Mitigating sub-synchronous oscillation using intelligent damping control of DFIG based on improved TD3 algorithm with knowledge fusion.

The occurrence of sub-synchronous oscillation (SSO) phenomenon in doubly-fed induction generators (DFIGs)-based wind turbines threatens the secure and stable operation of the power grid. Conventional sub-synchronous damping controllers encounter challenges in adapting to the dynamic operating conditions of power systems. This paper introduces an Intelligent Sub-Synchronous Damping Controller (I-SSDC) for DFIGs that integrates deep reinforcement learning (DRL) and knowledge to address the limitations of conventional methods for SSO mitigation. The initial step involves formulating a framework for I-SSDC using the improved twin delayed deep deterministic policy gradient (TD3) algorithm incorporating Softmax. Following this, a surrogate model is constructed, employing Weighted Linear Regression and regularization. This model is designed to identify the predominant influencing factors of SSO, focusing on the selection of the output signal (installation position) to optimize decision-making in I-SSDC. The objective is to enhance the controller's environmental adaptability and interpretability. Moreover, knowledge and experience related to SSOs are integrated into agent training to improve the exploration efficiency of the agent. Case studies under various operating conditions of the test power system validate the efficacy of the proposed I-SSDC in suppressing SSOs.

Learning an Autonomous Dynamic System to Encode Periodic Human Motion Skills.

Learning an autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for human motion skills transfer. However, most existing approaches focus on goal-directed motion skills transfer, and the study on periodic motion skills transfer is rare. One popular approach for periodic motion skills transfer is learning periodic dynamic movement primitive (DMP); however, periodic DMP is sensitive to spatial disturbances due to the introduction of the phase parameters. To solve this issue, this brief presents a novel approach to learn an ADS with a stable limit cycle without introducing phase parameters. First, a data-driven Lyapunov function (energy function) is learned, such that one of its level surfaces is consistent with periodic human demonstration trajectories. Then, an ADS is learned by sequentially solving energy function-related constrained optimization problems. With a proper design of constraint functions, we can ensure that the trajectory generated by the ADS will converge to an energy function-level surface, of which the shape is similar to periodic human demonstration trajectories. Experiments are conducted to show the effectiveness of the proposed approach (PA).

Blockade of DDR1/PYK2/ERK signaling suggesting SH2 superbinder as a novel autophagy inhibitor for pancreatic cancer.

Pancreatic cancer is highly lethal, of which 90% is pancreatic ductal adenocarcinoma (PDAC), with a 5-year survival rate of less than 12%, lacking effective treatment options and late diagnosis. Furthermore, the tumors show an intense resistance to cytotoxic chemotherapies. As autophagy is elevated in PDAC, targeting the autophagic pathway is regarded as a promising strategy for cancer treatment. Immunofluorescence and transmission electron microscopy were utilized to assess the autophagic flux. Label-free quantitative phosphoproteomics was used to figure out critically altered tyrosine phosphorylation of the proteins. Tumor-bearing mice were used to validate that SH2 TrM-(Arg)9 restrained the growth of tumor cells. SH2 TrM-(Arg)9 inhibited collagen-induced autophagy via blocking the DDR1/PYK2/ERK signaling cascades. SH2 TrM-(Arg)9 improved the sensitivity of PANC-1/GEM cells to gemcitabine (GEM). Inhibition of autophagy by SH2 TrM-(Arg)9 may synergized with chemotherapy and robusted tumor suppression in pancreatic cancer xenografts. SH2 TrM-(Arg)9 could enter into PDAC cells and blockade autophagy through inhibiting DDR1/PYK2/ERK signaling and may be a new treatment strategy for targeted therapy of PDAC.

Aromatized liposomes for sustained drug delivery.

Insufficient drug loading and leakage of payload remain major challenges in designing liposome-based drug delivery systems. These phenomena can limit duration of effect and cause toxicity. Targeting the rate-limiting step in drug release from liposomes, we modify (aromatized) them to have aromatic groups within their lipid bilayers. Aromatized liposomes are designed with synthetic phospholipids with aromatic groups covalently conjugated onto acyl chains. The optimized aromatized liposome increases drug loading and significantly decreases the burst release of a broad range of payloads (small molecules and macromolecules, different degrees of hydrophilicity) and extends their duration of release. Aromatized liposomes encapsulating the anesthetic tetrodotoxin (TTX) achieve markedly prolonged effect and decreased toxicity in an application where liposomes are used clinically: local anesthesia, even though TTX is a hydrophilic small molecule which is typically difficult to encapsulate. Aromatization of lipid bilayers can improve the performance of liposomal drug delivery systems.

