Large-scale heterogeneous synthesis of monodisperse high performance colloidal CsPbBr3 nanocrystals.

Colloidal lead halide perovskite nanocrystals (LHP NCs) are promising semiconductor materials for optoelectronic devices, but the high ionicity of LHP NCs makes their crystallization control and post-treatment difficult. Here, phosphonic acids (PAs) are employed as ligands to design a solid-liquid heterogeneous reaction system to regulate the LHP NC crystallization and achieve the desired focusing growth. During the heterogeneous synthesis, the precursors in the liquid phase are responsible for the burst nucleation and initial growth of NCs. Afterwards, the focusing growth of NCs is supported by the precursors released from the solid phase. In addition, the strong binding ability of PAs enables effective passivation of LHP NCs. Without post-treatment, gram-scale monodisperse CsPbBr3 NCs having photoluminescence with a full width at half-maximum of 18 nm and a quantum yield of near-unity are obtained. The CsPbBr3 NCs covered by a compact ligand layer keep initial quantum yield even after 18 cycles of purification, exhibiting excellent stability against polar solvents, ultraviolet irradiation and heat treatment. As scintillators, the prepared CsPbBr3 NCs show strong radioluminescence emission and high-resolution X-ray imaging.

Unity and ROS as a Digital and Communication Layer for Digital Twin Application: Case Study of Robotic Arm in a Smart Manufacturing Cell.

A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework's versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments.

Comprehensive determination of fatty acids in real samples without derivatization by DMU-SPME-GC methods.

The comprehensive determination of fatty acids without derivatization, including short-chain fatty acids (SCFAs), medium-chain fatty acids (MCFAs) and long-chain fatty acids (LCFAs), is a big challenge but powerful for lipidomics in biology, food, and environment. Herein, the dual mode unity solid-phase microextraction (DMU-SPME) combined with gas chromatography-flame ionization detector (GC-FID) or mass spectrometry (MS) was proposed as a powerful method for the determination of comprehensive free fatty acids in real samples. Under the optimized DMU-SPME conditions, the proposed method has good linearity (R2 ≥ 0.994) and low limits of determination (0.01-0.14 mg/L). In the stability analysis, the intra-day relative standard deviation was 1.39-12.43 %, and the inter-day relative standard deviation was 2.84-10.79 %. The recoveries of selected 10 fatty acids in real samples ranged from 90.18 % to 110.75 %, indicating that the method has good accuracy. Fatty acids ranging from C2 to C22 were detected in real samples by the untargeted determination method of DMU-SPME combined with gas chromatography-mass spectrometry (GC-MS). The DMU-SPME method proposed in this study can be used for lipid metabolism analysis and free fatty acid determination in the fields of biology, food, and environment.

Golden Breath: Feasibility and acceptability of a biofeedback-based virtual reality game on reducing children's needle-related pain and fear.

BACKGROUND: Needle procedures can cause pain and fear in children. Current literature reports that biofeedback-based virtual reality applications may help alleviate children's pain and fear. This study aims to evaluate the feasibility, acceptability, and safety of the newly developed game Golden Breath, which uses biofeedback-based virtual reality to reduce children's needle-related pain and fear. METHODS: The development of Golden Breath includes; (1) development of the application's features based on needs assessment, (2) gamification of the application content based on literature, (3) development of the prototype, (4) expert evaluation and feedback on the application content, (5) usability testing by 11 children (4-12 years). RESULTS: Regarding acceptability, the expert evaluation showed a high usability of the system. All children provided positive feedback and reported high satisfaction with Golden Breath. The game was feasible and effective for reducing children's pain and fear levels during needle procedures. Golden Breath was deemed safe for children because it did not cause symptoms such as dizziness, vomiting, or nausea. CONCLUSION: The expert evaluation, children's feedback, and pilot study results showed that the Golden Breath game is feasible, acceptable, and safe for children during the needle-related procedure. The pilot study revealed that the Golden Breath game effectively reduced pain and fear during blood sampling in children. PRACTICE TO IMPLICATIONS: It is recommended that healthcare professionals use Golden Breath to optimize the well-being of children receiving treatment for chronic and acute diseases.

Adult zebrafish can learn Morris water maze-like tasks in a two-dimensional virtual reality system.

