Functional Plasmon-Activated Water Increases Akkermansia muciniphila Abundance in Gut Microbiota to Ameliorate Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) is associated with dysbiosis and intestinal barrier dysfunction, as indicated by epithelial hyperpermeability and high levels of mucosal-associated bacteria. Changes in gut microbiota may be correlated with IBD pathogenesis. Additionally, microbe-based treatments could mitigate clinical IBD symptoms. Plasmon-activated water (PAW) is known to have an anti-inflammatory potential. In this work, we studied the association between the anti-inflammatory ability of PAW and intestinal microbes, thereby improving IBD treatment. We examined the PAW-induced changes in the colonic immune activity and microbiota of mice by immunohistochemistry and next generation sequencing, determined whether drinking PAW can mitigate IBD induced by 2,4,6-trinitrobenzene sulfonic acid (TNBS) and dysbiosis through mice animal models. The effects of specific probiotic species on mice with TNBS-induced IBD were also investigated. Experimental results indicated that PAW could change the local inflammation in the intestinal microenvironment. Moreover, the abundance of Akkermansia spp. was degraded in the TNBS-treated mice but elevated in the PAW-drinking mice. Daily rectal injection of Akkermansia muciniphila, a potential probiotic species in Akkermansia spp., also improved the health of the mice. Correspondingly, both PAW consumption and increasing the intestinal abundance of Akkermansia muciniphila can mitigate IBD in mice. These findings indicate that increasing the abundance of Akkermansia muciniphila in the gut through PAW consumption or other methods may mitigate IBD in mice with clinically significant IBD.

Linkage Engineering by Harnessing Supramolecular Interactions to Fabricate 2D Hydrazone-Linked Covalent Organic Framework Platforms toward Advanced Catalysis.

Covalent organic frameworks (COFs) are an emerging class of crystalline porous polymers with tailor-made structures and functionalities. To facilitate their utilization for advanced applications, it is crucial to develop a systematic approach to control the properties of COFs, including the crystallinity, stability, and functionalities. However, such an integrated design is challenging to achieve. Herein, we report supramolecular strategy-based linkage engineering to fabricate a versatile 2D hydrazone-linked COF platform for the coordination of different transition metal ions. Intra- and intermolecular hydrogen bonding as well as electrostatic interactions in the antiparallel stacking mode were first utilized to obtain two isoreticular COFs, namely COF-DB and COF-DT. On account of suitable nitrogen sites in COF-DB, the further metalation of COF-DB was accomplished upon the complexation with seven divalent transition metal ions M(II) (M = Mn, Co, Ni, Cu, Zn, Pd, and Cd) under mild conditions. The resultant M/COF-DB exhibited extended π-conjugation, improved crystallinity, enhanced stability, and additional functionalities as compared to the parent COF-DB. Furthermore, the dynamic nature of the coordination bonding in M/COF-DB allows for the easy replacement of metal ions through a postsynthetic exchange. In particular, the coordination mode in Pd/COF-DB endows it with excellent catalytic activity and cyclic stability as a heterogeneous catalyst for the Suzuki-Miyaura cross-coupling reaction, outperforming its amorphous counterparts and Pd/COF-DT. This strategy provides an opportunity for the construction of 2D COFs with designable functions and opens an avenue to create COFs as multifunctional systems.

Modulation effect of tea consumption on gut microbiota.

Tea is one of the most popular beverages in the world and possesses a wide range of beneficial effects for human health. The modulation of tea on gut microbiota has gained much interest in recent years. The present study discussed the modulation effect of various types of tea on gut microbiota, which plays crucial roles in human health, as investigated by in vitro animal and human studies. The currently available findings from a total of 23 studies support the modulation effects of tea liquid, tea extract, and its major active components, including polyphenols, polysaccharides, and teasaponin, on gut microbiota. Overall, tea possesses prebiotic-like effect and can alleviate the gut microbiota dysbiosis induced by high-fat diet in gut microbiota, despite the detailed bacterial taxa may alter depending on the types of tea supplemented. Current evidence implies that the modulation effect on gut microbiota may be an important action mechanism underlying the beneficial effect of tea consumption in daily life and also the great potential of strategically chosen tea extract to develop functional foods.

Molecular Expansion for Constructing Porous Organic Polymers with High Surface Areas and Well-Defined Nanopores.