Electrifying Carbon Capture by Developing Nanomaterials at the Interface of Molecular and Process Engineering.

ConspectusCarbon capture is an indispensable step toward closing the anthropogenic carbon cycle. However, the large-scale implementation of conventional thermochemical carbon capture technologies is hindered by their low energy efficiency, limited sorbent stability, and complexity in infrastructure integration. A mechanistically different alternative, commonly known as electrochemically mediated carbon capture (EMCC), has garnered increasing research traction over the past few years and relies on electrochemical stimuli instead of thermal or pressure swings for the capture and release of carbon dioxide (CO2). Compared to conventional methods, EMCC can be operated under mild conditions driven by intermittent renewable energy sources and has a flexible design to meet the multiscale demands of carbon capture, offering a potentially sustainable, energy-efficient, and cost-effective solution to CO2 concentration from dilute mixtures or the ambient environment.Nanomaterials have played a crucial role in carbon capture research. For instance, nanoporous materials can provide increased free volumes, surface areas, and active sites for carbon capture through physical or chemical adsorption from the gaseous phase. In contrast, EMCC relies on chemical absorption via acid-base interactions using solubilized CO2 in electrolytes. Therefore, most EMCC sorbents and mediators explored so far have been developed as molecules rather than nanomaterials. In recent years, our team has been focusing on electrifying the carbon capture processes at the molecular, materials, and process levels. We seek to address the most pressing issues associated with EMCC, either in fixed-bed or flow systems, that prevent their practical use. These issues include parasitic reactions with molecular oxygen, insufficient electrode capacity utilization, sorbent crossover, etc. To address these problems, there is an urgent need to develop rationally designed nanomaterials at the interface of molecular electrochemistry and device engineering. This Account provides an overview of recent progress on developing new chemistries and engineering batch/continuous processes for EMCC. We discuss the limitations of current EMCC technology and emphasize why nanomaterials are critical for electrifying carbon capture. First, we introduce the design principles for EMCC sorbents based on redox-active organic CO2 carriers and discuss metrics for their performance evaluation. Second, we showcase how molecular design can tackle problems of sorbent solubility, oxygen stability, and electrolyte compatibility in EMCC. Third, we discuss the early results of nanomaterials as solid sorbents in fixed-bed systems, nonswelling membranes for flow systems, and high-surface-area gas-liquid contactors. Finally, building on the foundation we established through our prior work, we offer perspectives on future directions for nanomaterials to help address the challenges in EMCC.

Neurodynamics-based distributed model predictive control of a low-speed two-stroke marine main engine power system.

This article studies a steady operation optimization problem of a low-speed two-stroke marine main engine (LTMME) power system including a cooling water subsystem, a fuel oil subsystem and a main engine subsystem with input and state constraints. Firstly, a distributed model with coupling inputs and states is established for the LTMME power system according to laws of thermodynamics and kinetics. Further, an optimization problem of the LTMME power system is formulated to ensure the system to operate steadily, subjected to constraint conditions of the distributed model and the input and state bounds. Moreover, the optimization problem is rewritten as a quadratic programming problem, and an iterative distributed model predictive control (DMPC) scheme based on a primal-dual neural network (PDNN) method is used to obtain the optimal inputs within the constrained range. Finally, based on the actual data from an underway ocean vessel named Mingzhou 501 with an LTMME power system, a group of simulations are carried out to verify the effectiveness of the proposed approach.

The area-reconstruction h-dome technique and its efficient Python implementation for improved particle size image analysis.

