Engineering Applications Research Articles

ATPSpin: A Single Microfluidic Platform that Produces Diversified ATPS-Alginate Microfibers.

Microfluidic spinning is emerging as a useful technique in the fabrication of alginate fibers, enabling applications in drug screening, disease modeling, and disease diagnostics. In this paper, by capitalizing on the benefits of aqueous two-phase systems (ATPS) to produce diverse alginate fiber forms, we introduce an ATPS-Spinning platform (ATPSpin). This ATPS-enabled method efficiently circumvents the rapid clogging challenges inherent to traditional fiber production techniques by regulating the interaction between alginate and cross-linking agents like Ba2+ ions. By varying system parameters under the guidance of a regime map, our system produces several fiber forms─solid, hollow, and droplet-filled─consistently and reproducibly from a single device. We demonstrate that the resulting alginate fibers possess distinct features, including biocompatibility. We also encapsulate HEK293 cells in the microfibers as a proof-of-concept that this versatile microfluidic fiber generation platform may have utility in tissue engineering and regenerative medicine applications.

Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review

High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.

Ultrasound-Responsive Aligned Piezoelectric Nanofibers Derived Hydrogel Conduits for Peripheral Nerve Regeneration.

Nerve guidance conduits (NGCs) have been considered as promising treatment strategy and frontier trend for peripheral nerve regeneration; while their therapeutic outcomes are limited by the lack of controllable drug delivery and available physicochemical cues. Herein, we propose novel aligned piezoelectric nanofibers derived hydrogel NGCs with ultrasound (US)-triggered electrical stimulation (ES) and controllable drug release for repairing peripheral nerve injury. The inner layer of the NGCs was the barium titanate piezoelectric nanoparticles (BTNPs)-doped polyvinylidene fluoride-trifluoroethylene [BTNPs/P(VDF-TrFE)] electrospinning nanofibers with improved piezoelectricity and aligned orientation. The outer side of the NGCs was the thermoresponsive poly(N-isopropylacrylamide) (pNIPAM) hybrid hydrogel with bioactive drug encapsulation. Such NGCs could not only induce neuronal oriented extension and promote neurite outgrowth with US-triggered wireless ES, but also realize the controllable nerve growth factor (NGF) release with the hydrogel shrinkage under US-triggered heating. Thus, the NGC could positively accelerate the functional recovery and nerve axonal regeneration of rat models with long sciatic nerve defects. We believe that the proposed US-responsive aligned piezoelectric nanofibers derived hydrogel NGCs will find important applications in clinic neural tissue engineering. This article is protected by copyright. All rights reserved.

Performance analysis of dual material control gate cavity on source electrically doped TFET biosensor for biomedical applications

The fabrication complexity, low sensitivity, detection speed, and costs associated with nanoscale devices have been significant concerns in the development of label-free biosensors. To address these issues, we report a novel dual material control gate cavity on source electrically doped tunnel field-effect transistor (DM-CG-CS-ED-TFET) for label-free biosensors. In this regard, the n+ drain and p+ source regions within the proposed device are induced by applying polarity gate (PG) bias voltages of PG-1 = +1.2 V and PG-2 = −1.2 V, respectively, across the respective polarity gate electrodes. This approach not only overcomes doping control issues but also avoids thermal budget constraints and minimizes fabrication complexity when compared to conventional TFET. For biomolecule sensing in the device, a nanogap cavity is created within the gate dielectric by selectively etching a portion of the polarity gate dielectric layer towards the source side. The performance of the proposed biosensor device is evaluated based on the variations in carrier concentration profile, energy band diagram, electric field, transfer (IDS - VGS) characteristics, drain current (IDS) sensitivity, ON-state current (ION) sensitivity, switching ratio (ION/IOFF) and subthreshold swing (SS) sensitivity. The sensitivity of the device is also investigated based on nano-cavity dimensions, practical challenges such as various filling factors, and the step profile generated from the steric hindrance. Moreover, the effect of temperature on sensitivity has also been investigated. For this, various biomolecules including Streptavidin (k = 2.1), APTES (k = 3.57), ferrocytochrome c (k = 4.7), keratin (k = 8) and Gelatin (k = 12), have been investigated for their performance using Silvaco ATLAS device simulator. The simulation results demonstrate that the proposed biosensor is a viable option for biosensing applications in biomedical engineering.