Construction of porous organic polymers (POPs) with high surface areas, well-defined nanopores, and excellent stability remains extremely challenging because of the unmanageable reaction process. Until now, only a few reported POPs have Brunauer-Emmett-Teller (BET) surface areas (SBET ) exceeding 3000 m2 g-1 . Herein, we demonstrate a molecular expansion strategy to integrate high surface areas, large nanopore sizes, and outstanding stability into POPs. A series of hyper-crosslinked conjugated polymers (HCCPs) with exceptional porosity are synthesized through this strategy. Specially, HCCP-6 and HCCP-11 exhibit the highest surface areas (SBET >3000 m2 g-1 ) and excellent total pore volumes (up to 3.98 cm3 g-1 ) among these HCCPs. They present decent total CH4 storage capacities of 491 and 421 mg g-1 at 80 bar and 298 K, respectively. Meanwhile, they are highly stable in harsh environments. The facile and general molecular expansion strategy would lead to improved synthetic routes of POPs for desired functions.

A Zwitterionic Ligand-Based Cationic Metal-Organic Framework for Rapidly Selective Dye Capture and Highly Efficient Cr2 O72- Removal.

A cationic metal-organic framework (MOF), [Cu2 L(H2 O)2 ]⋅(NO3 )2 ⋅5.5 H2 O (1) has been successfully synthesized from a zwitterionic ligand 1,1'-bis(3,5-dicarboxyphenyl)-4,4'-bipyridinium chlorine ([H4 L]Cl2 ). The framework of compound 1 contains classical {Cu2 (O2 C)4 } paddlewheels, and possesses typical nbo-type topology and two types of channels with sizes of 5.0 and 15.54 Å. Benefitting from the 3D cationic framework and high pore volume, compound 1 shows interesting selective adsorption ability for anionic dyes. Such material can be successfully employed in a chromatographic column to efficiently separate mixed dyes of Fluorescein Sodium and Methylene Blue. In addition, compound 1 exhibits excellent Cr2 O72- removal capacity with maximum adsorption amount of 222.5 mg g-1 , which ranks among the higher Cr2 O72- adsorption amounts of MOF materials ever reported, based on ion-exchange. The strategy to construct cationic MOFs based on zwitterionic ligands will promote the development of functional porous materials for the capture and removal of anionic pollutant species from contaminated liquid.

Post-cationic Modification of a Pyrimidine-Based Conjugated Microporous Polymer for Enhancing the Removal Performance of Anionic Dyes in Water.

Ionic porous organic polymers have attracted much attention due to their broad applications in catalysis, energy storage/conversion, proton conduction, etc. In this paper, an ionic porous organic polymer, CMP-PM-Me, was synthesized through post-synthetic modification of a pyrimidine-based conjugated microporous polymer, CMP-PM, which was constructed by the palladium catalyzed Sonogashira reaction of 1,3,5-triethynylbenzen and 2,5-dibromopyrimidine. These two polymers are porous with Brunauer-Emmett-Teller surface areas of 416 and 241 m2 g-1 for CMP-PM and CMP-PM-Me, respectively. Due to the cationic framework, CMP-PM-Me exhibits a much faster and more efficient adsorption performance to anionic dyes such as Congo red (CR) and methyl orange (MO) than that of CMP-PM with a neutral framework. The uptakes for CR are 400.0 mg g-1 for CMP-PM-Me and 344.8 mg g-1 for CMP-PM, respectively. Furthermore, CMP-PM-Me could quickly and drastically separate anionic dyes from the binary mixed solution of anionic and nonanionic dyes within a short time. This work not only enriches the family of ionic organic porous polymers and widens their synthetic utility, but also demonstrates their applications in the adsorption and separation of anionic dyes in water.

Localized instance fusion of MRI data of Alzheimer's disease for classification based on instance transfer ensemble learning.

