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Ruthenium (Ru)-based organometallic drugs have gained attention as chemotherapeutic and bioimaging agents due to their fewer side effects and excellent physical optical properties. Tuning the electronic structures of Ru complexes has been proven to increase the cytotoxicity of cancer cells and the luminescent efficiency of the analytical probes. However, the relationship between electronic structures and bioactivities is still unclear due to the potential enhancement of both electron donor and acceptor properties. Thus, we investigated the relationship between the electronic structures of Ru(II) complexes and cytotoxicity by optimizing the electron-withdrawing (complex 1), electron-neutral (complex 2), and electron-donating (complex 3) ligands through DFT calculations, bioactivities tests, and docking studies. Our results indicated that it was not sufficient to consider only either the effect of electron-withdrawing or electron-donating effects on biological activities instead of the total electronic effects. Furthermore, these complexes with electron-donating substituents (complex 3) featured unique “off-on” luminescent emission phenomena caused by the various “HOMO-LUMO” distributions when they interacted with DNA, while complex with electron-withdrawing substituent showed an “always-on” signature. These findings offer valuable insight into the development of bifunctional chemotherapeutic agents along with bioimaging ability.

Purpose The disinfection robot developed by the authors and team focuses on achieving fast and precise disinfection under a given or specific disinfection zone. This looks to solve problems with traditional robots that pay less attention to the level, efficiency and zones of disinfection. To effectively support and guarantee normal running for the whole system, a digital twin system is applied to the disinfection robot. This study aims to achieve fast, precise and thorough disinfection via the developed mobile robot. Design/methodology/approach The designed robot is composed primarily of the following three parts: a mobile platform, a six-axis robotic arm and a ultraviolet-C (UVC) LED array. The UVC LED array is installed on the end-effector to achieve large-scale, precise manipulation. The adoption of all types of advanced sensors and the development of an intuitive and user-friendly client interface are helpful in achieving remote control, path planning, data monitoring and custom disinfection functions. Findings Disinfection of three different locations in the laboratory was performed; the dosage distribution of the surface as radiated by the UVC robot was detected; and feasibility of development was validated. Originality/value The developed disinfection robot achieved fast, precise and thorough disinfection for a given or specific disinfection zone.

The deconfined quantum critical point (DQCP) represents a paradigm shift in quantum matter studies, presenting a “beyond Landau” scenario for order–order transitions. Its experimental realization, however, has remained elusive. Using high-pressure 11 B nuclear magnetic resonance measurements on the quantum magnet SrCu 2 (BO 3 ) 2 , we here demonstrate a magnetic-field induced plaquette-singlet to antiferromagnetic transition above 1.8 GPa at a notably low temperature, T c ≃ 0.07 K. First-order signatures of the transition weaken with increasing pressure, and we observe quantum critical scaling at the highest pressure, 2.4 GPa. Supported by model calculations, we suggest that these observations can be explained by a proximate DQCP inducing critical quantum fluctuations and emergent O(3) symmetry of the order parameters. Our findings offer a concrete experimental platform for the investigation of the DQCP.

In this ageing society, sarcopenia as a geriatric condition that can have significant negative impacts on an individual’s quality of life. Sarcopenia is a kind of aged syndrome associated with loss of muscle mass and function, which may lead to falls, fractures, gait disorders or even mortality. There are multiple ways to diagnose sarcopenia, such as using Magnetic resonance imaging (MRI), Dual-energy X-ray absorptiometry (DEXA) and Bioelectrical impedance analysis (BIA) etc. to calculate muscle mass; using handgrip or sit-to-stand to measure muscle strength; using short physical performance battery (SPPB), gait, and 5-time sit-to-stand to evaluate physical performance.In this work, we use two μIMUs worn on subjects to record their sit-to-stand motion, and then used several machine learning models to diagnose the severity of sarcopenia of the subjects. We recruited 53 elderly subjects in total for this work. The youngest subject is 65 years old and the oldest is 84 years old. Their average age is 70 years old. Among these 53 subjects, there are 12 healthy ones and 41 sarcopenia patients with different severity. The subject is instructed to do the single sit-to-stand (STS) three times, and two μIMUs attached to the subject’s waist and thigh transfer the data to a computer by Bluetooth. We separated the STS motion process into 4 phases based on the angle and angular velocity, extracted a total of 510 features for motion analytics. These features were futher analyzed by sequential feature selection with 5 different machine learning models (SVM, KNN, decision tree, LDA, and multilayer perceptron). With our proposed methodology, all 53 subjects could be classified as healthy or having sarcopenia with risk level 1, 2, or 3. The best accuracy to distinguish the healthy or sarcopenia subjects is 98.32%, and the best results to distinguish sarcopenia risk levels from 0 (healthy) to 3 (most severe) is 90.44%.

