Polydopamine (PDA) is a promising biomimetic material, but its structural complexity hinders rational control over its light absorption properties. The purpose of this study was to develop a simple post-synthetic method to tune the absorption spectrum of PDA using Bobbit's salt (4-acetylamino-2,2,6,6-tetramethylpiperidine-1-oxoammonium salt) as a mild oxidant. Conventional PDA nanoparticles were treated with Bobbit's salt either in pure water or in a 1:1 methanol-water mixture to obtain two modified samples. Structural analysis conducted using Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and mass spectrometry demonstrated that Bobbit's salt selectively oxidized catechol units to ortho-benzoquinone moieties, with the C-O/C=O ratio decreasing from 71:29 in the untreated PDA to 51:49 in the water-treated sample, while nitrogen functionalities remained unchanged. Consequently, the sample prepared in pure water showed generally lower absorbance across the visible-near-infrared range, whereas the sample prepared in the methanol-water mixture exhibited enhanced ultraviolet absorption but reduced near-infrared absorption. When coated onto polyvinylidene fluoride membranes, the water-treated PDA produced a brighter and more reddish-yellow appearance. On transparent poly(methyl methacrylate) substrates, the same coating also enhanced ultraviolet blocking and reduced visible transmittance. These findings conclude that Bobbit's salt is an effective and selective reagent for tailoring the optical properties of PDA, with potential applications in protective coatings and light-modulating materials.

Melanin-like polymers, particularly polydopamine, have gained significant attention as photothermal materials due to their broad light absorption (ultraviolet to near-infrared), high photothermal conversion efficiency, negligible fluorescence, good biocompatibility regarding unmodified melanin-like polymers, and universal adhesion. Upon light irradiation, these bioinspired polymers convert absorbed optical energy into heat through molecular vibration and electron-phonon coupling, making them ideal for diverse photothermal applications. This review comprehensively summarizes recent advances in using melanin-like polymers for photothermal purposes. In biomedical engineering, they serve as efficient agents for photothermal therapy and synergistic antibacterial treatment. In catalysis, their photothermal effect enhances pollutant degradation, hydrogen production, and chemical warfare agent detoxification. For water remediation, melanin-like polymers are fabricated into evaporators, membranes, and aerogels for solar-driven steam generation, desalination, and oil spill cleanup. They also enable sensitive photothermal sensing, near-infrared imaging, and laser desorption ionization mass spectrometry imaging. Furthermore, these materials are incorporated into soft actuators and self-healing elastomers for light-controlled shape memory, programmable folding, and remote manipulation. Finally, we discuss remaining challenges such as long-term stability, biocompatibility, scalability, and color limitations and provide future perspectives for advancing melanin-like photothermal materials toward practical applications.

To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the DL model. In this study, the DL model was utilized to differentiate between MIBC and NMIBC based on three-channel image inputs, including original T2WI images, segmented bladder, and regions of interest. Inception V3 was employed for model construction. The accuracy, sensitivity (SN), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) for predicting MIBC by DL model were 92.4%, 94.7%, 91.5%, 81.8% and 97.7% in the validation set and 92.1%, 86.8%, 94.6%, 88.5% and 93.8% in the internal test set. In the external test set, these values were 81.6%, 57.1%, 87.1%, 50.0% and 90.0%. Additionally, the accuracy, SN, SP, PPV, and NPV for predicting MIBC were 93.5%, 100%, 93.4%, 11.1%, and 100% in VI-RADS 2; 80.0%, 66.7%, 87.2%, 73.7% and 82.9% in VI-RADS 3; 90.3%, 91.7%, 85.7%, 95.7%, 75.0% in VI-RADS 4. The accuracy, SN, and PPV were 93.9%, 93.9%, and 100% in VI-RADS 5. The DL model based on T2WI can effectively predict MIBC and serve as a valuable complement to VI-RADS 3.