BACKGROUND: Diagnosis of Alzheimer's disease (AD) is very important, and MRI is an effective imaging mode of Alzheimer's disease. There are many existing studies on the diagnosis of Alzheimer's disease based on MRI data. However, there are no studies on the transfer learning between different datasets (including different subjects), thereby improving the sample size of target dataset indirectly. METHODS: Therefore, a new framework method is proposed in this paper to solve this problem. First, gravity transfer is used to transfer the source domain data closer to the target data set. Secondly, the best deviation between the transferred source domain samples and the target domain samples is searched by instance transfer learning algorithm (ITL) based on wrapper mode, thereby obtaining optimal transferred domain samples. Finally, the optimal transferred domain samples and the target domain training samples are combined for classification. If the source data and the target data have different features, a feature growing algorithm is proposed to solve this problem. RESULTS: The experimental results show that the proposed method is effective regardless of different kernel functions, different number of samples and different parameters. Besides, the transferred source domain samples by ITL algorithm can enlarge the target domain training samples and assist to improve the classification accuracy significantly. CONCLUSIONS: Therefore, the study can enlarge the samples of AD by instance transfer learning, thereby being helpful for the small sample problems of AD. Since the proposed algorithm is a framework algorithm, the study is heuristics to the relevant researchers.

Therapeutics for Inflammatory-Related Diseases Based on Plasmon-Activated Water: A Review.

It is recognized that the properties of liquid water can be markedly different from those of bulk one when it is in contact with hydrophobic surfaces or is confined in nano-environments. Because our knowledge regarding water structure on the molecular level of dynamic equilibrium within a picosecond time scale is far from completeness all of water's conventionally known properties are based on inert "bulk liquid water" with a tetrahedral hydrogen-bonded structure. Actually, the strength of water's hydrogen bonds (HBs) decides its properties and activities. In this review, an innovative idea on preparation of metastable plasmon-activated water (PAW) with intrinsically reduced HBs, by letting deionized (DI) water flow through gold-supported nanoparticles (AuNPs) under resonant illumination at room temperature, is reported. Compared to DI water, the created stable PAW can scavenge free hydroxyl and 2,2-diphenyl-1-picrylhydrazyl radicals and effectively reduce NO release from lipopolysaccharide-induced inflammatory cells. Moreover, PAW can dramatically induce a major antioxidative Nrf2 gene in human gingival fibroblasts. This further confirms its cellular antioxidative and anti-inflammatory properties. In addition, innovatively therapeutic strategy of daily drinking PAW on inflammatory-related diseases based on animal disease models is demonstrated, examples being chronic kidney disease (CKD), chronic sleep deprivation (CSD), and lung cancer.

Dependency criterion based brain pathological age estimation of Alzheimer's disease patients with MR scans.

OBJECTIVES: Traditional brain age estimation methods are based on the idea that uses the real age as the training label. However, these methods ignore that there is a deviation between the real age and the brain age due to the accelerated brain aging. METHODS: This paper considers this deviation and obtains it by maximizing the correlation between the estimated brain age and the class label rather than by minimizing the difference between the estimated brain age and the real age. Firstly, set the search range of the deviation as the deviation candidates according to the prior knowledge. Secondly, use the support vector regression as the age estimation model to minimize the difference between the estimated age and the real age plus deviation rather than the real age itself. Thirdly, design the fitness function based on the correlation criterion. Fourthly, conduct age estimation on the validation dataset using the trained age estimation model, put the estimated age into the fitness function, and obtain the fitness value of the deviation candidate. Fifthly, repeat the iteration until all the deviation candidates are involved and get the optimal deviation with maximum fitness values. The real age plus the optimal deviation is taken as the brain pathological age. RESULTS: The experimental results showed that the separability of the samples was apparently improved. For normal control- Alzheimer's disease (NC-AD), normal control- mild cognition impairment (NC-MCI), and mild cognition impairment-Alzheimer's disease (MCI-AD), the average improvements were 0.164 (31.66%), 0.1284 (34.29%), and 0.0206 (7.1%), respectively. For NC-MCI-AD, the average improvement was 0.2002 (50.39%). The estimated brain pathological age could be not only more helpful for the classification of AD but also more precisely reflect the accelerated brain aging. CONCLUSION: In conclusion, this paper proposes a new kind of brain age-brain pathological age and offers an estimation method for it that can distinguish different states of AD, thereby better reflecting accelerated brain aging. Besides, the brain pathological age is most helpful for feature reduction, thereby simplifying the relevant classification algorithm.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

BACKGROUND: The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. METHODS: In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. RESULTS: Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. CONCLUSIONS: This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

[Construction and characterization of hemolysin S gene mutant strain producing hyaluronic acid].