Seismic behavior of double-cell CFST columns with table tennis racket-shaped cross section

Multicell concrete-filled steel tube (CFST) columns with excellent mechanical performance have been used in super high-rise buildings in recent years. According to the requirements of a 265-m tall building, the double-cell CFST columns with an innovative cross section of table tennis racket were applied. Hence, six columns were designed according to the prototype columns and subjected to low-frequency cyclic loading. The impacts of different shear-span ratios (SSRs), axial load ratios (ALRs), and steel ratios on the seismic behavior were elaborated and clarified. The test results indicated that the seismic performance varied in different loading directions owing to the asymmetric cross section, and the columns performed better in the positive loading direction resulted from earlier and more sufficient confinement of the circular steel tube, which was also reflected by the measured strain development. With SSR deceasing from 2.5 to 1.7, earlier and more severe damage was recorded. The resisting capacity and stiffness were significantly improved at a cost of ductility loss. Increasing ALR from 0.2 to 0.4 brought about more damage and worse deformability. However, a proper increase in axial load was beneficial to the resisting capacity and stiffness, and its influence on the restoration capacity was associated with SSR. Increasing the steel ratio (by 33.36%) could effectively postpone and alleviate the local buckling of steel tube, thereby comprehensively improving the seismic performance. A finite element (FE) analysis of such columns was performed to carry out the damage evolution and parametric study, and provide references for their application in practical engineering.

Effect of Nano-TiO2 and Polypropylene Fiber on Mechanical Properties and Durability of Recycled Aggregate Concrete

In order to promote the engineering application of recycled concrete, the effects of PPF and nano-TiO2 dioxide on the mechanical properties and durability of recycled concrete were studied.Polypropylene fiber recycled concrete(PRAC) and nano-TiO2 recycled concrete(TRAC) were prepared by adding different volume contents of PPF and nano-TiO2. The experimental findings demonstrated that the PPF and nano-TiO2 improved the splitting tensile strength of RAC better than the compressive strength. When the volume content of nano-TiO2. and PPF is 0.8% and 1.0%, respectively, the corresponding splitting tensile strength of concrete reaches the maximum value(3.4 and 3.7 MPa). The contribution rates of nano-TiO2 and PPF with different volume contents to the mechanical properties of RAC have optimal values, which are 0.4 and 1.0%, respectively. The incorporation of nano-TiO2 and PPF can effectively inhibit the loss of RAC mass and the generation of pores under freeze–thaw conditions, and slow down the decrease of dynamic elastic modulus. When the volume content of PPF is 1.0% and the volume content of nano-TiO2 is 0.4%, the protection effect on the internal structure of RAC is better, and its carbon resistance is better. The results of RSM model analysis and prediction show that both PPF and nano-TiO2 can be used as admixture materials to improve the mechanical properties and durability of RAC, and the comprehensive improvement effect of PPF on RAC performance is better than that of nano-TiO2.

Study on the plume self-neutralization of ionic liquid electrospray thruster based on median potential

Abstract Ionic liquid electrospray thruster (ILET) has the advantages of high specific impulse, precise thrust control, and low structural mass, which make it ideal for small satellites. The charged particles of ILET’s plume may lead to device charging or even damage, restricting its engineering applications. Thus, this paper examines the self-neutralization effectiveness of the ILET's plume under various emission conditions using particle-in-cell simulations. In order to accurately evaluate the self-neutralization effectiveness of the ILET’s plume, the median potential is explained in this paper and its reasonableness as the evaluation criterion for self-neutralization of the plume is verified. The working envelope for achieving self-neutralization of the ILET’s plume is determined by simulating the bipolar plume under various emission conditions. The results indicate that when the highest and lowest potentials are the same, the mean electric field strength between two points in space with a better degree of neutrality is 200% higher compared to points with a lesser degree of neutrality. The study determines the working envelope to realize self-neutralization of the ILET’s plume with an effectiveness of 70%. When the emission voltage of the anode thruster is fixed, the range of the cathode thruster’s voltage ranges from 108.36 V to 228.74 V. The asymmetry between the anode and cathode emissions of the ILET prototype significantly influences the operational range of the cathode thruster. Greater asymmetry leads to a narrower operating range for the ILET to achieve self-neutralization of the plume. This study serves as a guide for the ILET to achieve self-neutralization of the plume.