A robust and efficient two-dimensional/three-dimensional (2D/3D) registration algorithm is critical to imageguided interventions, as it allows for intuitive, reproducible, and high-accuracy robot-assisted surgical procedures at competitive costs. Our approach adopts a multi-stage, self-supervised framework tailored to patientspecific contexts to tackle the 2D/3D registration problem. Preoperatively, a regression neural network is trained using synthesized X-rays to achieve robust initialization of rigid-body poses from the Special Euclidean group SE(3). However, SE(3) lacks a bi-invariant metric for measuring the distance between poses as it is not a direct product of compact and abelian groups. At the same time, existing left-invariant metrics fail to sufficiently account for the consistency and symmetry of spatial displacements under Lie group operations, which may hinder the network from correctly comprehending the 2D/3D projective geometry. To address these limitations, we propose a practical pose parameterization approach that embeds na & iuml;ve SE(3) pose elements into the fourdimensional Special Orthogonal group SO(4), thereby deriving an approximate bi-invariant metric for network training. Additionally, we present a cross-stage partial style, lightweight network CSP-ConvNeXt towards low-cost systematic solutions. Intraoperatively, we perform gradient-based optimization for real-time pose refinement. We report mean target registration error, network registration success rate, and sub-millimeter registration success rate for stage-dependent evaluations. Experimental results demonstrate that our method achieves state-of-the-art registration performance on two public datasets and one in-house dataset across all registration stages.

ConspectusMelanin-like polymers have attracted significant attention for their excellent light absorption and photothermal conversion properties. Unlike sequence-controlled biomacromolecules such as proteins and DNA, natural melanin and its analogues derive their broadband light absorption and photothermal performance from heterogeneous polymerization of 5,6-dihydroxyindole (DHI), indolequinone (IQ), and uncycled dopamine derivatives, as well as simultaneous progressive assembly between different monomeric species or oligomers. A key challenge in this field lies in establishing their structure-property relationships as well as precisely regulating the light absorption and photothermal performance of these bioinspired polymers to meet specific application requirements.Our research has revealed that nonradiative decay dominates their photothermal behavior, with absorbed optical energy converting to thermal vibrations within 1 ns, while regulating the light harvesting ability depends on molecular control over conjugation length, bandgap, and charge-transfer pathways through deliberate chemical design. By harnessing supramolecular assembly (hydrogen bonding, π-π interaction, cation-π interaction, etc.) and chemical reactions (metal-catechol coordination, Schiff base reaction, "click" chemistry, etc.), we have developed various emerging strategies, enabling customization of the light absorption regulation of melanin-like polymers. By leveraging covalent chemical toolboxes, electron donor-acceptor (D-A) pairs are constructed within the microstructures of melanin-like polymers, through nitroxide radicals (2,2,6,6-tetramethylpiperidinyl-1-oxide, TEMPO), nitrogen-containing heterocycles (e.g., hexachlorocyclotriphosphazene (HCCP), cyanogen chloride (CC), and trichloroisocyanuric acid (TCCA)), and mercaptotetrazole (MT) derivatives, narrowing the energy bandgap and enhancing the light absorption spectra in the visible and near-infrared regions. Doping various metal ions into the melanin-like polymers though metal-catechol coordination, d-d transition, and ligand-to-metal charge transfer (LMCT) amplifies the light-to-heat conversion performance. Notably, involving condensation polymerization using aldehyde linkers during the polymerization process of melanin-like polymers can not only avoid side reactions and achieve well-defined structure but also result in numerous D-A pairs for light harvesting improvement.Fabricating melanin-like polymers into fibers, thin films, capsules and shells, nanoparticles, and bulk materials (e.g., gels and elastomers) can profoundly optimize both light scattering and heat localization simultaneously. These strategies coupled with computational modeling and machine learning have also provided valuable insights into the structure-function relationships of melanin-like polymers, accelerating the precise design of materials for biomedical, energy, and environmental applications. This Account highlights our contributions to decode the chemistry of regulating the light absorption ability and photothermal performance of melanin-like polymers, offering a roadmap to bridge fundamental insights into practical photothermal technologies.

Metal ion-catecholate complexes (MCCs) extensively exist in plants and animals, which are in charge of versatile biological functions, such as constructing organs, controlled releasing metal ions and antibacterial. Inspired by this, researchers have exploited various kinds of artificial MCCs, which can serve as structural and functional synthons to construct advanced materials. In terms of the structural contribution, these complexes exhibit not only physical interactions, including metal-coordination, hydrogen bonding, π-π stacking and cation-π interactions, but also rich chemical reactions, including radical polymerization, Schiff base reaction and Michael addition. In terms of functional contribution, the complexes can endow the materials with the intrinsic properties of polyphenols and metal ions, including antioxidant, adhesion, antibacterial, bioimaging and catalyst. In addition, some emerging and fantastic functions are also originally from the complexes, such as tunable mechanical property, self-healing, controlled release and photothermal effect. In this review paper, we comprehensively discuss the recent development of MCC-based materials, including coatings, particles, metallogels and metal–organic frameworks (MOFs). Perspectives in this field has also been put forward as well.