The traditional watershed segmentation methods usually suffer from over segmentation for irregularly shaped particles. This is because the distance map of an irregularly shaped particle contains multiple local maxima, and over segmentation would happen if these local maxima were used as seeds for watershed segmentation. In this work, several methods based on morphological reconstruction, including h-dome transform, h-maxima, and area-reconstruction h-dome transform, are introduced to merge, or erase redundant local maxima, and the performance of these methods in avoiding over segmentation is compared. The results show that the area-reconstruction h-dome transform is the most effective method in controlling over segmentation among the evaluated methods. However, the area-reconstruction h-dome transform is achieved by superposition of binary reconstructions at each grayscale level, which is extremely time-consuming and impractical for batch processing. A hybrid pixel-queue algorithm is applied to accelerate the area-reconstruction h-dome transform, and the algorithm is implemented in Cython to further improve the computational efficiency. For a 2592 × 1944 pixel image, on a PC with an Intel Core i5 2.4GHz processor and 8 GB RAM, the processing time of the area-reconstruction h-dome transform after acceleration is about 549 ms, which is 249 times faster than the unaccelerated algorithm and 4 times faster than the reconstruction function in the Scikit-image library (an open-source image processing library for the Python programming language) which performs reconstruction by dilation. The accelerated area-reconstruction h-dome transform algorithm was successfully applied to the segmentation of rubber particles in a thermoplastic polyolefin (TPO) compound. RESEARCH HIGHLIGHTS: Techniques for segmenting particles with irregular shapes based on morphological reconstruction are reviewed. A fast algorithm for area-reconstruction h-dome transform is introduced based on Vincent's first approach combined with the pixel queue algorithm and Cython acceleration. The accelerated reconstruction algorithm is 249 times faster than the unaccelerated algorithm. The fast area-reconstruction h-dome transform algorithm is successfully applied to rubber particle segmentation of a thermal plastic polyolefin.

Learning From Human Demonstrations for Wheel Mobile Manipulator: An Unscented Model Predictive Control Approach.

Industry 4.0 requires new production models to be more flexible and efficient, which means that robots should be capable of flexible skills to adapt to different production and processing tasks. Learning from demonstration (LfD) is considered as one of the promising ways for robots to obtain motion and manipulation skills from humans. In this article, a framework that enables a wheel mobile manipulator to learn skills from humans and complete the specified tasks in an unstructured environment is developed, including a high-level trajectory learning and a low-level trajectory tracking control. First, a modified dynamic movement primitives (DMPs) model is utilized to simultaneously learn the movement trajectories of a human operator's hand and body as reference trajectories for the mobile manipulator. Considering that the auxiliary model obtained by the nonlinear feedback is hard to accurately describe the behavior of mobile manipulator with the presence of uncertain parameters and disturbances, a novel model is established, and an unscented model predictive control (UMPC) strategy is then presented to solve the trajectory tracking control problem without violating the system constraints. Moreover, a sufficient condition guaranteeing the input to state practical stability (ISpS) of the system is obtained, and the upper bound of estimated error is also defined. Finally, the effectiveness of the proposed strategy is validated by three simulation experiments.

Blockade of phosphotyrosine pathways suggesting SH2 superbinder as a novel therapy for pulmonary fibrosis.

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive and irreversible fibrotic disease with high mortality. Currently, pirfenidone and nintedanib are the only approved drugs for IPF by the U.S. Food and Drug Administration (FDA), but their efficacy is limited. The activation of multiple phosphotyrosine (pY) mediated signaling pathways underlying the pathological mechanism of IPF has been explored. A Src homology-2 (SH2) superbinder, which contains mutations of three amino acids (AAs) of natural SH2 domain has been shown to be able to block phosphotyrosine (pY) pathway. Therefore, we aimed to introduce SH2 superbinder into the treatment of IPF. Methods: We analyzed the database of IPF patients and examined pY levels in lung tissues from IPF patients. In primary lung fibroblasts obtained from IPF patient as well as bleomycin (BLM) treated mice, the cell proliferation, migration and differentiation associated with pY were investigated and the anti-fibrotic effect of SH2 superbinder was also tested. In vivo, we further verified the safety and effectiveness of SH2 superbinder in multiple BLM mice models. We also compared the anti-fibrotic effect and side-effect of SH2 superbinder and nintedanib in vivo. Results: The data showed that the cytokines and growth factors pathways which directly correlated to pY levels were significantly enriched in IPF. High pY levels were found to induce abnormal proliferation, migration and differentiation of lung fibroblasts. SH2 superbinder blocked pY-mediated signaling pathways and suppress pulmonary fibrosis by targeting high pY levels in fibroblasts. SH2 superbinder had better therapeutic effect and less side-effect compare to nintedanib in vivo. Conclusions: SH2 superbinder had significant anti-fibrotic effects both in vitro and in vivo, which could be used as a promising therapy for IPF.