Applying Machine Learning to Earthquake Engineering: A Scientometric Analysis of World Research

Machine Learning (ML) has developed rapidly in recent years, achieving exciting advancements in applications such as data mining, computer vision, natural language processing, data feature extraction, and prediction. ML methods are increasingly being utilized in various aspects of seismic engineering, such as predicting the performance of various construction materials, monitoring the health of building structures or components, forecasting their seismic resistance, predicting potential earthquakes or aftershocks, and evaluating the residual performance of post-earthquake damaged buildings. This study conducts a scientometric-based review on the application of machine learning in seismic engineering. The Scopus database was selected for the data search and retrieval. During the data analysis, the sources of publications relevant to machine learning applications in seismic engineering, relevant keywords, influential authors based on publication count, and significant articles based on citation count were identified. The sources, keywords, and publications in the literature were analyzed and scientifically visualized using the VOSviewer software tool. The analysis results will help researchers understand the trending and latest research topics in the related field, facilitate collaboration among researchers, and promote the exchange of innovative ideas and methods.

Enhanced predictive optimization of methane dry reforming via ResponseSurface methodology and artificial neural network approaches: Insights using a novel nickel-strontium-zirconium-aluminum catalyst

This study investigates the molecular dynamics of methane dry reforming catalyzed by a novel nickel-strontium-zirconium-aluminum (5Ni+3Sr/10Zr+Al) catalyst, leveraging both Response Surface Methodology (RSM) and Radial Basis Function Neural Network (RBFNN) for predictive optimization. Focusing on the impact of operational parameters—hourly space velocity, reaction temperature, and CO2:CH4 mole ratio—on the conversion rates and formation of reaction components, we aim to predict optimal conditions and corresponding process variables. Through a comparison of a three-layer Feed Forward Neural Network, optimized at a 3:10:1 topology, with traditional RSM approaches, our findings highlight the superior predictive capabilities of ANN models. Notably, ANN demonstrated exceptional performance with Radj2and F_Ratio values significantly surpass those of RSM, alongside lower statistical error terms. This superiority is attributed to ANN's robust handling of nonlinear relationships between inputs and outputs, asserting its potential for enhancing predictive accuracy in chemical process optimization. At optimum predicted conditions like 1 CH4/CO2,750 °C reaction temperature, 12000 cm3g−1h−1 space velocity, NiSrZrAl outperformed with > 85 % CH4 and CO2 conversion with H2/CO ∼1 up to 20 h time on stream. Our research underscores the importance of integrating advanced modeling techniques for the efficient and accurate prediction of catalytic reactions, offering valuable insights for future applications in chemical engineering and catalysis.

Development of latex/zinc oxide compounds with antibacterial properties for applications in biomedical engineering

Natural rubber latex (NRL)—a polymer extracted from the rubber tree (Hevea brasiliensis)—has been used in multiple biomedical applications but does not have antibacterial properties. In this work, ZnO nanoparticles with two different morphologies were synthesized and added to NRL at different concentrations in order to evaluate the antibacterial properties of the resulting compounds. The characterization results obtained by electron microscopy and x-ray diffraction showed nanoparticles with spherical (mean size 69 ± 17 nm) and sheet morphology (mean size 154 ± 46 nm) with Wurtzite crystalline phase for both nanomaterials, respectively. The results of antibacterial tests showed that both compounds are effective against E. coli, and the reduction in bacterial viability was 90.3% and 96.4% for sheets and spherical nanomaterials, respectively. In the case of S. aureus, bacterial viability was reduced in both cases. The greatest antibacterial activity was evidenced in the nanoparticles with spherical morphology.