Slope deformation prediction is one of the critical factors in the early warning of slope failure. Establishing an accurate slope deformation prediction model is important. Time-series displacement data of slopes directly reflect the deformation characteristics and stability properties of slopes. The use of existing data analysis approaches, such as statistical methods and machine learning algorithms, to establish a reasonable and accurate prediction model based on the monitored time-series displacement data is a common solution to slope deformation prediction. In this paper, we conduct a comparative investigation of machine learning approaches for slope deformation prediction based on monitored time-series displacement data. First, we established eleven slope deformation prediction models based on the time-series displacement data obtained from seven in situ monitoring points of the Huanglianshu landslide using machine learning approaches. Second, four evaluation metrics were used to comparatively analyze the prediction performance of all models at each monitoring point. The experimental results of the Huanglianshu landslide indicated that the long-short-term memory (LSTM) model with an attention mechanism and the transformer model achieved the highest prediction accuracy. The comparative analysis of model characteristics suggested that the Transformer model is better adapted to predict nonlinear landslide displacements that are affected by multiple factors. The drawn conclusion could help select a suitable slope deformation model for early landslide warnings.

Background Findings concerning gender differences in the associations between tobacco smoke exposure (TSE) and depression are inconsistent. This study aimed to investigate the gender-specific associations between active and passive TSE with depressive symptoms in a large, nationally representative sample of U.S. adults. Methods Data were from 27,175 adults aged ≥20 years in the 2007–2018 National Health and Nutrition Examination Survey (NHANES). Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9). Multivariable logistic regression was used to adjust for possible confounders. Whether the TSE-depression relationships may differ by age, race/ethnicity, socioeconomic status, body mass index (BMI), and self-reported health status was examined. Results After adjustment for lifestyle- and health-related variables, no significant associations between active (OR, 1.16 [95% CI, 0.87–1.55]) and passive TSE (OR, 0.84 [95% CI, 0.59–1.19]) and depressive symptoms were found among men. Among women, active TSE was associated with depressive symptoms (OR, 1.90 [95% CI, 1.51–2.39]), while the association for passive TSE was nonsignificant (OR, 1.11 [95% CI, 0.91–1.34]) after adjusting for lifestyle- and health-related variables. Interaction and subgroup analyses showed that self-reported health status could modify the relationship between passive TSE and depressive symptoms among women. Furthermore, a dose-response relationship between serum cotinine and depressive symptoms was found in women, but not in men. Conclusions This study suggests a stronger TSE-depression association in women than in men. Understanding these gender-specific patterns and identifying the potential moderators of such relationships will enable better targeting of public health interventions.

Icariin (ICA) has a significant capacity to protect against depression and hippocampal injury, but it cannot effectively cross the blood-brain barrier and accumulate in the brain. Therefore, the mechanism by which ICA protects against hippocampal injury in depression remains unclear. In this study, we performed proteomics analysis of cerebrospinal fluid to investigate the mechanism by which ICA prevents dysfunctional hippocampal neurogenesis in depression. A rat model of depression was established through exposure to chronic unpredictable mild stress for 6 weeks, after which 120 mg/kg ICA was administered subcutaneously every day. The results showed that ICA alleviated depressive symptoms, learning and memory dysfunction, dysfunctional neurogenesis, and neuronal loss in the dentate gyrus of rats with depression. Neural stem cells from rat embryonic hippocampi were cultured in media containing 20% cerebrospinal fluid from each group of rats and then treated with 100 μM corticosterone. The addition of cerebrospinal fluid from rats treated with ICA largely prevented the corticosterone-mediated inhibition of neuronal proliferation and differentiation. Fifty-two differentially expressed proteins regulated by chronic unpredictable mild stress and ICA were identified through proteomics analysis of cerebrospinal fluid. These proteins were mainly involved in the ribosome, PI3K-Akt signaling, and interleukin-17 signaling pathways. Parallel reaction monitoring mass spectrometry showed that Rps4x, Rps12, Rps14, Rps19, Hsp90b1, and Hsp90aa1 were up-regulated by chronic unpredictable mild stress and down-regulated by ICA. In contrast, HtrA1 was down-regulated by chronic unpredictable mild stress and up-regulated by ICA. These findings suggest that ICA can prevent depression and dysfunctional hippocampal neurogenesis through regulating the expression of certain proteins found in the cerebrospinal fluid. The study was approved by the Experimental Animal Ethics Committee of Guangzhou University of Chinese Medicine of China in March 2017.

