Use In Many Applications Research Articles

Toughness Enhancement of PLA-Based Filaments for Material Extrusion 3D Printing

Poly(lactic acid) (PLA) is one of the most popular biodegradable thermoplastics in the market of 3D printing filaments used in the material extrusion (ME) technique. This is because it can be printed easily at low temperatures. However, its inherent brittleness limits its use in many applications. In this work, the toughness of PLA filament was improved by blending with various types of rubbers including natural rubber (NR), acrylic core–shell rubber (CSR), and thermoplastic polyurethane (TPU) in the amount of 15% by weight. PLA/TPU filament was found to have the smoothest surface with the best shape and dimension stability, while PLA/NR filament rendered the highest tensile toughness. In term of the effect of printing temperature, the highest printing temperature in this study (210°C) provided the highest smoothness with the best shape stability and dimension accuracy. Interestingly, the tensile toughness and elongation at break of 3D printed specimens were found to be higher than those of compression-molded specimens for all filament types. This could be explained by the ability of the 3D printing technique to produce specimens that aligned in the printing direction in a fiber-like pattern.

Pd-doped flower like magnetic MnFe2O4 spinel ferrit nanoparticles: Synthesis, structural characterization and catalytic performance in the hydrazine-borane methanolysis

MnFe2O4 nanoparticles, an important member of nanostructured transition metal oxides, also known as spinel ferrites, find wide use in many applications, especially medicine and catalysis, due to their fascinating properties. In this research article, MnFe2O4 and MnFe2O4 supported Pd0 nanoparticles were primarily prepared by solvothermal and impregnation/reduction methods, respectively. After the structural and morphological examination of the MnFe2O4 decorated Pd0 nanoparticles (Pd@MnFe2O4), their catalytic activity was tested in the release of hydrogen from the methanolysis of hydrazine-borane. The morphological characterizations of Pd@MnFe2O4 catalyst show that Pd0 nanoparticles were successfully decorated (average particle size ∼2.83 nm) on the flower-like MnFe2O4 spinel ferrite nanoparticles. The Pd@MnFe2O4 catalyst presented high catalytic performance in the H2 production via the methanolysis of hydrazine-borane, and the turn-over frequency value at 298 K was 47.2 min−1, which is the highest catalytic systems in the literature for this purpose. The Pd@MnFe2O4 catalyst displaying good activity in ten consecutive catalytic runs can be considered a good heterogeneous catalyst for the hydrogen production from the methanolysis of hydrazine-borane.

Morphological Diagram of Dynamic-Interfacial-Release-Induced Surface Instability.

In this study, a morphological diagram was constructed for quantitatively predicting various modes of surface instabilities caused by the dynamic interfacial release of strain in initially flat bilayer composites comprising an elastomer and a capping layer. Theory, experiment, and simulation were combined to produce the diagram, which enables systematic generation of the following instability patterns: wrinkle, fold, period-double, delamination, and coexisting patterns. The pattern that forms is most strongly affected by three experimental parameters: the elastic modulus of the elastomer, the elastic modulus of the capping layer, and the thickness of the capping layer. The morphological diagram offers understanding of the formation of complex patterns and development of their applications. Critically, the wrinkle alignment can be well controlled by changing the direction of the interfacial release to enable the creation of centimeter-sized and highly ordered lamellar wrinkled patterns with a single orientation on a soft elastomer without the need for costly high-vacuum instruments or complex fabrication processes. The method and diagram have great potential for broad use in many practical applications ranging from flexible electronic devices to smart windows.

A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation

Accurately predicting the power produced during solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for its balanced operation and optimized dispatch and reduces operating costs. Solar PV power generation depends on the weather conditions, such as temperature, relative humidity, rainfall (precipitation), global solar radiation, wind speed, etc., and it is prone to large fluctuations under different weather conditions. Its power generation is characterized by randomness, volatility, and intermittency. Recently, the demand for further investigation into the uncertainty of short-term solar PV power generation prediction and its effective use in many applications in renewable energy sources has increased. In order to improve the predictive accuracy of the output power of solar PV power generation and develop a precise predictive model, the authors used predictive algorithms for the output power of a solar PV power generation system. Moreover, since short-term solar PV power forecasting is an important aspect of optimizing the operation and control of renewable energy systems and electricity markets, this review focuses on the predictive models of solar PV power generation, which can be verified in the daily planning and operation of a smart grid system. In addition, the predictive methods identified in the reviewed literature are classified according to the input data source, and the case studies and examples proposed are analyzed in detail. The contributions, advantages, and disadvantages of the predictive probabilistic methods are compared. Finally, future studies on short-term solar PV power forecasting are proposed.