Virtual reality (VR) has emerged as a powerful tool for investigating neural mechanisms of decision-making, spatial cognition, and navigation. In many head-fixed VRs for rodents, animals locomote on spherical treadmills that provide rotation information in two axes to calculate two-dimensional (2D) movement. On the other hand, zebrafish in a submerged head-fixed VR can move their tail to enable movement in 2D VR environment. This motivated us to create a VR system for adult zebrafish to enable 2D movement consisting of forward translation and rotations calculated from tail movement. Besides presenting the VR system, we show that zebrafish can learn a virtual Morris water maze-like (VMWM) task in which finding an invisible safe zone was necessary for the zebrafish to avoid an aversive periodic mild electric shock. Results show high potential for our VR system to be combined with optical imaging for future studies to investigate spatial learning and navigation.

Enhancing mutual understanding of e-scooter user's perspective in overtaking maneuver through replaying own driving trajectory.

The global adoption of e-scooters as a convenient mode of micro-mobility transportation is on the rise, offering a flexible solution for covering first- and last-mile journeys. However, this surge in usage brings challenges, particularly concerning road safety, as e-scooter riders often share road space with other vehicles, heightening the risk of serious accidents. While numerous studies have explored safe overtaking behaviors and safety perceptions from drivers' viewpoints, limited attention has been given to understanding the varying safety perceptions of both drivers and e-scooter riders, particularly after riding an e-scooter and being overtaken by their own vehicles. This research aims to bridge this gap by examining variations in safety perceptions and assessing behavioral changes before and after experiencing overtaking scenarios. Specifically, the study focuses on scenarios where an e-scooter rider experiences being overtaken by a vehicle they had previously driven. A Unity-based sequential simulation process is employed to replay scenarios obtained from a vehicle simulator during an e-scooter experiment involving the same participant without their awareness. This innovative approach allows e-scooter rider participants to relive their own prior vehicle overtaking maneuvers while riding an e-scooter. The findings reveal that most participants (64%) felt less safe as e-scooter riders, influenced by factors like relative speed and acceleration of overtaking vehicles. After experiencing being overtaken by their own pre-driven vehicles, a noteworthy positive correlation emerged between safety perception and lateral distance, indicating that greater distance is derived from a better understanding of e-scooter safety. The study demonstrates the effectiveness of the sequential simulation strategy in fostering safe driving behavior and raising road safety awareness. Experiencing overtaking behaviors firsthand as an e-scooter rider, previously behind the wheel of the overtaking vehicle, encourages a heightened awareness of road safety. These findings have significant implications for road safety authorities, suggesting the potential application of this approach in driver education programs. By incorporating such interventions tailored to improve the safety of vulnerable road users, authorities can take proactive steps towards mitigating risks associated with micro-mobility transportation.

Relative profile measurements in 1.5T MR-linacs: investigation of central axis deviations.

Objective. In 1.5 T MR-linacs, the absorbed dose central axis (CAX) deviates from the beam's CAX due to inherent profile asymmetry. In addition, a measured CAX deviation may be biased due to potential lateral (to the beam) effective point of measurement (EPOML) shifts of the detector employed. By investigating CAX deviations, the scope of this study is to determine a set ofEPOMLshifts for profile measurements in 1.5 T MR-linacs.Approach. The Semiflex 3D ion chamber and microDiamond detector (PTW, Germany) were considered in the experimental study while three more detectors were included in the Monte Carlo (MC) study. CAX deviations in the crossline and inline profiles were calculated based on inflection points of the 10×10 cm2field, at five centers. In MC simulations, the experimental setup was reproduced. A small water voxel was simulated to calculate CAX deviation without the impact of the detector-specificEPOMLshift.Main results. All measurements were consistent among the five centers. MC-based and experimental measurements were in agreement within uncertainties. Placing the microDiamond in the vertical orientation does not appear to affect the detector'sEPOML, which is on its central longitudinal axis. For the Semiflex 3D in the crossline direction, the CAX deviation was 2.3 mm, i.e. 1 mm larger than the ones measured using the microDiamond and simulated considering the ideal water detector. Thus, anEPOMLshift of 1 mm is recommended for crossline profile measurements under both Semiflex 3D orientations. For the inline profile, anEPOMLshift of -0.5 mm was determined only for the parallel configuration. In the MC study, CAX deviations were found detector- and orientation-dependent. The dead volume is responsible for theEPOMLshift only in the inline profile and under the parallel orientation.Significance. This work contributes to data availability on the correction or mitigation of the magnetic field-induced changes in the detectors' response.