19F-NMR signals are sensitive to local electrostatic fields and are useful in probing protein structures and dynamics.

The linear molecules BeNCO and OBeNC are kinetically stable enough for conducting experimental observation and detection.

As a popular platform-independent language, Java is widely used in enterprise applications. In the past few years, language vulnerabilities exploited by Java malware have become increasingly prevalent, which cause threats for multi-platform. Security researchers continuously propose various approaches for fighting against Java malware programs. However, the presence of complex hidden techniques, such as code obfuscation, makes identifying complicated Java malware become challenging. Therefore, there is an urgent need to develop new approaches for resisting hidden techniques. In this paper, we present BejaGNN, a novel behavior-based Java malware detection method using static analysis, word embedding technique, and graph neural network. Specifically, BejaGNN leverages static analysis techniques to extract ICFGs from Java program files and then prunes these ICFGs to remove noisy instructions. Then, work embedding techniques are adopted to learn semantic representations for Java bytecode instructions. Finally, BejaGNN builds a graph neural network classifier to determine the maliciousness of Java programs. Experimental results on a public Java bytecode benchmark demonstrate that BejaGNN achieves high F1 98.8% and is superior to existing Java malware detection approaches, which verifies the promise of graph neural network in Java malware detection.

BackgroundCT053PTSA is a novel tyrosine kinase inhibitor that targets MET, AXL, VEGFR2, FLT3 and MERTK. Here, we present preclinical data about CT053PTSA, and we conducted the first-in-human (FIH) study to evaluate the use of CT053PTSA in adult patients with pretreated advanced solid tumors.MethodsThe selectivity and antitumor activity of CT053PTSA were assessed in cell lines in vitro through kinase and cellular screening panels and in cell line-derived tumor xenograft (CDX) and patient-derived xenograft (PDX) models in vivo. The FIH, phase I, single-center, single-arm, dose escalation (3 + 3 design) study was conducted, patients received at least one dose of CT053PTSA (15 mg QD, 30 mg QD, 60 mg QD, 100 mg QD, and 150 mg QD). The primary objectives were to assess safety and tolerability, to determine the maximum tolerated dose (MTD), dose-limiting toxicity (DLT), and the recommended dose of CT053PTSA for further study. Secondary objectives included pharmacokinetics, antitumor activity.ResultsCT053 (free-base form of CT053PTSA) inhibited MET, AXL, VEGFR2, FLT3 and MERTK phosphorylation and suppressed tumor cell angiogenesis by blocking VEGF and HGF, respectively, in vitro. Moreover, cell lines with high MET expression exhibited strong sensitivity to CT053, and CT053 blocked the MET and AXL signaling pathways. In an in vivo study, CT053 significantly inhibited tumor growth in CDX and PDX models. Twenty eligible patients were enrolled in the FIH phase I trial. The most common treatment-related adverse events were transaminase elevation (65%), leukopenia (45%) and neutropenia (35%). DLTs occurred in 3 patients, 1/6 in the 100 mg group and 2/4 in the 150 mg group, so the MTD was set to 100 mg. CT053PTSA was rapidly absorbed after the oral administration of a single dose, and the Cmax and AUC increased proportionally as the dose increased. A total of 17 patients in this trial underwent tumor imaging evaluation, and 29.4% had stable disease.ConclusionsCT053PTSA has potent antitumor and antiangiogenic activity in preclinical models. In this FIH phase I trial, CT053PTSA was well tolerated and had a satisfactory safety profile. Further trials evaluating the clinical activity of CT053PTSA are ongoing.