Objective: Streptococcus equi subsp. zooepidemicus (GCS) is mainly used to produce hyaluronic acid (HA) in the industry. GCS secretes the hemolysis toxin (streptolysin S, SLS) that causes hemolysis in the host cells. Therefore, the safety of HA produced by GCS is concerned. We constructed an engineering strain, to produce commercial HA without SLS by knocking out saga. Method: The sagA of GCS was knocked out by the thermosensitive delivery vector system pJR700. The sagA mutant was identified through PCR with primers homologous to the flanking regions and SLS analysis. The yield of HA, HA molecular weight and virulence factors such as streptolysin Hylc, hyaluronate lyase, glyceraldehyde-3-phosphate dehydrogenase and cell surface proteins were determined by spectrophotometer and SDS-PAGE. Result: We constructed successfully the in-frame deletion sagA mutant strain of GCS. In the sagA mutant, HA titer increased more than 30% than that of the wild type strain and no SLS hemolytic activity was detected. Compared to the wild type strain the sagA mutant decreased the quality of surface proteins, hemolytic Hylc activity and glyceraldehyde-3-phosphate dehydrogenase activity. The activities of hyaluronidase and cell were increased in the sagA mutant. Conclusion: The sagA not only expressed hemolysis S but also regulated production of HA, the quality of surface proteins and activities of hyaluronidase, hemolysis Hylc and glyceraldehydes-3-phosphate dehydrogenase in Streptococcus equi subsp. zooepidemicus.

[Research on Diagnosis Algorithm of Parkinson's Disease Based on Speech Sample Multi-edit and Random Forest].

Parkinson's disease(PD)diagnosis based on speech data has been proved to be an effective way in recent years.There are still some problems on preprocessing samples,ensemble learning,and so on.The problems can further cause misleading of classifiers,unsatisfactory classification accuracy and stability.This paper proposed a new diagnosis algorithm of PD by combining multi-edit sample selection method and random forest.At the end of it,this paper presents a group of experiments carried out with the newest public datasets.Experimental results showed that this proposed algorithm realized the classification of the samples and the subjects of PD.Furthermore,it achieved average classification accuracy of 100% and obtained improvement of up to 29.44% compared to those provided by the subjects.This paper proposes a new speech diagnosis algorithm for PD based on instance selection;and the method algorithm has a higher and more stable classification accuracy,compared with the other algorithms.

Quantitative evaluation on activated property-tunable bulk liquid water with reduced hydrogen bonds using deconvoluted Raman spectroscopy.

Interesting properties of water with distinguishable hydrogen-bonding structure on interfacial phase or in confined environment have drawn wide attentions. However, these unique properties of water are only found within the interfacial phase and confined environment, thus, their applications are limited. In addition, quantitative evaluation on these unique properties associating with the enhancement of water's physical and chemical activities represents a notable challenge. Here we report a practicable production of free-standing liquid water at room temperature with weak hydrogen-bonded structure naming Au nanoparticles (NPs)-treated (AuNT) water via treating by plasmon-induced hot electron transfer occurred on resonantly illuminated gold NPs (AuNPs). Compared to well-known untreated bulk water (deionized water), the prepared AuNT water exhibits many distinct activities in generally physical and chemical reactions, such as high solubilities to NaCl and O2. Also, reducing interaction energy within water molecules provides lower overpotential and higher efficiency in electrolytic hydrogen production. In addition, these enhanced catalytic activities of AuNT water are tunable by mixing with deionized water. Also, most of these tunable activities are linearly proportional to its degree of nonhydrogen-bonded structure (DNHBS), which is derived from the O-H stretching in deconvoluted Raman spectrum.

Chronic ß1-adrenergic blockade enhances myocardial ß3-adrenergic coupling with nitric oxide-cGMP signaling in a canine model of chronic volume overload: new insight into mechanisms of cardiac benefit with selective ß1-blocker therapy.