Environmental Regulation, Corporate Economic Performance and Spatial Technology Spillover: Evidence from China's Heavily Polluting Listed Corporations.

The relationship between environmental regulation, technology spillover, and economic performance has been the subject of intense scholarly debate in environmental economics for many years. The famous Porter hypothesis states that environmental regulation promotes both the economic performance and the environmental performance of corporations. However, the existing literature has paid relatively little attention to micro-level research and spatial spillover effects. This article endeavors to fill this gap by an empirical analysis of a sample of 900 of China's heavily polluting listed corporations for the period of 2013-2016. By utilizing spatial econometric methods to measure spatial direct and indirect effects and decomposing total factor productivity change into technical change, pure efficiency change, and scale efficiency change, we find that environmental regulation promotes corporate total factor productivity but widens the disparity between profitable and unprofitable corporations. Our results also suggest that the direct and indirect effects of environmental regulation and corporate profitability on promoting total factor productivity rely heavily on the efficiency changes, while the contribution of the key component, technical change, is insignificant.

Prolonged Retrobulbar Local Anesthesia of the Cornea Does Not Cause Keratopathy in Mice.

Purpose: Prolonged local anesthesia (PLA) of the cornea is currently assumed to cause neurotrophic keratitis and is strongly discouraged. We investigate whether PLA of the cornea per se causes neurotrophic keratitis. Methods: PLA of the cornea was induced in 12 female albino BALB/c mice by retrobulbar injection of a polymeric prodrug (PGS-TTX) where the site 1 sodium channel blocker tetrodotoxin (TTX) was slowly released from the polymer polyglycerol sebacate. The duration and depth of corneal anesthesia was monitored by the Cochet-Bonnet esthesiometer. Corneal injury from PLA was assessed by slit lamp examination with 2% sodium fluorescein dye, histology, corneal nerve density by immunohistochemistry with anti-ß III tubulin antibody and confocal microscopy, and corneal neurotrophin levels (substance P and neurokinin A) by an enzyme-linked immunosorbent assay. PLA was also induced by topical amitriptyline (80 mM), used as a positive control for local anesthetic-induced corneal injury. Frequent ocular lubrication was provided. Results: Retrobulbar PGS-TTX resulted in complete corneal anesthesia lasting 50.1 ± 3.6 hours and mean time to complete resolution of block of 55.1 ± 3.6 hours with no keratopathy provided lubrication was provided. Topical 80 mM amitriptyline induced complete corneal anesthesia for 24 hours and developed keratopathy. There was no difference in the histology, levels of corneal neurotrophins, and corneal nerve density between the retrobulbar PGS-TTX group and normal cornea. Conclusions: In the absence of topical toxicity or corneal exposure, PLA of the cornea per se does not cause keratitis. Translational Relevance: PLA of the cornea could be highly beneficial in acute and chronic painful corneal conditions.

A Hierarchical Data-Driven Predictive Control of Image-Based Visual Servoing Systems With Unknown Dynamics.

In this article, a hierarchical predictive control (PC) algorithm is designed for visual servoing mobile robot systems. At the kinematic level, the image-based visual servoing model of a wheeled mobile robot is established. By defining the corresponding performance index of the PC, an iterative linear quadratic regulator (iLQR) is used to obtain the velocity controller and to provide reference velocity for dynamics. In dynamics, a data-driven PC controller based on the Gaussian process (GP) is proposed to obtain the torque controller with unknown dynamics. The input-to-state practical stability (ISpS) of the system based on the proposed data-driven PC method is proved by introducing reasonable assumptions. The corresponding theorem also analyzes the maximum upper bound of GP inference error. Finally, the effectiveness of the proposed hierarchical controller is verified by simulations and experiments.

Src acts as the target of matrine to inhibit the proliferation of cancer cells by regulating phosphorylation signaling pathways.