Ruthenium (Ru)-based chemotherapeutic agents are a choice to replace traditional platinum-containing metallodrugs due to fewer side effects. It has been proved that the mechanism of Ru complex drugs is to highly likely bind with DNA and certain proteins, which also highly depends on the electronic structures of Ru complexes. However, the relationship between electronic properties and chemotherapeutic activities has not yet been completely systemically investigated, which limits the effective drug design strategies. Herein, we propose that increasing the electron densities of Ru would enhance the nucleophilic substitution rate of chlorine atoms (Cl) on Ru, providing better bioactivity against both amino acids and nucleic acids. A series of complexes with various optimized electron-donating groups (EDGs) were synthesized according to DFT calculations. In addition, kinetics substitution with L-histidine, DNA binding experiments, and cell cytotoxicity studies verified our assumptions. Surprisingly, these complexes could also be potential cellular imaging probes

With the widespread usage of Android smartphones in our daily lives, the Android platform has become an attractive target for malware authors. There is an urgent need for developing an automatic malware detection approach to prevent the spread of malware. The low code coverage and poor efficiency of the dynamic analysis limit the large-scale deployment of malware detection methods based on dynamic features. Therefore, researchers have proposed a plethora of detection approaches based on abundant static features to provide efficient malware detection. This paper explores the direction of Android malware detection based on graph representation learning. Without complex feature graph construction, we propose a new Android malware detection approach based on lightweight static analysis via the graph neural network (GNN). Instead of directly extracting Application Programming Interface (API) call information, we further analyze the source code of Android applications to extract high-level semantic information, which increases the barrier of evading detection. Particularly, we construct approximate call graphs from function invocation relationships within an Android application to represent this application and further extract intrafunction attributes, including required permission, security level, and Smali instructions’ semantic information via Word2Vec, to form the node attributes within graph structures. Then, we use the graph neural network to generate a vector representation of the application, and then malware detection is performed on this representation space. We conduct experiments on real-world application samples. The experimental results demonstrate that our approach implements high effective malware detection and outperforms state-of-the-art detection approaches.

Mucus plays an important role in regulating biological activities. Accordingly, investigating the structures and properties of mucus can be beneficial for providing a better understanding of physiological and pathological changes. Due to a lack of adequate observation tools, the ultra-microstructures of native mucus functional interfaces are not yet fully understood. Atomic force microscopy (AFM) provides a novel and powerful tool for investigating the structures and properties of biomaterials in their native states. Nevertheless, to date, studies of biomaterials using AFM are commonly performed in air, which cannot faithfully reflect the structures of biomaterials in liquids. In this paper, by adopting the mucilage secreted by the carnivorous sundew plant as an example, high-resolution AFM imaging and analysis of mucous nanostructures in aqueous conditions were achieved. Sundew mucilage was coated onto the surface of a glass slide or mica, and the subsequent AFM imaging of the mucilage-coated substrates in pure water revealed that a large number of nanoparticles were contained in the sundew mucilage. In contrast, AFM imaging of sundew mucilage-coated substrates in air showed that dried mucilage contained a large number of nanofibers. This underscored the structural differences in sundew mucilage in the air and in a solution. Furthermore, AFM multiparametric imaging was employed to visually characterize the mechanical properties of sundew mucilage nanostructures in pure water and showed significant differences in the mechanics between nanoparticles and nanofibers. This study provides a novel approach for investigating the ultra-microstructures and mechanics of mucus’ functional interfaces in their native states in situ , which has fundamental impacts on research focused on biomaterials.

Morphology and texture detection, which are important components of tactile sensing, augment the response of human beings to external stimuli. Similarly, tactile sensing-based information acquisition systems in robots can help enhance the interactions of robots with the surroundings. The main drawback of morphology and texture sensing methods is their inability to explain and quantify sensing information, which makes it difficult to utilize prior knowledge and necessitates a new training process to fit the new task, even if the changes between the existing and new tasks are minuscule. Another drawback is its dependence on large datasets. To solve these problems, a hybrid connectionist symbolic model (HCSM) is proposed herein that combines historic symbolic knowledge and end-to-end neural networks. The symbolic model requires a smaller dataset and possesses an improved transferability of detection. Neural networks can be easily established and exhibit better fault tolerance for non-ideal samples. The HCSM model combines these advantages. Experiments with the tactile-based morphology and texture detection demonstrated that the new method can transfer the detection ability to fit new tasks without requiring additional retraining and has a 16% higher recognition precision than a convolutional neural network, LeNet, AlexNet, VGG16, and ResNet. The HCSM method with these features can broaden the range of applications of tactile sensing.