Magnesium production by molten salt electrolysis with liquid tin cathode and multiple effect distillation.

Low-cost clean primary production of magnesium metal is important for its use in many applications, from light-weight structural components to energy technologies. This work describes new experiments and cost and emissions analysis for a magnesium metal production process. The process combines molten salt electrolysis of MgO using MgF₂-CaF₂ electrolyte and a reactive liquid tin cathode, with gravity-driven multiple effect thermal system (G-METS) distillation to separate out the magnesium product, and re-use of the tin. Electrolysis experiments with carbon anodes showed current yield above 90%, while a yttria-stabilized zirconia solid oxide membrane (SOM) anode experiment showed 84% current yield. G-METS distillation is an important component of the envisioned process. It can potentially lower costs and energy use considerably compared with conventional magnesium distillation. Techno-economic analysis including detailed mass and energy balances shows that this electrolyte composition could lower costs by utilizing CaO, which is the primary impurity in MgO, as the Hall-Héroult process uses the sodium impurity in alumina. Analysis options include: raw material types (magnesite rock vs. brine or seawater), drying and calcining using electricity vs. natural gas, and carbon vs. SOM anode type. Using SOM inert anodes results in a cost premium around 10%-15%, mostly due to higher electrical energy usage resulting from membrane resistance, and reduces GHG emissions by approximately 1kg CO₂/kg Mg product. Capital and operating cost estimates, and cradle to gate greenhouse gas (GHG) emissions analysis under several raw material and process technology scenarios, show comparable costs and emissions to those of aluminum production.

Effect of austenite fraction and stability on strength-hardening-ductility in additively manufactured 17-4 PH stainless steel containing nitrogen

Additively-manufactured (AM) 17-4 precipitation-hardening (PH) martensitic stainless steel (SS) built from nitrogen-atomized powder often retains a large volume fraction of austenite. The retained austenite lowers the yield strength compared to both wrought and AM 17-4 SS built from argon-atomized powder, hindering its use in many applications. However, retained austenite allows for high work hardening and high ductility through deformation-induced martensitic transformation (DIMT). This work systematically investigates the influence of various heat treatments on the volume fraction and stability of the retained austenite from tensile loading in AM 17-4 SS containing a mass fraction of 0.13% nitrogen (AM 17-4N SS). Synchrotron-based high-energy x-ray diffraction (HEXRD) is used to quantify the austenite volume fraction resulting from the heat treatments and the extent of DIMT in tensile specimens loaded to various plastic strains and specimens loaded to fracture. We show that by altering the volume fraction and stability of the retained austenite in AM 17-4N SS using different heat treatments, the DIMT process can be utilized to vary the strength, work hardening, and ductility, thus enabling its use in a wide range of applications. The AM 17-4N SS is also shown to meet or exceed the peak aged requirements for wrought 17-4 SS with yield strength (YS) of >1200 MPa, an ultimate tensile strength (UTS) of >1500 MPa, and a uniform elongation of ≥13% while retaining ≈ 20% volume fraction of austenite.

A Comparative Study of Two Common Pump-Controlled Hydraulic Circuits for Single-Rod Actuators

Pump-controlled hydraulic circuits are proven to be more efficient than conventional valve-controlled circuits. Pump-controlled hydraulic circuits for double rod cylinders are well developed and are in use in many practical applications. Existing pump-controlled circuits for single-rod actuators experience oscillation issues under specific operating conditions; that is identified as a critical operating zone on the load-velocity plane. The challenge in these circuits is to find out the proper way to compensate for the differential flow at both sides of the cylinder in all operating conditions. The two main types of valves commonly used by researchers, to compensate for differential flow in single-rod cylinder circuits, are: pilot-operated check valves, and shuttle valves. In this research, a performance comparison between circuits equipped with either valves, in terms of the size of the critical zone and the oscillations’ characteristics, was accomplished. Simulation studies showed that the circuits that utilize pilot-operated check valves possesses smaller oscillatory zones and less severe oscillations, when compared to circuits with shuttle valves. Experimental work verified the simulation results and proved the accuracy of the mathematical models. Hence, pump-controlled circuits with pilot-operated check valves are recommended to be the basic platform for further efforts to solve the oscillation problem in pump-controlled circuits.