FMCW Laser Fuze Structure with Multi-Channel Beam Based on 3D Particle Collision Scattering Model under Smoke Interference.

In the environment of smoke and suspended particles, the accurate detection of targets is one of the difficulties for frequency-modulated continuous-wave (FMCW) laser fuzes to work properly in harsh conditions. To weaken and eliminate the significant influence caused by the interaction of different systems in the photon transmission process and the smoke particle environment, it is necessary to increase the amplitude of the target echo signal to improve the signal-to-noise ratio (SNR), which contributes to enhancing the detection performance of the laser fuze for the ground target in the smoke. Under these conditions, the particle transmission of photons in the smoke environment is studied from the perspective of three-dimentional (3D) collisions between photons and smoke particles, and the modeling and Unity3D simulation of FMCW laser echo signal based on 3D particle collision is conducted. On this basis, a laser fuze structure based on multiple channel beam emission is designed for the combined effect of particle features from different systems and its impact on the target characteristics is researched. Simulation results show that the multiple channel laser emission enhances the laser target echo signal amplitude and also improves the anti-interference ability against the combined effects of multiple particle features compared with the single channel. Through the validation based on the laser prototype with four-channel beam emitting, the above conclusions are supported by the experimental results. Therefore, this study not only reveals the laser target properties under the 3D particle collision perspective, but also reflects the reasonableness and effectiveness of utilizing the target characteristics in the 3D particle collision mode to enhance the detection performance of FMCW laser fuze in the smoke.

Clinical experience with adaptive MRI-guided pancreatic SBRT and the use of abdominal compression to reduce treatment volume.

Introduction: This work presents a method to treat stereotactic body radiation therapy (SBRT) for pancreatic cancer on a magnetic resonance-guided linear accelerator (MR-linac) using daily adaptation, real-time motion monitoring, and abdominal compression. Methods: The motion management and treatment planning process involves a magnetic resonance imaging (MRI) simulation with cine and 3D images, a computed tomography (CT) simulation with a breath-hold CT and a 4DCT, pre-treatment verification and planning MRI, and intrafraction MRI cine images. Results: The results from 26 patients were included in this work. Our motion management process results in consistent motion analysis on the CT simulation, MRI simulation, and each treatment fraction. The liver dome was found to be an overestimate of tumor superior/inferior (SI) motion for most patients. Adding compression reduced SI liver dome motion by 6.2 mm on average. Clinical outcomes are similar to those observed in the literature. Conclusions: In this work, we demonstrate how pancreatic SBRT can be successfully treated on an MR-linac using abdominal compression. This allows for an increased duty cycle compared to gating and/or breath-hold techniques.

SynthOutdoor: A synthetic dataset for 3D outdoor light estimation.

In this work, we present a novel dataset, SynthOutdoor, comprising 39,086 high-resolution images, aimed at addressing the data scarcity in the field of 3D light direction estimation under the assumption of distant lighting. SynthOutdoor was generated using our software (which is also publicly available), that in turn is based on the Unity3D engine. Our dataset provides a set of images rendered from a given input scene, with the camera moving across a predefined path within the scene. This dataset captures a wide variety of lighting conditions through the implementation of a solar cycle. The dataset's ground truth is composed of the following elements: the 3D light direction and color intensity of the sun; the color intensity of the ambient light; the instance segmentation masks of each object and the surface normals map, in which each pixel is assigned with the 3D surface normal in that point (encoded as 3 color channels). By providing not only the light direction and intensity, but also the geometric and semantic information of the rendered images, our dataset can be used not only for light estimation, but also for more general tasks such as 3D geometry and shading estimation from 2D images.

Systematic studies on the kinetic process of 20(S)-protopanaxadiol in rats and dogs: absorption, distribution, metabolism and excretion.