Multi-channel decentralized decoupling FxLMS algorithm and active vibration control experiment

When vibrations generated by marine machinery propagate through a ship’s hull into the ocean, they produce low-frequency radiated noise with distinct “acoustic fingerprint” characteristics. This noise, characterized by stable and concentrated energy, long transmission distances, and difficulty in elimination, becomes the primary target for enemy sonar detection. Active vibration isolation serves as a critical method for reducing low-frequency vibrations in ships and enhancing their acoustic stealth performance. However, control challenges persist, including multi-frequency excitation, frequency fluctuation, multi-channel coupling, and slow convergence speed. To address these issues, this paper introduced an innovative multi-channel decentralized decoupling filtered-x least mean square (DMFxLMS) algorithm. Firstly, a recursive least squares identification algorithm with a forgetting factor was proposed, taking into account the characteristics of single-input, multi-output and multi-input, and multi-output control systems, effectively enhancing the algorithm’s convergence speed and control accuracy. Secondly, based on the decentralized decoupling control concept, the multi-channel control system was simplified into parallel single-channel control loops. The control weight coefficient updates were only related to adjacent error signals, significantly reducing the algorithm’s computational complexity. Thirdly, an anti-impact link was designed to improve the algorithm’s robustness, considering the interference caused by other mechanical equipment during the control process. The influence of abnormal error signals in the control weight coefficient correction term was suppressed, and a percentage function was introduced to limit the output signal. Finally, the feasibility and effectiveness of the DMFxLMS algorithm were verified through simulations and experiments. The results demonstrated that the DMFxLMS algorithm achieved significant control effects for both constant frequency line spectrum excitation and frequency fluctuating line spectrum excitation, fulfilling the objective of reducing base vibration. The DMFxLMS algorithm exhibited fast convergence and excellent robustness, making it suitable for practical engineering applications.

Publisher Correction: An Injectable In Situ Forming Collagen/Alginate/CaSO4 Composite Hydrogel for Tissue Engineering Applications: Optimization, Characterization and In Vitro Assessments

Publisher Correction: An Injectable In Situ Forming Collagen/Alginate/CaSO4 Composite Hydrogel for Tissue Engineering Applications: Optimization, Characterization and In Vitro Assessments

Egg White Photocrosslinkable Hydrogels as Versatile Bioinks for Advanced Tissue Engineering Applications

AbstractThree dimensional (3D) bioprinting using photocrosslinkable hydrogels has gained considerable attention due to its versatility in various applications, including tissue engineering and drug delivery. Egg White (EW) is an organic biomaterial with excellent potential in tissue engineering. It provides abundant proteins, along with biocompatibility, bioactivity, adjustable mechanical properties, and intrinsic antiviral and antibacterial features. Here, a photocrosslinkable hydrogel derived from EW is developed through methacryloyl modification, resulting in Egg White methacryloyl (EWMA). Upon exposure to UV light, synthesized EWMA becomes crosslinked, creating hydrogels with remarkable bioactivity. These hydrogels offer adjustable mechanical and physical properties compatible with most current bioprinters. The EWMA hydrogels closely resemble the native extracellular matrix (ECM) due to cell‐binding and matrix metalloproteinase‐responsive motifs inherent in EW. In addition, EWMA promotes cell growth and proliferation in 3D cultures. It facilitates endothelialization when investigated with human umbilical vein endothelial cells (HUVECs), making it an attractive replacement for engineering hemocompatible vascular grafts and biomedical implants. In summary, the EWMA matrix enables the biofabrication of various living constructs. This breakthrough enhances the development of physiologically relevant 3D in vitro models and opens many opportunities in regenerative medicine.

3D-Printed Polycaprolactone-Based Containing Calcium Zirconium Silicate: Bioactive Scaffold for Accelerating Bone Regeneration

There is an essential clinical need to develop rapid process scaffolds to repair bone defects. The current research presented the development of calcium zirconium silicate/polycaprolactone for bone tissue engineering utilising melt extrusion-based 3D printing. Calcium zirconium silicate (CZS) nanoparticles were added to polycaprolactone (PCL) porous scaffolds to enhance their biological and mechanical properties, while the resulting properties were studied extensively. No significant difference was found in the melting point of the samples, while the crystallisation temperature points of the samples containing bioceramic increased from 36.1 to 40.2 °C. Thermal degradation commenced around 350 °C for all materials. According to our results, increasing the CZS content from 0 to 40 wt.% (PC40) in porous scaffolds (porosity about 55–62%) improved the compressive strength from 2.8 to 10.9 MPa. Furthermore, apatite formation ability in SBF solution increased significantly by enhancing the CZS percentage. According to MTT test results, the viability of MG63 cells improved remarkably (~29%) in PC40 compared to pure PCL. These findings suggest that a 3D-printed PCL/CZS composite scaffold can be fabricated successfully and shows great potential as an implantable material for bone tissue engineering applications.