OBJECTIVES:Accurate preoperative differentiation between non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is crucial for surgical decision-making in bladder cancer (BCa) patients. MIBC diagnosis relies on the Vesical Imaging-Reporting and Data System (VI-RADS) in clinical using multi-parametric MRI (mp-MRI). Given the absence of some sequences in practice, this study aims to optimize the existing T2-weighted imaging (T2WI) sequence to assess MIBC accurately. METHODS:We analyzed T2WI images from 615 BCa patients and developed a multi-view fusion self-distillation (MVSD) model that integrates transverse and sagittal views to classify MIBC and NMIBC. This 3D image classification method leverages z-axis information from 3D MRI volume, combining information from adjacent slices for comprehensive features extraction. Multi-view fusion enhances global information by mutually complementing and constraining information from the transverse and sagittal planes. Self-distillation allows shallow classifiers to learn valuable knowledge from deep layers, boosting feature extraction capability of the backbone and achieving better classification performance. RESULTS:Compared to the performance of MVSD with classical deep learning methods and the state-of-the-art MRI-based BCa classification approaches, the proposed MVSD model achieves the highest area under the curve (AUC) 0.927 and accuracy (Acc) 0.880, respectively. DeLong's test shows that the AUC of the MVSD has statistically significant differences with the VGG16, Densenet, ResNet50, and 3D residual network. Furthermore, the Acc of the MVSD model is higher than that of the two urologists. CONCLUSIONS:Our proposed MVSD model performs satisfactorily distinguishing between MIBC and NMIBC, indicating significant potential in facilitating preoperative BCa diagnosis for urologists.

Objective. To assist urologist and radiologist in the preoperative diagnosis of non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), we proposed a combination models strategy (CMS) utilizing multiparametric magnetic resonance imaging. Approach. The CMS includes three components: image registration, image segmentation, and multisequence feature fusion. To ensure spatial structure consistency of T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced imaging (DCE), a registration network based on patch sampling normalized mutual information was proposed to register DWI and DCE to T2WI. Moreover, to remove redundant information around the bladder, we employed a segmentation network to obtain the bladder and tumor regions from T2WI. Using the coordinate mapping from T2WI, we extracted these regions from DWI and DCE and integrated them into a three-branch dual-channel input. Finally, to fully fuse low-level and high-level features of T2WI, DWI, and DCE, we proposed a distributed multilayer fusion model for preoperative MIBC prediction with five-fold cross-validation. Main results. The study included 436 patients, of which 404 were for the internal cohort and 32 for external cohort. The MIBC was confirmed by pathological examination. In the internal cohort, the area under the curve, accuracy, sensitivity, and specificity achieved by our method were 0.928, 0.869, 0.753, and 0.929, respectively. For the urologist and radiologist, Vesical Imaging-Reporting and Data System score >3 was employed to determine MIBC. The urologist demonstrated an accuracy, sensitivity, and specificity of 0.842, 0.737, and 0.895, respectively, while the radiologist achieved 0.871, 0.803, and 0.906, respectively. In the external cohort, the accuracy of our method was 0.831, which was higher than that of the urologist (0.781) and the radiologist (0.813). Significance. Our proposed method achieved better diagnostic performance than urologist and was comparable to senior radiologist. These results indicate that CMS can effectively assist junior urologists and radiologists in diagnosing preoperative MIBC.

Medical image segmentation is a crucial task in many clinical applications, such as tumor detection and surgical planning. However, the annotation process for medical images is often both time-consuming and expensive, which requires professional knowledge and experience. This study aims to develop a medical image segmentation method based on semi-supervised learning, which can improve the performance of segmentation by effectively using labeled data and unlabeled data. We proposed a novel multi-task semi-supervised method based on multi-branch cross pseudo supervision, called MS2MPS, which can efficiently utilize unlabeled data for semi-supervised medical image segmentation. The proposed method consists of two multi-task backbone networks with multiple output branches, which were used to simultaneously generate segmentation probability maps (SPM) and signed distance maps (SDM) to get more constraints and information. Moreover, a multi-branch cross pseudo supervised (MPS) approach was proposed to promote the high similarity between predictions of the same input image by multiple perturbation networks. Experiments on the public medical image segmentation dataset Automated Cardiac Diagnosis Challenge (ACDC) and Left Atrium (LA) dataset demonstrate that our approach is superior to existing some semi-supervised segmentation methods in terms of Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and Jaccard Similarity Coefficient (JSC). When trained with 10% labeled data, compared to the best results in all compared methods, our approach improved the DSC by 2.63% and 1.54% on the ACDC and LA datasets, increased the JSC by 23.15% and 0.79%, and simultaneously reduced the HD95 by 12.57% and 7.77% on the respective datasets. Our proposed semi-supervised medical image segmentation approach is an effective and practical solution for medical image analysis, particularly in scenarios where labeled data is limited or expensive.