Studies have shown that matrine has antitumor activity against many types of cancers. However, the direct target in cancer cells of its anticancer effect has not been identified. The purpose of this study was to find the molecular target of matrine to inhibit the proliferation of cancer cells and explore its mechanism of action. Herein we showed that matrine inhibited the proliferation of cancer in vitro and in vivo. Pull-down assay with matrine-amino coupling resins and liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) identified Src as the target of matrine. Cellular thermal shift assay (CETSA) and drug affinity responsive target stability (DARTS) provided solid evidences that matrine directly bound to Src. Bioinformatics prediction and pull-down experiment demonstrated that Src kinase domain was required for its interaction with matrine and Ala392 in the kinase domain participated in matrine-Src interaction. Intriguingly, matrine was proven to inhibit Src kinase activity in a non-ATP-competitive manner by blocking the autophosphorylation of Tyr419 in Src kinase domain. Matrine down-regulated the phosphorylation levels of MAPK/ERK, JAK2/STAT3, and PI3K/Akt signaling pathways via targeting Src. Collectively, matrine targeted Src, inhibited its kinase activity, and down-regulated its downstream MAPK/ERK, JAK2/STAT3, and PI3K/Akt phosphorylation signaling pathways to inhibit the proliferation of cancer cells.

Aptamer-SH2 superbinder-based targeted therapy for pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) exhibits the poorest prognosis of all solid tumors with a 5-year survival rate of less than 10% and a median survival of 6 months after diagnosis. Numerous targeted agents have been developed and evaluated to improve the survival benefit in patients with PDAC. Unfortunately, most agents have been proven futile mainly owing to the dense stroma and the sophisticated signaling pathways of PDAC. Here, we show the potent effectiveness of Aptamer-SH2 superbinder-(Arg)9 conjugate on the treatment of PDAC. In this conjugate, DNA aptamer selected against PDAC cell line confers the function of specifically recognizing and binding to the PDAC cells and activated pancreatic stellate cells (PSCs) in stroma; cell penetrating peptide (Arg)9 facilitates the intracellular delivery of fused proteins; SH2 superbinder conducts the drastic blockade of multiple phosphotyrosines (pY)-based signaling pathways in tumor cells. METHODS: PDAC-associated pY were reanalyzed by bioinformatics screen. XQ-2d and SH2 superbinder-(Arg)9 were crosslinked with BMH to form XQ-2d-SH2 CM-(Arg)9 conjugate. Immunofluorescence was utilized to assess the potency of the conjugate entering cells. MTT and wound healing assays were performed to evaluate the proliferation or migration of PANC-1 and BxPC-3 cells, respectively. Western blot and Pulldown assays revealed that conjugate influenced several pY-based signaling pathways. Tumor-bearing mice were used to validate XQ-2d-SH2 CM-(Arg)9, which restrained the growth and metastasis of cancer cells. RESULTS: XQ-2d-His-SH2 CM-(Arg)9 conjugate restrained proliferation, invasion, and metastasis of PDAC cells with potent efficacy via blocking the activity of several pY-related signaling cascades. XQ-2d-His-SH2 CM-(Arg)9 could eliminate the dense stroma of PDAC and then arrive at tumor tissues. CONCLUSIONS: XQ-2d-SH2 CM-(Arg)9 conjugate may efficiently destroy the pancreatic stroma and show potent antitumor efficacy with minimal toxic effect by regulating tumor cell proliferation and metastasis in vitro and in vivo, which makes it to be a promising targeted therapy of PDAC.

Water enables mild oxidation of methane to methanol on gold single-atom catalysts.

As a 100% atom-economy process, direct oxidation of methane into methanol remains as a grand challenge due to the dilemma between activation of methane and over-oxidation of methanol. Here, we report that water enabled mild oxidation of methane into methanol with >99% selectivity over Au single atoms on black phosphorus (Au1/BP) nanosheets under light irradiation. The mass activity of Au1/BP nanosheets reached 113.5 µmol gcatal-1 in water pressured with 33 bar of mixed gas (CH4:O2 = 10:1) at 90 °C under light irradiation (1.2 W), while the activation energy was 43.4 kJ mol-1. Mechanistic studies revealed that water assisted the activation of O2 to generate reactive hydroxyl groups and •OH radicals under light irradiation. Hydroxyl groups reacted with methane at Au single atoms to form water and CH3* species, followed by oxidation of CH3* via •OH radicals into methanol. Considering the recycling of water during the whole process, we can also regard water as a catalyst.

Coatable and Resistance-Proof Ionic Liquid for Pathogen Eradication.