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

Electric vertical takeoff and landing (eVTOL) aircraft represent a crucial aviation technology to transform future transportation systems. The unique characteristics of eVTOL aircraft include reduced noise, low pollutant emission, efficient operating cost, and flexible maneuverability, which in the meantime pose critical challenges to advanced power retention techniques. Thus, optimal takeoff trajectory design is essential due to immense power demands during eVTOL takeoffs. Conventional design optimizations, however, adopt high-fidelity simulation models in an iterative manner resulting in a computationally intensive mechanism. In this work, we implement a surrogate-enabled inverse mapping optimization architecture, i.e., directly predicting optimal designs from design requirements (including flight conditions and design constraints). A trained inverse mapping surrogate performs real-time optimal eVTOL takeoff trajectory predictions with no need for running optimizations; however, one training sample requires one design optimization in this inverse mapping setup. The excessive training cost of inverse mapping and the characteristics of optimal eVTOL takeoff trajectories necessitate the development of the regression generative adversarial network (regGAN) surrogate. We propose to further enhance regGAN predictive performance through the transfer learning (TL) technique, creating a scheme termed regGAN-TL. In particular, the proposed regGAN-TL scheme leverages the generative adversarial network (GAN) architecture consisting of a generator network and a discriminator network, with a combined loss of the mean squared error (MSE) and binary cross-entropy (BC) losses, for regression tasks. In this work, the generator employs design requirements as input and produces optimal takeoff trajectory profiles, while the discriminator differentiates the generated profiles and real optimal profiles in the training set. The combined loss facilitates the generator training in the dual aspects: the MSE loss targets minimum differences between generated profiles and training counterparts, while the BC loss drives the generated profiles to share analogous patterns with the training set. We demonstrated the utility of regGAN-TL on optimal takeoff trajectory designs for the Airbus A3 Vahana and compared its performance against representative surrogates, including the multi-output Gaussian process, the conditional GAN, and the vanilla regGAN. Results showed that regGAN-TL reached the 99.5% generalization accuracy threshold with only 200 training samples while the best reference surrogate required 400 samples. The 50% reduction in training expense and reduced standard deviations of generalization accuracy achieved by regGAN-TL confirmed its outstanding predictive performance and broad engineering application potential.

Experimental and finite element investigation on bolted connection in monolithic 3-dimensional cuff with pultruded box section

This article presents a concise and practical research study on the application of pultruded GFRP (Glass Fiber Reinforced Polymer) hollow sections in structural engineering. Experimental and computational investigations are conducted on the utilization of GFRP cuff components for joining tubular pultruded fiber-reinforced polymer (PFRP) beams and columns. Both adhesive bonding and bolted connections are explored for connecting PFRP box sections of beam-column interfaces. The study investigates twelve series of beam-to-column connections under monotonic loading conditions, varying cuff geometry. A finite element model is developed to represent the pultruded fiber-reinforced polymer box sections, columns, and cuff sections. The model’s accuracy in depicting frame stiffness and cuff damage patterns, considering different thicknesses and lengths of GFRP cuff connections, is validated against experimental test frame behavior. This research fills a gap in scientific inquiry regarding GFRP hollow profiles, providing valuable insights for structural engineering applications.

Development of Improved Confined Compression Testing Setups for Use in Stress Relaxation Testing of Viscoelastic Biomaterials

The development of cell-based biomaterial alternatives holds significant promise in tissue engineering applications, but it requires accurate mechanical assessment. Herein, we present the development of a novel 3D-printed confined compression apparatus, fabricated using clear resin, designed to cater to the unique demands of biomaterial developers. Our objective was to enhance the precision of force measurements and improve sample visibility during compression testing. We compared the performance of our innovative 3D-printed confined compression setup to a conventional setup by performing stress relaxation testing on hydrogels with variable degrees of crosslinking. We assessed equilibrium force, aggregate modulus, and peak force. This study demonstrates that our revised setup can capture a larger range of force values while simultaneously improving accuracy. We were able to detect significant differences in force and aggregate modulus measurements of hydrogels with variable degrees of crosslinking using our revised setup, whereas these were indistinguishable with the convectional apparatus. Further, by incorporating a clear resin in the fabrication of the compression chamber, we improved sample visibility, thus enabling real-time monitoring and informed assessment of biomaterial behavior under compressive testing.

