism Revealed! Influence of Polaron Occupied Surface Trap States on CsPbBr3 Nanocrystal Photoluminescence: Ab Initio Excited-State Dynamics.

Lead halide perovskite (LHP) nanocrystals (NCs) show exceptional defect tolerance which has been attributed to their unique electronic structure, where defect energy levels are not introduced inside the fundamental bandgap, and the role of polarons in screening charge carriers from defects. Here, we use ab initio atomistic simulations to explore the interplay between various surface chemistries (A = Cs+, R'NH3+; X = Br-, RCOO-) used to passivate a CsPbBr3 NC surface and their impact on the ground-state (GS) and excited-state (ES) photophysical properties. We investigate pristine fully passivated surfaces and A-X vacancy defects that reflect chemical reactions A+ + X- → AX on the surface, which result in ligand desorption. For each surface configuration, calculations are performed in the GS and lowest ES (L-ES) electronic configurations, approximating polaron formation after photoexcitation. For models with A-X surface vacancies, we find that localized electron surface trap (ST) states emerge ∼100-400 meV below the pristine Se band in the L-ES configuration due to polaronic nuclear reorganization. Surprisingly, these trap states contribute relatively bright Sh → ST spectral features. To test if these surface trap states remain bright in a dynamic (thermal) situation we implement excited-state molecular dynamics simulations. It is found that the surface defected model shows an enhanced nonradiative recombination rate which reduces the photoluminescence quantum yield (PLQY) from 95% for the pristine surface to 75%. This is accompanied by an order of magnitude reduction in PL intensity and a red shift of the transition energy. This study provides more evidence of the defect tolerance of LHP NCs along with evidence of surface trap states contributing to efficient photoluminescence. The observation of relatively bright surface trap states could provide insight into photophysical phenomena, such as size-dependent stretched-exponential photoluminescence decay and Stokes shifts.

Characterization and correction of serial deferred charge in LSST camera ITL CCDs

Electro-optical testing and characterization of the Vera C. Rubin Observatory Legacy Survey of Space and Time Camera has shown that the focal plane charge-coupled devices developed by Imaging Technology Laboratory exhibit serial deferred charge signal in excess of the expected proportional deferred charge loss from global charge transfer inefficiency. We model the additional deferred charge as resulting from localized charge trapping in the serial register and from a signal-dependent electronic offset effect. A physically motivated forward deferred charge simulation algorithm and a reverse deferred charge correction algorithm were developed in order to correct for deferred charge during data reduction and reduce the possible impacts on Rubin Observatory project science goals. We apply the correction algorithm to calibration data and demonstrate the ability to reduce deferred charge signal measured in the overscan pixel regions and remove the serial pixel correlations resulting from the redistribution of charge between pixel neighbors during readout.

Dendritic neuron model trained by information feedback-enhanced differential evolution algorithm for classification

As the well-known McCulloch–Pitts neuron model has long been criticized to be oversimplified, different algebra to formulate a single neuron model has received increasing interests. The dendritic neuron model (DNM) which considers the nonlinear information processing capacity of both synapses and dendrites has shown its effectiveness on classification problems. However, an effective learning method for DNM is still highly desired and challenging because the traditional error back-propagation (BP) algorithm usually suffers from issues originated from the proliferation of saddle points and local minima trapping. In this study, a novel non-BP learning algorithm based on the differential evolution algorithm is proposed. By fully incorporating the feedback information from evolving population and individuals, the proposed algorithm significantly outperforms other ten state-of-the-art non-BP algorithms and BP in terms of the classification accuracy. Additionally, the neural morphology and hardware realization are also confirmed. These lead us to believe that the well-learned DNM is considerably more powerful on computations than the traditional McCulloch–Pitts type, and can be possibly used as a fundamental unit in the next-generation deep learning techniques.

A HYBRID FUZZY C-MEDOIDS CLUSTERING USING THE WHALE OPTIMIZATION ALGORITHM

Although the fuzzy c-means algorithm (FCM) has been widely used in many fields, they are sensitive to noise and outliers. Recently, the Fuzzy C-Medoids (FCMdd) algorithm has been shown to be more effective in dealing with noise data. The difference between FCM and FCMdd is the formation mechanism of clusters. At the same time, FCM builds clusters based on membership function and samples in the cluster. FCMdd selects some of the existing actual samples as cluster medoids. This results in FCMdd being able to handle noise better than FCM. This paper presents a hybrid approach of the whale optimization algorithm WOA with FCMdd (FWCMdd) to optimize the clustering process. This hybridization prevents FWCMdd from falling into the local trap and rapidly converging. This solution has been compared with the Fuzzy k-Medoids (FKM) algorithm and the primitive FCMdd. The results indicate that the proposed method is better than most of the evaluation indicators.