Background and Objective: Ginseng has been regarded as a precious medicinal herb with miraculous effects in Eastern culture. The primary chemical constituents of ginseng are saponins, and the physiological activities of ginsenosides determine their edible and medicinal value. The aim of this study is to comprehensively and systematically investigate the kinetic processes of 20(S)-protopanaxadiol (PPD) in rats and dogs, in order to promote the rational combination of ginseng as a drug and dietary ingredient. Methods: PPD was administered, and drug concentration in different biological samples were detected by liquid chromatography tandem mass spectrometry (LC/MS/MS) and radioactive tracer methods. Pharmacokinetic parameters such as absorption, bioavailability, tissue distribution, plasma protein binding rate, excretion rate, and cumulative excretion were calculated, along with inference of major metabolites. Results: This study systematically investigated the absorption, distribution, metabolism, excretion (ADME) of PPD in rats and dogs for the first time. The bioavailabilities of PPD were relatively low, with oral absorption nearly complete, and the majority underwent first-pass metabolism. PPD had a high plasma protein binding rate and was relatively evenly distributed in the body. Following oral administration, PPD underwent extensive metabolism, potentially involving one structural transformation and three hydroxylation reactions. The metabolites were primarily excreted through feces and urine, indicating the presence of enterohepatic circulation. The pharmacokinetic processes of PPD following intravenous administration aligned well with a three-compartment model. In contrast, after gastric administration, it fitted better with a two-compartment model, conforming to linear pharmacokinetics and proportional elimination. There were evident interspecies differences between rats and dogs regarding PPD, but individual variations of this drug were minimal within the same species. Conclusion: This study systematically studied the kinetic process of PPD in rats and also investigated the kinetic characteristics of PPD in dogs for the first time. These findings lay the foundation for further research on the dietary nutrition and pharmacological effects of PPD.

Development and validation of an MR-driven dose-of-the-day procedure for online adaptive radiotherapy in upper gastrointestinal cancer patients.

Objective.To develop and validate a dose-of-the-day (DOTD) treatment plan verification procedure for liver and pancreas cancer patients treated with an magnetic resonance (MR)-Linac system.Approach.DOTD was implemented as an automated process that uses 3D datasets collected during treatment delivery. Particularly, the DOTD pipeline's input included the adapt-to-shape (ATS) plan-i.e. 3D-MR dataset acquired at beginning of online session, anatomical contours, dose distribution-and 3D-MR dataset acquired during beam-on (BON). The DOTD automated analysis included (a) ATS-to-BON image intensity-based deformable image registration (DIR), (b) ATS-to-BON contours mapping via DIR, (c) BON-to-ATS contours copying through rigid registration, (d) determining ATS-to-BON dosimetric differences, and (e) PDF report generation. The DIR process was validated by two expert reviewers. ATS-plans were recomputed on BON datasets to assess dose differences. DOTD analysis was performed retrospectively for 75 treatment fractions (12-liver and 5-pancreas patients).Main results.The accuracy of DOTD process relied on DIR and mapped contours quality. Most DIR-generated contours (99.6%) were clinically acceptable. DICE correlated with depreciation of DIR-based region of interest mapping process. The ATS-BON plan difference was found negligible (<1%). The duodenum and large bowel exhibited highest variations, 24% and 39% from fractional values, for 5-fraction liver and pancreas. For liver 1-fraction, a 62% variation was observed for duodenum.Significance.The DOTD methodology provides an automated approach to quantify 3D dosimetric differences between online plans and their delivery. This analysis offers promise as a valuable tool for plan quality assessment and decision-making in the verification stage of the online workflow.

Introducing HiBoP: a Unity-based visualization software for large iEEG datasets.

BACKGROUND: Intracranial EEG data offer a unique spatio-temporal precision to investigate human brain functions. Large datasets have become recently accessible thanks to new iEEG data-sharing practices and tighter collaboration with clinicians. Yet, the complexity of such datasets poses new challenges, especially regarding the visualization and anatomical display of iEEG. NEW METHOD: We introduce HiBoP, a multi-modal visualization software specifically designed for large groups of patients and multiple experiments. Its main features include the dynamic display of iEEG responses induced by tasks/stimulations, the definition of Regions and electrodes Of Interest, and the shift between group-level and individual-level 3D anatomo-functional data. RESULTS: We provide a use-case with data from 36 patients to reveal the global cortical dynamics following tactile stimulation. We used HiBoP to visualize high-gamma responses [50-150 Hz], and define three major response components in primary somatosensory and premotor cortices and parietal operculum. COMPARISON WITH EXISTING METHODS(S): Several iEEG softwares are now publicly available with outstanding analysis features. Yet, most were developed in languages (Python/Matlab) chosen to facilitate the inclusion of new analysis by users, rather than the quality of the visualization. HiBoP represents a visualization tool developed with videogame standards (Unity/C#), and performs detailed anatomical analysis rapidly, across multiple conditions, patients, and modalities with an easy export toward third-party softwares. CONCLUSION: HiBoP provides a user-friendly environment that greatly facilitates the exploration of large iEEG datasets, and helps users decipher subtle structure/function relationships.