3D-printed scaffolds for tissue engineering applications using thermosensitive hydrogels based on biopolymer blends

AbstractThree-dimensional (3D) printing is an emerging technology for the construction of complex 3D constructs used for tissue engineering applications. In this study, we are proposing the preparation of 3D printing hydrogel inks consisting of the synthetic polymers poly(caprolactone) and poly(lactic acid), the biopolymer chitosan, and naturally derived gelatin. In addition, pluronic F-127 was used to improve the miscibility between the hydrophobic and hydrophilic components due to its amphiphilic character, as well as for its good 3D printability. The printability of the hydrogel inks was optimized by varying the composition, the extrusion nozzle, and the temperature, while the integrity of the 3D scaffolds was secured via sol–gel transition. The produced hydrogels with PCL-pluronic-chitosan-gelatin/15-20-4-2 wt% (PC3.75-Pl5-CG) and PLA-pluronic-chitosan-gelatin/10-20-4-2 wt% (PL2.5-Pl5-CG) presented the best printability, producing smooth and uniform porous scaffolds. The prepared hydrogels were formed via the interactions between the polymers through hydrogen bonding. Additionally, the produced hydrogels exhibited temperature-dependent swelling behavior, and the scaffolds with PCL presented lower swelling capacity than the scaffolds with PLA. The produced scaffolds presented slower hydrolyzation rate in simulated body fluid (SBF) at 25 °C compared to 37 °C. Biological studies proved that the 3D-printed porous scaffolds were non-cytotoxic and promoted human adipose-derived mesenchymal stem cell adhesion. Graphical abstract

Stress Analysis of Transmission Shafts: A Multi-Faceted Investigation

Transmission shafts constitute pivotal components in mechanical systems, responsible for transferring power from the engine to various mechanisms. The integrity and longevity of these systems hinge on the robustness of transmission shafts, necessitating comprehensive stress analysis. This paper presents an in-depth exploration into stress analysis methodologies applied to transmission shafts. Analytical, numerical, and experimental approaches are scrutinized, with a focus on their efficacy, limitations, and advancements. Material properties, geometric configurations, loading conditions, and manufacturing intricacies are dissected to discern their influence on shaft behavior. Through this comprehensive examination, this paper endeavors to contribute to the advancement of transmission shaft design, ensuring reliability and performance across diverse engineering applications.

Enhanced Intrinsic Self-Healing Performance of Mussel Inspired Coating via In-Situ Cation Capture.

Under damp or aquatic conditions, the corrosion products deposited on micro-cracks/pore sites bring about the failure of intrinsically healable organic coatings. Inspired by mussels, a composite coating of poly (methyl methacrylate-co-butyl acylate-co-dopamine acrylamide)/phenylalanine-functionalized boron nitride (PMBD/BN-Phe) is successfully prepared on the reinforcing steel, which exhibits excellent anti-corrosion and underwater self-healing capabilities. The self-healing property of PMBD is derived from the synergistic effect of hydrogen bonding and metal-ligand coordination bonding, and thereby the continuous generation of corrosion products can be significantly suppressed through in situ capture of cations by the catechol group. Furthermore, the corrosion protection ability can be remarkably improved by the labyrinth effect of BN and the inhibition role of Phe, and the desired interfacial compatibility can be formed by the hydrogen bonds between BN-Phe and PMBD matrix. The corrosion current density (icorr) of PMBD/BN-Phe coating is determined as 7.95×10-11Acm-2. The low-frequency impedance modulus (|Z|f = 0.0 1 Hz is remained at 3.47×109Ωcm2, indicating an ultra-high self-healing efficiency (≈89.5%). It is anticipated to provide a unique strategy for development of an underwater self-healing coating and robust durability for application in anti-corrosion engineering of marine buildings.