Input Distribution Coverage: Measuring Feature Interaction Adequacy in Neural Network Testing

Testing deep neural networks (DNNs) has garnered great interest in the recent years due to their use in many applications. Black-box test adequacy measures are useful for guiding the testing process in covering the input domain. However, the absence of input specifications makes it challenging to apply black-box test adequacy measures in DNN testing. The Input Distribution Coverage (IDC) framework addresses this challenge by using a variational autoencoder to learn a low dimensional latent representation of the input distribution, and then using that latent space as a coverage domain for testing. IDC applies combinatorial interaction testing on a partitioning of the latent space to measure test adequacy. Empirical evaluation demonstrates that IDC is cost-effective, capable of detecting feature diversity in test inputs, and more sensitive than prior work to test inputs generated using different DNN test generation methods. The findings demonstrate that IDC overcomes several limitations of white-box DNN coverage approaches by discounting coverage from unrealistic inputs and enabling the calculation of test adequacy metrics that capture the feature diversity present in the input space of DNNs.

Graphene Strain-Effect Transistor with Colossal ON/OFF Current Ratio Enabled by Reversible Nanocrack Formation in Metal Electrodes on Piezoelectric Substrates

Extraordinarily high carrier mobility in graphene has led to many remarkable discoveries in physics and at the same time invoked great interest in graphene-based electronic devices and sensors. However, the poor ON/OFF current ratio observed in graphene field-effect transistors has stymied its use in many applications. Here, we introduce a graphene strain-effect transistor (GSET) with a colossal ON/OFF current ratio in excess of 107 by exploiting strain-induced reversible nanocrack formation in the source/drain metal contacts with the help of a piezoelectric gate stack. GSETs also exhibit steep switching with a subthreshold swing (SS) < 1 mV/decade averaged over ∼6 orders of magnitude change in the source-to-drain current for both electron and hole branch amidst a finite hysteresis window. We also demonstrate high device yield and strain endurance for GSETs. We believe that GSETs can significantly expand the application space for graphene-based technologies beyond what is currently envisioned.

Pyroelectric infrared detectors and materials—A critical perspective

Pyroelectric infrared detectors (PIRDs) have a number of advantages over other IR sensors, including room-temperature operation, wide wavelength sensitivity, and low cost, leading to their use in many applications and a market expected to reach U.S.$68 million by 2025. Physical models that can be used to accurately predict the performances of PIRDs of different types are reviewed in detail. All polar dielectrics exhibit the pyroelectric effect, so there are many materials potentially available for use in PIRDs. Traditionally, a range of “figures-of-merit” (FoMs) are employed to aid the selection of the best material to use in a given application. These FoMs, and their utility in determining how a given pyroelectric material will behave in a PIRD, are reviewed in the light of the physical models and the availability of dielectric data, which cover the frequency ranges of greatest interest for PIRDs (0.1–100 Hz). The properties of several pyroelectric materials are reviewed, and models are derived for their dielectric properties as functions of frequency. It is concluded, first, that the availability of full-frequency dielectric data is highly desirable if accurate predictions of device performance are to be obtained from the models and that second, the FoMs have practical utility in only very limited circumstances. Thus, they must be used with considerable care and circumspection. The circumstances under which each FoM is likely to give a good prediction for utility are discussed. The properties of some recently researched pyroelectric materials, including lead-containing single crystals in the Pb[(Mg⅓Nb⅔)xTi1−x]O3 system and Na½Bi½TiO3–K½Bi½TiO3 based lead-free crystals and ceramics, are reviewed in the light of this, and their properties and potential for device applications compared with the industry-standard material, LiTaO3. It is concluded that while there is potential for significant device performance improvements by using improved materials, especially with the PMN-PT-based materials, factors such as temperature stability, uniformity, and ease-of-processing are at least as important as device performance in determining material utility. The properties reported for the new lead-free materials do not, as yet, promise a performance likely to compete with LiTaO3 for mm-scale detectors, a material that is both readily available and lead-free.