Antibiotic-resistant bacteria infect close to 3 million people, and kill 35,000, each year in the United States. Ionic liquid (IL)-based antimicrobial agents have the potential to diversify our ever-diminishing antibiotic arsenal. Here, we describe an IL with potent submicromolar antimicrobial activity in vitro against clinically relevant Gram-negative and Gram-positive bacterial pathogens as well as anti-infective activity in a mouse model. The IL kills pathogenic bacteria such as Acinetobacter baumannii, Salmonella enterica, and Escherichia coli by disrupting their outer membrane and does not select for bacterial resistance. We show incorporation of our IL into surface coatings to generate a type of antibiofilm material. The IL-loaded ionogel surfaces demonstrate high-antimicrobial and antifouling activity by killing bacteria in both static and dynamic tests. Our IL-based antibiofilm surfaces are low-cost and easy to manufacture, can be formed on glass, latex, plastic, and metal surfaces, such as catheters and other medical devices where high local concentrations of antimicrobials are needed, and may have applications in other clinical and industrial settings.

Vision-based neural predictive tracking control for multi-manipulator systems with parametric uncertainty.

To deal with the coordination problem for multi-manipulator trajectory tracking systems with parametric uncertainties, this paper proposes a two-layer control scheme incorporating a model predictive strategy and an extended state observer. In the kinematic layer, the visual information is implemented and a visual servoing error model is derived by the image-based visual servoing strategy. A recurrent neural network model predictive control approach is proposed to obtain velocities which are regarded as the reference signals for the dynamic layer. For dynamics, a linear time-varying dynamic model of the multi-manipulator system coupled with the object is established, where the parametric uncertainty is recognized as an added disturbance. An extended state observer is sequentially designed to estimate the disturbance by using pole placement method. The input-to-state practical stability of the system is further analyzed with a bounded disturbance. Finally, simulations and comparison are given to verify the effectiveness and robustness of the proposed algorithm.

LKB1-MARK2 signalling mediates lipopolysaccharide-induced production of cytokines in mouse macrophages.

Lipopolysaccharide (LPS) is an endotoxin involved in a number of acute and chronic inflammatory syndromes. Although LPS-induced signalling has been extensively studied, there are still mysteries remaining to be revealed. In the current study, we used high-throughput phosphoproteomics to profile LPS-initiated signalling and aimed to find novel mediators. A total of 448 phosphoproteins with 765 phosphorylation sites were identified, and we further validated that the phosphorylation of MARK2 on T208 was important for the regulation on LPS-induced CXCL15 (human IL-8 homolog), IL-1ß, IL-6 and TNF-α release, in which LKB1 had a significant contribution. In summary, induction of cytokines by LPS in mouse macrophage is regulated by LKB1-MARK2 signals. Our study provides new clues for further exploring the underlying mechanisms of LPS-induced diseases, and new therapeutic approaches concerning bacterial infection may be derived from these findings.

Light-triggered release of conventional local anesthetics from a macromolecular prodrug for on-demand local anesthesia.

An on-demand anesthetic that would only take effect when needed and where the intensity of anesthesia could be easily adjustable according to patients' needs would be highly desirable. Here, we design and synthesize a macromolecular prodrug (P407-CM-T) in which the local anesthetic tetracaine (T) is attached to the polymer poloxamer 407 (P407) via a photo-cleavable coumarin linkage (CM). P407-CM-T solution is an injectable liquid at room temperature and gels near body temperature. The macromolecular prodrug has no anesthetic effect itself unless irradiated with a low-power blue light emitting diode (LED), resulting in local anesthesia. By adjusting the intensity and duration of irradiation, the anesthetic effect can be modulated. Local anesthesia can be repeatedly triggered.

Functionalized Multiarmed Polycaprolactones as Biocompatible Tissue Adhesives.

Existing tissue adhesives have a trade-off between adhesive strength and biocompatibility. Here, we report a series of biocompatible multiarmed polycaprolactones (PCL) as tissue adhesives that can be released from a hot glue gun and the length of each arm was kept at ∼2-3 kg mol-1 in all the polymers. The adhesion properties were dependent on the number of functionalized (N-hydroxysuccinimide ester (NHS), aldehyde (CHO), and isocyanate (NCO)) arms of the multiarmed polymers. The more arms, the higher the adhesion strength. For example, the adhesion strength in binding cut rat skin increased from 2.3 N cm-2 for 2PCL-NHS to 11.2 N cm-2 for 8-PCL-NHS. CHO- and NCO-modified 8PCL also had suitable adhesive properties. All the multiarmed polymers had minimal cytotoxicity in vitro and good biocompatibility in vivo, suggesting their potential as promising alternative surgical adhesives.