Solar-driven vapor generation has emerged as a promising wastewater remediation technology for clean water production. However, the complicated and diversified contaminants in wastewater still restrict its practical applications. Herein, inspired by the melanin in nature, a robust aerogel was facilely fabricated for multifunctional water remediation via a one-pot condensation copolymerization of 5,6-dihydroxyindole and formaldehyde. Benefiting from the superhydrophilicity, underwater superoleophobicity, and synergistic coordination effects, the resulting aerogel not only showed excellent performances in underwater oil resistance and oil-water separation ability, but also removed organic dyes and heavy metal ions contaminants in wastewater simultaneously. Moreover, owing to its admirable light harvesting capacity and porous microstructure for fast water transportation, the aerogel-based evaporator exhibited an excellent evaporation rate of 1.42 kg m(-2) h(-1) with a 91% evaporation efficiency under 1 sun illumination, which can be reused for long-term water evaporation. Note that such a stable evaporation rate could be maintained even in wastewater containing complex multicomponent contaminants. Outdoor evaporation experiments for lotus pond wastewater under natural sunlight also proved its great potential in practical applications. All those promising features of this all-in-one melanin-inspired aerogel may provide new strategies for the development of robust photothermal devices for multifunctional solar-driven water remediation.

"Click" chemistry, featuring high selectivity, efficiency, and modularity, has been regarded as a powerful tool to construct and engineer macromolecular materials with complex and disordered structures. In this work, we reported our first effort to use the thiol-Michael "click" chemistry to tune the energy structure and light harvesting behavior of melanin-like polymers by "clicking" mercaptotetrazole (MT) building blocks into the complicated polydopamine (PDA) networks, successfully acquiring a series of melanin-like nanoparticles with boosted light absorption and photothermal effect. A comprehensive characterization by density functional theory (DFT) stimulation, spectral analysis, cyclic voltammetry, and transient absorption demonstrated the energy bandgap reduction and light harvesting enhancement of those MT-doped PDA samples. The resulting polymers also exhibited excellent photothermal effect, which could be applied in efficient electric energy generation through photothermoelectric conversion and solar desalination application. This study provided new opportunities to regulate the functions of a complex bioinspired macromolecule system through "click" chemistries.

Objective. Computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in medical imaging modalities, and provide valuable information for clinical diagnosis and treatment. However, due to hardware limitations and radiation safety concerns, the acquired images are often limited in resolution. Super-resolution reconstruction (SR) techniques have been developed to enhance the resolution of CT and MRI slices, which can potentially improve diagnostic accuracy. To capture more useful feature information and reconstruct higher quality super-resolution images, we proposed a novel hybrid framework SR model based on generative adversarial networks. Approach. The proposed SR model combines frequency domain and perceptual loss functions, which can work in both frequency domain and image domain (spatial domain). The proposed SR model consists of 4 parts: (i) the discrete Fourier transform (DFT) operation transforms the image from the image domain to frequency domain; (ii) a complex residual U-net performs SR in the frequency domain; (iii) the inverse discrete Fourier transform (iDFT) operation based on data fusion transforms the image from the frequency domain to image domain; (iv) an enhanced residual U-net network is used for SR of image domain. Main results. Experimental results on bladder MRI slices, abdomen CT slices, and brain MRI slices show that the proposed SR model outperforms state-of-the-art SR methods in terms of visual quality and objective quality metric such as the structural similarity (SSIM) and the peak signal-to-noise ratio (PSNR), which proves that the proposed model has better generalization and robustness. (Bladder dataset: upscaling factor of 2: SSIM = 0.913, PSNR = 31.203; upscaling factor of 4: SSIM = 0.821, PSNR = 28.604. Abdomen dataset: upscaling factor of 2: SSIM = 0.929, PSNR = 32.594; upscaling factor of 4: SSIM = 0.834, PSNR = 27.050. Brain dataset: SSIM = 0.861, PSNR = 26.945). Significance. Our proposed SR model is capable of SR for CT and MRI slices. The SR results provide a reliable and effective foundation for clinical diagnosis and treatment.