Data-Independent Phase-Only Beamforming of FDA-MIMO Radar for Swarm Interference Suppression

This paper proposes two data-independent phase-only beamforming algorithms for frequency diverse array multiple-input multiple-output radar against swarm interference. The proposed strategy can form a deep null at the interference area to achieve swarm interference suppression by tuning the phase of the weight vector, which can effectively reduce the hardware cost of the receiver. Specifically, the first algorithm imposes constant modulus constraint and sidelobe level constraint, and the phase-only weight vector is solved. The second algorithm performs a constant modulus decomposition of the weight vector to obtain two phase-only weight vectors, and uses two parallel phase shifters to synthesize one beamforming weight. Both methods can obtain the phase-only weight to realize suppression for swarm interference. Simulation results demonstrate that our strategy shows superiority in beam shape, output signal-to-interference-noise ratio, and phase shifter quantization performance, and has the potential for use in many applications, such as radar countermeasures and electronic defense.

A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data

In data analysis, recognizing unusual patterns (outliers’ analysis or anomaly detection) plays a crucial role in identifying critical events. Because of its widespread use in many applications, it remains an important and extensive research brand in data mining. As a result, numerous techniques for finding anomalies have been developed, and more are still being worked on. Researchers can gain vital knowledge by identifying anomalies, which helps them make better meaningful data analyses. However, anomaly detection is even more challenging when the datasets are high-dimensional and multivariate. In the literature, anomaly detection has received much attention but not as much as anomaly detection, specifically in high dimensional and multivariate conditions. This paper systematically reviews the existing related techniques and presents extensive coverage of challenges and perspectives of anomaly detection within high-dimensional and multivariate data. At the same time, it provides a clear insight into the techniques developed for anomaly detection problems. This paper aims to help select the best technique that suits its rightful purpose. It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. Finally, it would be a line of future studies to extend by comparing the methods on other domain-specific datasets and offering a comprehensive anomaly interpretation in describing the truth of anomalies.

Unintentional formation of nitrate and nitrite ions during nanodiamonds sonication: A source of radical and electron scavengers

Ever since studies on diamond nanoparticles (NDs) expanded, their exceptional colloidal stability was investigated as it is a key parameter for their further use in many applications. While a sonication step is required to deaggregate NDs and obtain stable aqueous suspensions, few works focused on the impact of the sonication process on the suspension composition. High-powered sonication induces the formation of a variety of species by radical pathways including nitrite and nitrate ions, which may act as radical or electron scavengers. Such effect may significantly affect the expected photocatalytic or radiolytic properties of NDs. This work aims to warn about the formation of these ionic species during the deagglomeration of NDs by sonication. Our investigation reveals for the first time that for oxidized detonation NDs, such treatment promotes the formation of additional NO2- ions and hydroxyl radicals, suggesting an increase in cavitation bubbles formation. Hydrogenated particles on the opposite present no overproduction of nitrite ions. Our results also show that these species can be avoided in suspension by sonicating under controlled atmosphere with no significant perturbation of NDs colloidal stability or surface chemistry.

Oxygen Adsorption on Polar and Non-Polar Zn:ZnO Heterostructures from First Principles.

Zn:ZnO nanostructures have been studied extensively due to their potential use in many applications, such as oxygen scavengers for food packaging applications. Under atmospheric conditions, ZnO grows on the surface of Zn via an oxidation process. The mechanisms governing Zn oxidation are still not fully understood, with classical oxidation models, such as the Cabrera Mott, underestimating the oxide thickness of Zn:ZnO core-shell structures. In this work, Ab initio DFT calculations were performed to assess the adsorption properties of oxygen molecules on Zn:ZnO heterostructures to help elucidate the mechanisms involved in the growth of a ZnO film on a Zn substrate. Results suggest that the charge transfer mechanism from the Zn:ZnO heterostructures to the adsorbed oxygen layer can be promoted by two different processes: the electronic doping of ZnO due to the formation of the Zn:ZnO interface and the excess surface charge due to the presence of dangling bonds on the as cleaved ZnO.