The ß1-adrenergic antagonist metoprolol improves cardiac function in animals and patients with chronic heart failure, isolated mitral regurgitation (MR), and ischemic heart disease, though the molecular mechanisms remain incompletely understood. Metoprolol has been reported to upregulate cardiac expression of ß3-adrenergic receptors (ß3AR) in animal models. Myocardial ß3AR signaling via neuronal nitric oxide synthase (nNOS) activation has recently emerged as a cardioprotective pathway. We tested whether chronic ß1-adrenergic blockade with metoprolol enhances myocardial ß3AR coupling with nitric oxide-stimulated cyclic guanosine monophosphate (ß3AR/NO-cGMP) signaling in the MR-induced, volume-overloaded heart. We compared the expression, distribution, and inducible activation of ß3AR/NO-cGMP signaling proteins within myocardial membrane microdomains in dogs (canines) with surgically induced MR, those also treated with metoprolol succinate (MR+ßB), and unoperated controls. ß3AR mRNA transcripts, normalized to housekeeping gene RPLP1, increased 4.4 × 10(3)- and 3.2 × 10(2)-fold in MR and MR+ßB hearts, respectively, compared to Control. Cardiac ß3AR expression was increased 1.4- and nearly twofold in MR and MR+ßB, respectively, compared to Control. ß3AR was detected within caveolae-enriched lipid rafts (Cav3(+)LR) and heavy density, non-lipid raft membrane (NLR) across all groups. However, in vitro selective ß3AR stimulation with BRL37344 (BRL) triggered cGMP production within only NLR of MR+ßB. BRL induced Ser (1412) phosphorylation of nNOS within NLR of MR+ßB, but not Control or MR, consistent with detection of NLR-specific ß3AR/NO-cGMP coupling. Treatment with metoprolol prevented MR-associated oxidation of NO biosensor soluble guanylyl cyclase (sGC) within NLR. Metoprolol therapy also prevented MR-induced relocalization of sGCß1 subunit away from caveolae, suggesting preserved NO-sGC-cGMP signaling, albeit without coupling to ß3AR, within MR+ßB caveolae. Chronic ß1-blockade is associated with myocardial ß3AR/NO-cGMP coupling in a microdomain-specific fashion. Our canine study suggests that microdomain-targeted enhancement of myocardial ß3AR/NO-cGMP signaling may explain, in part, ß1-adrenergic antagonist-mediated preservation of cardiac function in the volume-overloaded heart.

Pressure-overload-induced subcellular relocalization/oxidation of soluble guanylyl cyclase in the heart modulates enzyme stimulation.

RATIONALE: Soluble guanylyl cyclase (sGC) generates cyclic guanosine monophophate (cGMP) upon activation by nitric oxide (NO). Cardiac NO-sGC-cGMP signaling blunts cardiac stress responses, including pressure-overload-induced hypertrophy. The latter itself depresses signaling through this pathway by reducing NO generation and enhancing cGMP hydrolysis. OBJECTIVE: We tested the hypothesis that the sGC response to NO also declines with pressure-overload stress and assessed the role of heme-oxidation and altered intracellular compartmentation of sGC as potential mechanisms. METHODS AND RESULTS: C57BL/6 mice subjected to transverse aortic constriction (TAC) developed cardiac hypertrophy and dysfunction. NO-stimulated sGC activity was markedly depressed, whereas NO- and heme-independent sGC activation by BAY 60-2770 was preserved. Total sGCα(1) and ß(1) expression were unchanged by TAC; however, sGCß(1) subunits shifted out of caveolin-enriched microdomains. NO-stimulated sGC activity was 2- to 3-fold greater in Cav3-containing lipid raft versus nonlipid raft domains in control and 6-fold greater after TAC. In contrast, BAY 60-2770 responses were >10 fold higher in non-Cav3 domains with and without TAC, declining about 60% after TAC within each compartment. Mice genetically lacking Cav3 had reduced NO- and BAY-stimulated sGC activity in microdomains containing Cav3 for controls but no change within non-Cav3-enriched domains. CONCLUSIONS: Pressure overload depresses NO/heme-dependent sGC activation in the heart, consistent with enhanced oxidation. The data reveal a novel additional mechanism for reduced NO-coupled sGC activity related to dynamic shifts in membrane microdomain localization, with Cav3-microdomains protecting sGC from heme-oxidation and facilitating NO responsiveness. Translocation of sGC out of this domain favors sGC oxidation and contributes to depressed NO-stimulated sGC activity.

Room-temperature sensor based on surface-enhanced Raman spectroscopy.