Constructing a donor-acceptor (D-A) structure is a common strategy to change the polarizability and dipole moment of local molecules and induce the re-hybridization of molecular orbitals, which could lead to the reduction of the bandgap and promote the transfer of electrons. Although such a strategy has been successfully applied in organic optoelectronics with well-defined molecular structures, very limited progress has been reported for polymers with disordered and complex structures. In this work, we strived to employ this strategy to manipulate the light absorption and photothermal behaviors of melanin inspired, polydopamine (PDA), a typical kind of electron-rich molecular systems by involving a strong receptor unit, trichloroisocyanuric acid through covalent connection to construct D-A pairs. This design could decrease the bandgap and improve the optical absorption by orbital re hybridization, which has been carefully verified by detailed spectral analysis and simulated calculation. The remarkable photothermal performances present promising potential in photothermal Marangoni actuators and solar power generation and provided new opportunities for the rational design of the microstructure of melanin.

Solar steam generation is a robust and environ-mentally friendly way to obtain fresh water and alleviate the water shortage problem. Although a wide variety of steam generation devices have been well documented, most fabricated materials are often only suitable for one device and generally difficult to be removed, resulting in a disposable device. This may raise serious concerns about the total cost issues, particularly for those devices with complex and well-tailored microstructures. Thus, it is urgent to develop a robust and dynamic photothermal coating that can be easily removed and flexibly switched between different installations with stable photothermal conversion ability. In this work, we report a degradable and recyclable metal-phenolic network-based photo-thermal coating assembled from two kinds of naturally occurring building blocks [gallic acid (GA) and Fe(III)]. Hydrophilic poly(vinylidene fluoride) (PVDF) was selected as the substrate, resulting in the robust GA@Fe(III)@PVDF membrane, which can achieve a high evaporation rate of 1.539 kg m-2 h-1 and a superb steam generation efficiency of 90.2% under 1 sun illumination. Impressively, the GA@Fe(III) coating can be degraded under acidic condition rapidly, and the recovered PVDF can be recoated by the GA@Fe(III) networks again. The steam generation performance of the recovered GA@Fe(III)@PVDF can remain stable over 150 degrading and recoating cycles. We believed that this work could offer opportunities to fabricate low-cost, sustainable, and degradable photothermal coatings for solar steam generation.

As a typical type of melanin-inspired materials, polydopamine (PDA) has been widely used in photothermal conversion and energy harvesting applications due to its broad spectral absorption feature, but its limited absorption in the visible light region severely hinders further utilization. Although enhancing the visible light absorption and photothermal performance of PDA is urgent, it is quite difficult to achieve this goal via structural tailoring strategies because of the complicated and disordered structure within PDA. To address this issue, we reported a facile approach involving nitrogen-containing heterocycles toward the design of two new kinds of PDA nanomaterials. The introduction of nitrogen-containing heterocycles greatly reduced the lowest unoccupied molecular orbital of the molecular segments and then promoted electron delocalization in the local area. We verified the changes of energy bandgap and exciton decay of nitrogen-containing heterocycle-doped PDA through spectral analysis, density functional theory calculation, and electrostatic potential distribution on the molecular surface. Therefore, these heterocycle-doped PDAs exhibited improved visible light absorption and photothermal performances, which can be used in solar power generation applications. This work provides new opportunities for the rational design and structural tailoring of bioinspired photothermal materials.

Background: An accurate preoperative assessment of Non-Muscle-Invasive Bladder Cancer (NMIBC) and Muscle -Invasive Bladder Cancer (MIBC) in Bladder Cancer (BCa) can help the urologist make diagnostic decisions. Considering the absence of multiparametric MRI for contrast medium allergy and economic reasons, this study aims to develop a deep learning method based on T2-Weighted (T2WI) images alone for predicting NMIBC and MIBC.Method: We propose a Multi-task BCa Muscular Invasion Prediction (MBMIP) model to discriminate MIBC from NMIBC. The three-channel-input including the original T2WI image, segmented bladder, and the region of in-terest can help the MBMIP model locate the bladder and pay more attention to the surrounding information of the tumor. Inception V3 is used as the feature extraction module, which uses multiple branches to extract high-level features with different degrees of abstraction. In addition, based on the idea of multi-task learning, a reconstruction block for T2WI images is also introduced to assist the backbone classification network to improve the classification performance.Results: The entire data consist of retrospective data (390 cases), prospective data (39 cases), and multi-center data (39 cases). In the retrospective test, the accuracy, sensitivity, and specificity of the MBMIP model are 0.911, 0.889, and 0.920 respectively, while those of the prospective test are 0.923, 1.000, and 0.885. And in the muti-center test, the MBMIP model yields accuracy, sensitivity, and specificity of 0.846, 0.667, and 0.879.Conclusion: The MBMIP model could achieve a satisfactory prediction result in discriminating between NMIBC and MIBC, which may aid urologists in preoperative decision-making for BCa patients.