A microbial treatment population dynamics optimization approach

In order to solve some function optimization problems, Population Dynamics Optimization Algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500∼1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

P13.10 Chemoattraction of glioma cells in a local hydrogel trap and immune control associated with improved survival and cognitive functions in a mouse model of glioblastoma resection

Abstract BACKGROUND Glioblastoma (GB) is the most aggressive brain primary tumor. The prognosis remains poor mainly due to the invasiveness of glioma cells, radio and/or chemoresistance and GB-induced immunosuppressive environment. Here, we propose to use a local delivery system based on a biocompatible hydrogel containing the chemopeptide urotensin II (hUII) or a biased synthetic analog DAB8-hUII, to “trap” GB cells, and/or to control immune cells expressing its G protein-coupled receptor UT, leading to tumor regression and neurological benefit, in a mouse model of GB resection. MATERIAL AND METHODS In vitro, invasion towards UII/analog across different hydrogels or glue of human or murine GB-GFP cell lines was evaluated in Boyden chamber and cloning ring assays. In vivo GB cells were intrastriatally xenografted, then resected while hydrogel- or glue-containing UII/analog was injected in the cavity resection. Behavioral tests, brain immunohistochemical analyses and mouse survival were then investigated. RESULTS In vitro, invasive capacity of human U87 and 42MG or murine GL261 and CT2A GB cells was stimulated by UII loaded into hydrogel-based hyaluronic acid supplemented with collagen or other chemicals, PNIPAAm-PEG, or thrombin-fibrin glue. In vivo, injection of UII- or DAB8-hUII-loaded glue into the cavity resection of GL261 and CT2A GB in C57BL/6 mice significantly improved survival compared with tumor and resected experimental conditions. Neurological status was also tested before and after GB resection. We found that GL261 and CT2A cell-bearing mice expressed altered spontaneous activity, emotion and cognitive functions. Intracavity injection of the glue improved resignation and anxiety and increased motor activity and cognition with a best cognitive recovery with hUII and DAB-8-hUII-loaded glue groups. Ex vivo brain analyses revealed high expression of UT and UII in some GB GFP-positive cells and macrophages within GB core and at the interface with the normal brain, GB cells expressing UT migrating along tortuous podocalyxin+ vascular components. In brains bearing hydrogel/hUII glue, vascularization appears modified and GFAP+ astrocytes and F4/80+ macrophages were highly recruited in the border of the cavity, compared with the other conditions. CONCLUSION A local glue containing UII may trap GB cells and remodel the tumor microenvironment responsible for survival and cognitive improvements, providing new option in the therapeutic arsenal of GB.

Shape, Electronic Structure, and Trap States in Indium Phosphide Quantum Dots

Localized states on undercoordinated atoms at the surface of a quantum dot (QD) can become electron or hole traps when the atomic orbitals involved fall within the highest occupied molecular orbital (HOMO)–lowest unoccupied molecular orbital (LUMO) gap of the material. Therefore, QDs of more covalent materials are predicted to be more sensitive to trap-state formation. Here, we analyze the relation between the presence of undercoordinated surface atoms and the formation of localized trap states for InP QDs by means of density functional theory. We argue that the requirement of charge neutrality limits the possible shapes cation-rich InP QDs can take, among which small cuboctahedrons and tetrahedrons of any size. We thus select both structures as InP model QDs and show that a complete InCl3 surface passivation results in an electronic structure with a clean, delocalized HOMO–LUMO gap in either case. Upon removal of InCl3 formula units from the InP cuboctahedron, hole traps linked to 2-coordinated surface P can form, a behavior similar to that of CdSe QDs. On the other hand, InCl3 displacement from the InP tetrahedron can lead to hole and electron traps, related to 2- or 3-coordinated surface P and 2-coordinated surface In, respectively. Since we obtain a very similar electronic structure of pristine InP and CdSe cuboctahedrons, we argue that the concept of ionicity provides little guidance to understand the enhanced sensitivity of InP to trap-state formation. Therefore, more apt descriptors are needed to predict the energetic position of atomic orbitals of uncoordinated atoms relative to the delocalized band-edge states.