As reported in the literature, several factors, such as scattering cross sections, polarisability and wavelength suitability, contribute to increased SERS enhancement. In general, the advantage of surface-enhanced Raman scattering (SERS)-active Ag nanoparticles (NPs) is their higher SERS enhancement over Au NPs because the molar extinction coefficient of the Ag NPs is the highest of its kind among metals. Nevertheless, the corresponding SERS-active hot spots on Au are of inherently greater stability than on Ag. In this work, innovative temperature sensors based on SERS-active Au and Ag substrates prepared by sonoelectrochemical deposition-dissolution cycles (SEDDCs) are first reported. The SERS intensity of the model probe molecules of Rhodamine 6G (R6G) adsorbed on a SERS-active Ag substrate is monotonically increased from 25 to 50 °C. Moreover, this temperature-dependent intensity is linear with a slope of ca. 430 cps per °C between 25 to 45 °C. In addition, the reversibility and reusability of the developed temperature sensors are evaluated after the R6G-adsorbed sensors are alternately exposed to the temperatures of 25 and 45 °C in a sealed chamber. After every five cycles, the SERS spectra of treated substrates were recorded and compared with those of the as-prepared substrates. Experimental results indicate that SERS enhancement capability is mostly reversible based on 90% intensity of the Raman signal being maintained for the SERS-active Au substrate after 25 cycles (only 15 cycles for the Ag substrate).

Innovative fabrication of a Au nanoparticle-decorated SiO2 mask and its activity on surface-enhanced Raman scattering.

Surface-enhanced Raman scattering (SERS) utilizing the well-defined localized surface plasmon resonance (LSPR) of Ag and Au nanoparticles (NPs) under resonant irradiation has emerged as a promising spectroscopy technique for providing vibrational information on trace molecules. The Raman scattering intensity from molecules close to the surface of these finely divided metals can be significantly enhanced by a factor of more than 10(6). In addition to the high sensitivity, the reproducibility of the SERS signal is also an important parameter for its reliable application. In this work, we report on the innovative and facile fabrication of a Au NP-decorated SiO2 mask coated on indium tin oxide (ITO) glass as a SERS array substrate. First, a highly ordered porous SiO2 mask with pore sizes of 350 nm in diameter and wall thickness of 60 nm was deposited on ITO glass by using spin coating. Then, Au NPs were controllably decorated into the pores of the conductive ITO glass-bottomed SiO2 mask by using sonoelectrochemical deposition-dissolution cycling (SEDDC). Experimental results indicate that the SERS effect of Rhodamine 6G (R6G) observed on this developed substrate increases with an increase in the deposition time of Au NPs in SEDDC. The corresponding optimal enhancement factor (EF) that is obtained is ca. 6.5 × 10(7). Significantly, this system achieves an optimal reproducibility under a medium-length deposition time of Au NPs in SEDDC with a relative standard deviation (RSD) of 12% for measurements of five spots on different areas. The low RSD of the SERS signal and the large EF suggest that the developed array system can serve as an excellent spectroscopy platform for practical applications in analytical chemistry.

Electron and surface engineering of Ni2P/MnP4 heterojunction as high performance bifunctional electrocatalyst for amperage-level overall water splitting.

Producing hydrogen through electrocatalytic overall water splitting with ampere-level current density is still limited by the high cost and poor stability of electrocatalysts. In this work, a new type Ni2P/MnP4 heterojunction composite material was designed and prepared as bifunctional electrocatalyst. Based on XPS spectra and theoretical calculation, the formation of Ni2P/MnP4 heterojunction successfully modulates the local electronic structure of Ni2P and enhances the ionization of H and Ni by increasing the electron transfer rate. Moreover, the special nanovilli structure and superhydropholic/superaerophobic surface of Ni2P/MnP4 heterojunction accelerates the transfer of electrolyte and gaseous products. Benefiting from these advantages, the as-prepared Ni2P/MnP4/CF not only exhibits superior electrocatalytic performance, which can release 10 mA/cm2 current density with a low overpotential of 69 mV and 247 mV for HER and OER respectively, but also shows admirable stability of continuous overall water splitting to drive 1000 mA/cm2 for 180 h without notable activity degradation. We believe this material possesses outstanding potential for industrial applications, and our strategy may provide a new pathway to design relative materials.