Solar steam generation is an emerging strategy for water desalination using renewable solar energy and seawater resources. In order to convert solar energy into heat for seawater evaporation, we developed a bi-layered structure composite for high-efficient solar evaporation based on photothermal-enhanced arginine-doped polydopamine (APDA) and raw wood, which are biodegradable and sustainable. Note that the APDA coating layer exhibited improved optical absorption and photothermal conversion ability compared with conventional polydopamine (PDA) coating on account of the construction of donor-acceptor pairs within the APDA microstructure system. Density functional theory (DTF) calculation further confirmed that the energy bandgap of APDA could be narrowed though donor-acceptor microstructures and then enhanced the absorption spectrum. The resulting APDA-wood composite performed a solar vapor generation efficiency of ~77% on the condition of 1 sun illumination. The water evaporation process was quite stable over 100 cycles and the metal ions in seawater were almost eliminated after desalination. This amino acid-initiated cost-effective and facile coating method provided new opportunities to fabricate photothermal-enhanced coating materials for solar evaporation applications.

A solar steam generation method has been widely investigated as a sustainable method to achieve seawater desalination and sewage treatment. However, oil pollutants are usually emitted in real seawater or wastewaters, which can cause serious fouling problems to disturb the solar evaporation performance. In this work, a mussel-inspired, low-cost, polydopamine-filled cellulose aerogel (PDA-CA) has been rationally designed and fabricated with both superhydrophilicity and underwater superoleophobicity. The resulting PDA-CA device could also achieve a high solar evaporation rate of 1.36 kg m(-1) h(-1) with an 86% solar energy utilize efficiency under 1 sun illumination. In addition, the PDA-CA not only exhibited promising antifouling capacity for long-term water evaporation but also engaged in the effective adsorption of organic dye contaminants. These promising features of PDA-CA may offer new opportunities for developing multifunctional photothermal devices for solar-driven water remediation.

Discovery and development of new sustainable photothermal materials with tunable light absorption spectra play a key role in solar energy harvesting and conversion. One possible solution to this quest is to check nature as a source of matters or inspiration. Inspired by the formation of tea stains, a unique class of dark stain materials generated by the interfacial reaction between tea polyphenols and metal substance, we reported the facile preparation and screening of a series of photothermal nanocoating layers via the metal ion (i.e. Cu(II),Fe(III), Ni(II), Zn(II)) promoted in situ polymerization of typical phenolic moieties of tea polyphenols (i.e., catechol and pyrogallol). It was found that those resulting metal-polyphenolic nanocoatings showed various promising features, such as high blackness and strong adhesion, excellent and tunable light absorption properties, good hydrophilicity and long-term stability. We further fabricated the photothermal composite devices by in situ formation of metal-polyphenolic nanocoatings on pristine silks for solar desalination, which demonstrated promising durable evaporation behaviors with excellent evaporation rates and steam generation efficiencies. We believe that this work could provide more opportunities towards new types of bio-inspired and sustainable photothermal nanomaterials for solar energy harvesting applications such as water desalination.

Natural polyphenols have attracted great interests in medicine, food and cosmetics due to their versatile functions such as antioxidant, anticancer and antibacterial. The polyphenolic structures (i.e. catechol, and pyrogallol) in natural polyphenols are responsible for strong noncovalent interactions via multiple hydrogen bonding and hydrophobic interactions, and dynamic covalent complexation with boronate groups and multiple metal ions. In this review, we focused on the preparation and applications of natural polyphenol-based coating films, nanoparticles, nanocapsules, and hydrogels emerged from their chemical and functional signatures. The beneficial role and mechanism of natural polyphenols in facilitating the delivery of proteins, nucleic acids and conventional drug molecules were reviewed. Finally, the challenges of natural polyphenol-based delivery systems and perspectives in rational design of next generation natural polyphenol biomaterials will be discussed.