Flavonoids from natural products are well-identified as potential antiviral agents in the treatment of SARS-CoV-2 (COVID-19) infection and related diseases. However, some major species of flavonoids from Chinese traditional folk medicine, such as of Artemisia argyi (A. argyi), have not been evaluated yet. Here, we choose five major flavonoids obtained from A. argyi, namely, Jaceosidin (1), Eupatilin (2), Apigenin (3), Eupafolin (4), and 5,6-Dihydroxy-7,3′,4′-trimethoxyflavone (5), compared to the well-studied Baicalein (6), as potential inhibitors analogs for COVID-19 by computational modeling strategies. The frontier molecular orbitals (FMOs), chemical reactivity descriptors, and electrostatic surface potential (ESP) were performed by density functional theory (DFT) calculations. Additionally, these flavonoids were docked on the main protease (PDB: 6LU7) of SARS-CoV-2 to evaluate the binding affinities. Computational analysis predicted that all of these compounds show a high affinity and might serve as potential inhibitors to SARS-CoV-2, among which compound (5) exhibits the least binding energy (−155.226 kcal/mol). The high binding affinity could be enhanced by increasing the electron repulsion due to the valence shell electron pair repulsion model (VSEPR). Consequently, the major flavonoids in Artemisia argyi have a significant ability to reduce the deterioration of COVID-19 in the terms of DFT calculations and molecular docking.

This paper introduces a novel sensor calibration scheme, the Hybrid Analytical and Network (HAN) model, which is especially suitable for regression based on neural networks with small training datasets. Through the HAN model, qualitative physical knowledge can be used to facilitate network training so that the demand on training data can be significantly reduced. Furthermore, training results can be transferred to the calibration of other types of sensors. The HAN model has been used to calibrate two haptic sensors, namely, the balance sensor and the skin sensor, which both operate on the principle of Frustrated Total Internal Reflection (FTIR), but for two different applications. The balance sensor is designed for human balance ability assessment, while the skin sensor is designed for evaluation of human skin properties. The calibration results using the HAN model are far more accurate and faster than other neural network training methods, especially for a small experiment dataset. The strong transfer ability of the HAN model is also demonstrated by the transfer attempt from balance sensor calibration to that of the skin sensor. The HAN model provides a new method to integrate model-based and data-driven sensor calibration methods, and shows greater efficacy. Its application is not restricted to sensor calibration but has broader potential value in all neural network regression tasks.

Context Chinese herbal formula JiaWeiSiNiSan (JWSNS) has been widely used to prevent stress-induced neuropsychiatric ailments in clinics and proven to have therapeutic anti-stress effects on rats. However, the mechanism remains unclear. Objective Based on the proteomics of cerebrospinal fluid (CSF), this study explores the possible mechanism and target proteins of JiaWeiSiNiSan raising stress resilience and preventing stress damage. Materials and methods A 6-week Chronic Unpredictable Mild Stress (CUMS) model was applied on adult Wistar male rats to observe the effects of JWSNS on improving mental stress resilience. Tandem Mass Tag (TMT) proteomics and bioinformatics analysis were used to screen and analyze differentially expressed proteins (DEPs) in CSF. Parallel Reaction Monitoring (PRM) was used to validate target DEPs. Results Significantly decreased sucrose preference, locomotion activity level and accuracy of T-maze, as well as increased immobility time, were observed in CUMS rats compared to CON rats while JWSNS improved above depression-like behaviours. The quantitative proteomics and bioinformatics analysis showed that JWSNS decreased the expression of Rps4x, HSP90AA1, Rps12, Uba1, Rsp14, Tuba1b in CUMS rats CSF (p

Mechanics are intrinsic properties which appears throughout the formation, development, and aging processes of biological systems. Mechanics have been shown to play important roles in regulating the development and metastasis of tumors, and understanding tumor mechanics has emerged as a promising way to reveal the underlying mechanisms guiding tumor behaviors. In particular, tumors are highly complex diseases associated with multifaceted factors, including alterations in cancerous cells, tissues, and organs as well as microenvironmental cues, indicating that investigating tumor mechanics on multiple levels is significantly helpful for comprehensively understanding the effects of mechanics on tumor progression. Recently, diverse techniques have been developed for probing the mechanics of tumors, among which atomic force microscopy (AFM) has appeared as an excellent platform enabling simultaneously characterizing the structures and mechanical properties of living biological systems ranging from individual molecules and cells to tissue samples with unprecedented spatiotemporal resolution, offering novel possibilities for understanding tumor physics and contributing much to the studies of cancer. In this review, we survey the recent progress that has been achieved with the use of AFM for revealing micro/nanoscale mechanics in tumor development and metastasis. Challenges and future progress are also discussed.