From individual resilience to collective response: reframing ecological emotions as catalysts for holistic environmental engagement.

The ongoing international study on the mental health implications of climate change has prompted a deeper exploration of ecological emotions such as eco-anxiety, eco-worry and eco-grief, which are associated with environmental degradation and the escalating climate crisis. Although psychological and mental health literature has mainly presented preliminary conceptual analyses, the understanding of ecological emotions remains unclear. This narrative review aims to clarify the definition, highlight precipitating factors, and outline the effects of ecological emotions on mental health, emphasizing the need for thorough research to shift the nonclinical intervention approach from merely promoting individual resilience to encouraging collective engagement. Our analysis of the literature reveals that the existing theoretical framework, which predominantly focuses on bolstering individual resilience, provides only temporary relief for acute symptoms without addressing the foundational social and environmental factors that trigger these ecological emotions. We conclude that it is crucial to overcome the limitations of Western anthropocentrism's human-to-human interaction approach and embrace the unity of humans and nature to effectively manage the increasing ecological emotions. This perspective draws insights from the holistic and collective wisdom of indigenous cultures and traditional Chinese philosophy, offering a potential pathway toward maintaining a sustainable emotional balance amid the worsening global ecological turmoil.

Using Unity for Scientific Visualization as a Course-based Undergraduate Research Experience.

We have developed a series of course-based undergraduate research experiences for students integrated into course curriculum centered around the use of 3D visualization and virtual reality for science visualization. One project involves the creation and use of a volumetric renderer for hyperstack images, paired with a biology project in confocal microscopy. Students have worked to develop and test VR enabled tools for confocal microscopy visualization across headset based and CAVE based VR platforms. Two applications of the tool are presented: a rendering of Drosophila primordial germ cells coupled with automated detection and counting, and a database in development of 3D renderings of pollen grains. Another project involves the development and testing of point cloud renderers. Student work has focused on performance testing and enhancement across a range of 2D and 3D hardware, including native Quest apps. Through the process of developing these tools, students are introduced to scientific visualization concepts, while gaining practical experience with programming, software engineering, graphics, shader programming, and cross-platform design.

Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study.

Background: Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the disease may vary from individual to individual, that is, it is essential to identify the clinical parameters that indicate a good prognosis for the patient. Machine learning (ML) algorithms have been used for analyzing complex medical data and identifying prognostic indicators. However, there is still an urgent need for a model to elucidate the predictors related to patient outcomes. Therefore, this research aimed to verify, through ML, the variables involved in the discharge of patients admitted to the ICU due to COVID-19. Methods: In this study, 126 variables were collected with information on demography, hospital length stay and outcome, chronic diseases and tumors, comorbidities and risk factors, complications and adverse events, health care, and vital indicators of patients admitted to an ICU in southern Brazil. These variables were filtered and then selected by a ML algorithm known as decision trees to identify the optimal set of variables for predicting patient discharge using logistic regression. Finally, a confusion matrix was performed to evaluate the model's performance for the selected variables. Results: Of the 532 patients evaluated, 180 were discharged: female (16.92%), with a central venous catheter (23.68%), with a bladder catheter (26.13%), and with an average of 8.46- and 23.65-days using bladder catheter and submitted to mechanical ventilation, respectively. In addition, the chances of discharge increase by 14% for each additional day in the hospital, by 136% for female patients, 716% when there is no bladder catheter, and 737% when no central venous catheter is used. However, the chances of discharge decrease by 3% for each additional year of age and by 9% for each other day of mechanical ventilation. The performance of the training data presented a balanced accuracy of 0.81, sensitivity of 0.74, specificity of 0.88, and the kappa value was 0.64. The test performance had a balanced accuracy of 0.85, sensitivity 0.75, specificity 0.95, and kappa value of 0.73. The McNemar test found that there were no significant differences in the error rates in the training and test data, suggesting good classification. This work showed that female, the absence of a central venous catheter and bladder catheter, shorter mechanical ventilation, and bladder catheter duration were associated with a greater chance of hospital discharge. These results may help develop measures that lead to a good prognosis for the patient.

Miracle in "White":Hexagonal Boron Nitride.