IoT smart city framework using AI for urban sound classification

The advances of artificial intelligence approaches for automatically extracting and classifying disturbing sounds have great potential and application in the development of smart cities. In this paper, an urban sound event classification system based on deep learning technologies has been created by using the MEL-frequency cepstral coefficients as feature extractors and the Convolutional Neural Networks as classifiers. The designed system was trained and tested using the UrbanSound8K dataset which resulted in high classification accuracy of 92.67% of the tested results. In addition to this, validation of unknown sound events was applied. Furthermore, we propose the creation of a stand-alone system capable of recording and classifying sounds within an urban environment and sending the classification data to the cloud. The implementation of such units could result in real-time sound classification which can be used in smart cities based on the Internet of Things technology. According to this, an IoT smart city framework using AI for urban sound classification was proposed. The created system could find its use in many applications by making contribution in creating a feasible and deployable real-time sound classification system.

DFT perspective of gas sensing properties of Fe-decorated monolayer antimonene

The first-principles study was utilized to study the adsorption behavior of the toxic gases (CO, COS, NO, NO2, NH3, and SO2) on the Fe-decorated antimonene (Sb) monolayer. Antimonene is extensively researched for use in many applications because of its amazing features. The adsorption of gases on the Fe-decorated Sb monolayer showed that it is energetically favorable. CO, COS, NO, and NO2 gases showed chemisorption behavior while NH3 and SO2 showed physisorption. The decoration of Fe on Sb changed its semiconducting behavior to metallic. The higher adsorption energy values (-2.35 eV, −2.31 eV, −3.69 eV, −2.19 eV, −0.24 eV, and −1.54 eV) of the gases (CO, COS, NO, NO2, NH3, and SO2) on Fe-decorated Sb concluded that the material is preferable than Pure Sb. Strong hybridization was seen between the d-orbital of Fe-decorated Sb and the gas molecules in the valance band resulting in improved electronic properties. Structural properties, adsorption analysis, transfer of charge, the density of states (DOS), and analysis of frequency shift, transmission, and recovery time were investigated in detail. All the gases showed a small desorption time except NO while studying the recovery time analysis and suggested that the material appeared as a reversible sensor.

Bioplastics: Innovation for Green Transition.

Bioplastics are one of the possible alternative solutions to the polymers of petrochemical origins. Bioplastics have several advantages over traditional plastics in terms of low carbon footprint, energy efficiency, biodegradability and versatility. Although they have numerous benefits and are revolutionizing many application fields, they also have several weaknesses, such as brittleness, high-water absorption, low crystallization ability and low thermal degradation temperature. These drawbacks can be a limiting factor that prevents their use in many applications. Nonetheless, reinforcements and plasticizers can be added to bioplastic production as a way to overcome such limitations. Bioplastics materials are not yet studied in depth, but it is with great optimism that their industrial use and market scenarios are increasing; such growth can be a positive driver for more research in this field. National and international investments in the bioplastics industry can also promote the green transition. International projects, such as EcoPlast and Animpol, aim to study and develop new polymeric materials made from alternative sources. One of their biggest problems is their waste management; there is no separation process yet to recycle the nonbiodegradable bioplastics, and they are considered contaminants when mixed with other polymers. Some materials use additives, and their impact on the microplastics they leave after breaking apart is subject to debate. For this reason, it is important to consider their life cycle analysis and assess their environmental viability. These are materials that can possibly be processed in various ways, including conventional processes used for petrochemical ones. Those include injection moulding and extrusion, as well as digital manufacturing. This and the possibility to use these materials in several applications is one of their greatest strengths. All these aspects will be discussed in this review.

Current Status and Perspectives on the Green Synthesis of Succinic Polyesters for Value‐Added Applications

AbstractPolybutylene succinate (PBS) and other succinic (co)polyesters are biodegradable polymers with favorable mechanical and thermal properties that find use in many applications. Due to environmental concerns, polymers based on succinic acid (SA) have been gaining attention, as SA can be produced through biotechnological processes. Thus, this review aims to highlight the synthesis and characteristics of PBS and other succinic copolyesters, with emphasis in the works employing metallic catalysts and enzymes. In addition, the modification of the macromolecular structure by copolymerization or postpolymerization is also discussed. Currently, metallic catalysts are normally used in the synthesis of these materials, under conditions of high temperatures, which can favor the occurrence of thermal degradation, increasing the dispersion of chain length distributions. Moreover, the incrustation of metallic catalysts in polymeric materials makes their application in biomedical products difficult, due to toxicity requirements. In this context, enzymatic catalysis is gaining ground, offering milder synthesis temperatures, high selectivity, and uniformity of synthesized products. This biotechnological route can substitute oligomerization processes with metallic catalysis in future industrial processes, producing materials free from metallic contamination. In addition to production by catalytic routes, trends for future applications of succinic (co)polyesters are presented, with emphasis on the value‐added materials sectors.