Natural hydrogels have been widely applied in the field of biomedicine for their biocompatible and biodegradable characteristics. However, due to the lack of adequate observation tools, so far the detailed organizations and structures of native natural hydrogels at the nanoscale are still not fully understood. The advent of atomic force microscopy (AFM) provides a novel powerful tool for characterizing biomaterials, but studies about utilizing AFM to investigate the nanostructures of native natural hydrogels are still scarce. In this paper, AFM in situ high-resolution imaging was used to reveal the nanoscopic fine structures of the natural hydrogels secreted by four types of carnivorous plants ( Drosera capensis , Pinguicula cyclosecta , Sarracenia rubra , and Dionaea muscipula ). Mucilage secreted by Drosera capensis or Pinguicula cyclosecta was coated on the surface of mica, and the subsequent AFM imaging of the mucilage-coated mica in air clearly revealed that both the mucilage secreted by Drosera capensis and Pinguicula cyclosecta contained a large number of nanofibers which had diverse self-assembly behaviors. Nectar secreted by Sarracenia rubra or Dionaea muscipula was coated on the surface of glass slides, and AFM images of the nectar-coated glass slide recorded in deionized water showed that both the nectar secreted by Sarracenia rubra and Dionaea muscipula considerably contained nanoparticles. Statistical results showed that the size of nanoparticles in the nectar of Dionaea muscipula was significantly larger than that of nanoparticles in the nectar of Sarracenia rubra . The study improves our understanding of the natural hydrogels secreted by carnivorous plants and provides a novel approach for characterizing the nanostructures of natural biomaterials in their native states, offering new possibilities for the studies of supramolecular biomaterials.

Atomic force microscopy (AFM) based nanorobotic technology provides a unique manner for delicate operations at the nanoscale in various ambient, thanks to its ultrahigh spatial resolution, outstanding environmental adaptability, and numerous measurement approaches. However, one vital challenge behind nanoscale operations is the task space positioning problem, known for its difficulty to realize desirable relative position between the AFM sharp tip and the target. It is noted that although one AFM possesses nanometer imaging resolution, it is hard to achieve nanometer positioning accuracy due to system uncertainties, such as the uncompensated nonlinearity and the thermal drift generated internally/externally. In order to tackle the vital positioning problem in the space, this letter proposes a specific visual servoing control framework using a local ambient image as feedback to overcome positioning uncertainty at the nanoscale. In this letter, we employ the optimal Archimedean spiral scanning strategy and try to exceed the speed criterion to pursue faster and uniform local scan for generating feedback images. To fulfill reliable precise tip locating with the possible non-ideal feedback images, a type of visual servoing control approach i.e., extended non-vector space (ENVS) controller based on the subset projection method, was developed for tackling environmental noise and disturbances. Experimental studies were conducted to verify the effectiveness of the proposed task space tip locating methodology. Testing results demonstrated that the positioning uncertainty was attenuated dramatically, and $\text{1 nm}$ level positioning precision has been achieved for the $\text{500 nm}\times \text{500 nm}$ task space.

Commonly overexpressed in many cancers and associated with tumor growth, metastasis, drug resistance, and poor overall survival, Axl has emerged as a promising target for cancer therapy. However, the availability of new chemical forms for Axl inhibition is limited. Herein, we present the development and characterization of novel Axl inhibitors, including the design, synthesis, and structure-activity relationships (SARs) of a series of diphenylpyrimidine-diamine derivatives. Most of these compounds exhibited remarkable activity against the Axl kinase. In particular, the promising compound m16 showed the highest enzymatic inhibitory potency (IC

Polymer coatings having high amounts of renewable carbon and self-healing properties are highly sought after in a sustainability perspective. We report here the development of bio-/CO2-derived noni...