The exploration of 2D materials has captured significant attention due to their unique performances, notably focusing on graphene and hexagonal boron nitride (h-BN). Characterized by closely resembling atomic structures arranged in a honeycomb lattice, both graphene and h-BN share comparable traits, including exceptional thermal conductivity, impressive carrier mobility, and robust pi-pi interactions with organic molecules. Notably, h-BN has been extensively examined for its exceptional electrical insulating properties, inert passivation capabilities, and provision of an ideal ultraflat surface devoid of dangling bonds. These distinct attributes, contrasting with those of h-BN, such as its conductive versus insulating behavior, active versus inert nature, and absence of dangling surface bonds versus absorbent tendencies, render it a compelling material with broad application potential. Moreover, the unity of such contradictions endows h-BN with intriguing possibilities for unique applications in specific contexts. This review aims to underscore these key attributes and elucidate the intriguing contradictions inherent in current investigations of h-BN, fostering significant insights into the understanding of material properties.

Development of shielding evaluation and management program for O-ring type linear accelerators.

The shielding parameters can vary depending on the geometrical structure of the linear accelerators (LINAC), treatment techniques, and beam energies. Recently, the introduction of O-ring type linear accelerators is increasing. The objective of this study is to evaluate the shielding parameters of new type of linac using a dedicated program developed by us named ORSE (O-ring type Radiation therapy equipment Shielding Evaluation). The shielding evaluation was conducted for a total of four treatment rooms including Elekta Unity, Varian Halcyon, and Accuray Tomotherapy. The developed program possesses the capability to calculate transmitted dose, maximum treatable patient capacity, and shielding wall thickness based on patient data. The doses were measured for five days using glass dosimeters to compare with the results of program. The IMRT factors and use factors obtained from patient data showed differences of up to 65.0% and 33.8%, respectively, compared to safety management report. The shielding evaluation conducted in each treatment room showed that the transmitted dose at every location was below 1% of the dose limit. The results of program and measurements showed a maximum difference of 0.003 mSv/week in transmitted dose. The ORSE program allows for the shielding evaluation results to the clinical environment of each institution based on patient data.

Near-Unity Photoluminescence Quantum Yield of Core-Only InP Quantum Dots via a Simple Postsynthetic InF3 Treatment.

Indium phosphide (InP) quantum dots (QDs) are considered the most promising alternative for Cd and Pb-based QDs for lighting and display applications. However, while core-only QDs of CdSe and CdTe have been prepared with near-unity photoluminescence quantum yield (PLQY), this is not yet achieved for InP QDs. Treatments with HF have been used to boost the PLQY of InP core-only QDs up to 85%. However, HF etches the QDs, causing loss of material and broadening of the optical features. Here, we present a simple postsynthesis HF-free treatment that is based on passivating the surface of the InP QDs with InF3. For optimized conditions, this results in a PLQY as high as 93% and nearly monoexponential photoluminescence decay. Etching of the particle surface is entirely avoided if the treatment is performed under stringent acid-free conditions. We show that this treatment is applicable to InP QDs with various sizes and InP QDs obtained via different synthesis routes. The optical properties of the resulting core-only InP QDs are on par with InP/ZnSe/ZnS core-shell QDs, with significantly higher absorption coefficients in the blue, and with potential for faster charge transport. These are important advantages when considering InP QDs for use in micro-LEDs or photodetectors.

An evaluation of intelligent and immersive digital applications in eliciting cognitive states in humans through the utilization of Emotiv Insight.

The amalgamation of Virtual Reality (VR) and Artificial Intelligence (AI) results in the development of many promising applications that are helpful for society in many aspects. This research was done to study the effect of immersive and non-immersive applications on user's psychological parameters. In this paper, an intelligent, interactive, and immersive digital application was designed, and the various psychological parameters of users while using the application were analyzed through the brain computer interactive device, Emotiv. The impact of these robust and immersive applications on the emotions of human beings was analyzed. According to the observations, the stress and relaxation levels are getting minimally affected, whereas the engagement levels are high for an immersive application rather than a non-immersive application. Hence, it can be concluded that immersive applications put users "in" the application environment and provide a near-realistic experience by blurring the line between the real and virtual worlds. Deeper immersion results from the increased sensation of presence, which in turn is helpful in increasing motivation and emotional investment.•This paper demonstrates the implementation of the A* algorithm within the Unity 3D Game Engine to develop an intelligent digital application, fostering interactivity and depth.•This paper explores the integration of VR technology to transform the digital application into an immersive and interactive experience, enhancing user engagement and realism.•This paper investigates the utilization of the Emotiv Insight device to analyze cognitive parameters within both non-immersive AI-based and immersive AI & VR-based applications, providing insights into user experiences.