Phase Morphology, Mechanical, and Thermal Properties of Calcium Carbonate-Reinforced Poly(L-lactide)-b-poly(ethylene glycol)-b-poly(L-lactide) Bioplastics

Poly(L-lactide) (PLLA) is a promising candidate as a bioplastic because of its non-toxicity and biodegradability. However, the low flexibility of PLLA limits its use in many applications. Poly(L-lactide)-b-poly(ethylene glycol)-b-poly(L-lactide) (PLLA-b-PEG-b-PLLA) block copolymer is of interest for bioplastic applications due to its superior flexibility compared to PLLA. The aim of this work is to modify PLLA-b-PEG-b-PLLA using a low-cost calcium carbonate (CaCO3) filler to improve material properties compared to PLLA/CaCO3 composites. The addition of CaCO3 enhanced the crystallinity and thermal stability for the PLLA-b-PEG-b-PLLA matrix but not for the PLLA matrix, as determined by differential scanning calorimetry (DSC), X-ray diffractometry (XRD), and thermogravimetric analysis (TGA). Phase morphology investigation using scanning electron microscopy (SEM) revealed that the interfacial adhesion between PLLA-b-PEG-b-PLLA and CaCO3 was stronger than between PLLA and CaCO3. Additionally, tensile testing was carried out to determine the mechanical properties of the composites. With the addition of CaCO3, the tensile stress and Young's modulus of the PLLA-b-PEG-b-PLLA matrix were increased, whereas these properties of the PLLA matrix were significantly decreased. Thus, CaCO3 shows great promise as an inexpensive filler that can induce nucleation and reinforcing effects for PLLA-b-PEG-b-PLLA bioplastics.

Solvatochromic and Aggregation-Induced Emission Active Nitrophenyl-Substituted Pyrrolidinone-Fused-1,2-Azaborine with a Pre-Twisted Molecular Geometry.

Boron-nitrogen-containing heterocycles with extended conjugated π-systems such as polycyclic aromatic 1,2-azaborines, hold the fascination of organic chemists due to their unique optoelectronic properties. However, the majority of polycyclic aromatic 1,2-azaborines aggregate at high concentrations or in the solid-state, resulting in aggregation-caused quenching (ACQ) of emission. This practical limitation poses significant challenges for polycyclic aromatic 1,2-azaborines' use in many applications. Additionally, only a few solvatochromic polycyclic aromatic 1,2-azaborines have been reported and they all display minimal solvatochromism. Therefore, the scope of available polycyclic 1,2-azaborines needs to be expanded to include those displaying fluorescence at high concentration and in the solid-state as well as those that exhibit significant changes in emission intensity in various solvents due to different polarities. To address the ACQ issue, we evaluate the effect of a pre-twisted molecular geometry on the optoelectronic properties of polycyclic aromatic 1,2-azaborines. Specifically, three phenyl-substituted pyrrolidinone-fused 1,2-azaborines (PFAs) with similar structures and functionalized with diverse electronic moieties (-H, -NO2, -CN, referred to as PFA 1, 2, and 3, respectively) were experimentally and computationally studied. Interestingly, PFA 2 displays two distinct emission properties: 1) solvatochromism, in which its emission and quantum yields are tunable with respect to solvent polarity, and 2) fluorescence that can be completely "turned off" and "turned on" via aggregation-induced emission (AIE). This report provides the first example of a polycyclic aromatic 1,2-azaborine that displays both AIE and solvatochromism properties in a single BN-substituted backbone. According to time-dependent density function theory (TD-DFT) calculations, the fluorescence properties of PFA 2 can be explained by the presence of a low-lying n-π* charge transfer state inaccessible to PFA 1 or PFA 3. These findings will help in the design of future polycyclic aromatic 1,2-azaborines that are solvatochromic and AIE-active as well as in understanding how molecular geometry affects these compounds' optoelectronic properties.