Electronic Skin Based on Polydopamine-Modified Superelastic Fibers with Superior Conductivity and Durability.

Owing to their excellent elasticities and adaptability as sensing materials, ionic hydrogels exhibit significant promise in the field of intelligent wearable devices. Nonetheless, molecular chains within the polymer network of hydrogels are susceptible to damage, leading to crack extension. Hence, we drew inspiration from the composite structure of the human dermis to engineer a composite hydrogel, incorporating dopamine-modified elastic fibers as a reinforcement. This approach mitigates crack expansion and augments sensor sensitivity by fostering intermolecular forces between the dopamine on the fibers, the hydrogel backbone, and water molecules. The design of this composite hydrogel elevates its breaking tensile capacity from 35 KJ to 203 KJ, significantly enhancing the fatigue resistance of the hydrogel. Remarkably, its electrical properties endure stability even after 2000 cycles of testing, and it manifests heightened sensitivity compared to conventional hydrogel configurations. This investigation unveils a novel method for crafting composite-structured hydrogels.

Edge interface microenvironment regulation of CoOOH/commercial activated carbon nano-hybrids enabling PMS activation for degrading ciprofloxacin.

Peroxymonosulfate (PMS) is widely employed to generate oxygen-containing reactive species for ciprofloxacin (CIP) degradation. Herein, cobalt oxyhydroxide @activated carbon (CoOOH@AC) was synthesized via a wet chemical sedimentation method to activate PMS for degradation of CIP. The result suggested AC can support the vertical growth of CoOOH nanosheets to expose high-activity Co-contained edges, possessing efficient PMS activation and degradation activity and catalytic stability. In the presence of 3.0 mg of optimal CoOOH@AC and 2 mM PMS, 96.8 % of CIP was degraded within 10 min, approximately 11.6 and 9.97 times greater than those of CoOOH/PMS and AC/PMS systems. Notably, it was disclosed that the optimal CoOOH@AC/PMS system still exhibited efficient catalytic performance in a wide pH range, different organics and common co-existing ions. Quenching experiments and electron paramagnetic resonance indicated that both radical and non-radical processes contributed to the degradation of CIP, with 1O2 and direct electron transfer accounting for the non-radical pathway and SO4•- and •OH serving as the main radical active species. Finally, possible CIP degradation pathways were proposed based on high-performance liquid chromatography-mass spectrometry. This study provided an alternate method for wastewater treatment based on PMS catalyzed by cobalt-based hydroxide.

Effect of SARS-CoV-2 infection and vaccine on ovarian reserve: A systematic review.

OBJECTIVE: To evaluate the effect of SARS-CoV-2 infection and vaccination on ovarian reserve. METHODS: Relevant articles were identified in the EMBASE, PubMed, and Web of Science databases from January 2020 to May 2023. Available clinical indicators of ovarian reserve, such as anti-Müllerian hormone (AMH), antral follicle count (AFC), follicle-stimulating hormone (FSH), and estradiol (E2), as well as the time interval from infection or vaccination to measurements, were assessed. RESULTS: Only 2 studies provided evidence that SARS-CoV-2 infection could damage ovarian function. In a comparison of the vaccinated and unvaccinated groups, although 1 prospective cohort study observed the transient statistically significant decrease on serum AMH levels at 3 or 6 months of follow-up, serum AMH levels remained within the normal reserve range (>1.1 ng/dl) throughout the study period. CONCLUSION: Overall, whether ovarian reserve may be affected by SARS-CoV-2 infection remains controversial and further investigations are warranted to clarify this issue. Based on the current evidence, it is safe to assume that COVID-19 vaccination does not exert any adverse effect on ovarian reserve parameters such as AMH, AFC, FSH, and E2, which will provide reassurance for women attempting to fall pregnant.

Mechanisms underlying soil microbial regulation of available phosphorus in a temperate forest exposed to long-term nitrogen addition.

With exogenous nitrogen (N) input into soil, phosphorus (P) could become a limiting nutrient for plant growth. Soil microbes play a crucial role in regulating soil P cycle and availability. P functional genes, further, regulate soil P availability. It is unclear how the addition of N in different chemical forms and rates influences the composition of soil microbes associated with P cycling and the abundance of P functional genes. A long-term experiment of N addition in three chemical forms with two levels in a temperate forest was performed to reveal the influences and the underlying mechanisms. We found that both chemical N forms and N rates selected for different P-solubilizing microbes. Ammonia form-N increased the abundances of P-solubilizing bacteria at low and high rates. Continuous N deposition included a significant decrease in soil pH and inhibited the viability and activity of bacterial communities in soil, especially the P-solubilizing bacteria. Thus, it restricted inorganic P mobilization and led to a decrease in soil available P. In addition, ammonium-N enhanced the relative abundance of most of the functional genes related to organic P mineralization, while nitrate-N presented a decrease trend. Ammonium-N significantly decreased most of the functional genes relevant to P transportation, whereas the other chemical N forms did not change them. Although N-addition consistently decreased the functional genes relevant to inorganic P solubilization, two of them (ppx and ppa) were the exceptions and showed an increase trend. N addition also decreased soil pH and altered soil properties, and indirectly contributed to the changes in community composition of P-solubilizing microbes and the abundances of multiple P functional genes. Our results provide a mechanistic explanation for the regulation of microbes on N-induced available P limitation via tuning the compositions of P-solubilizing microbes and the abundances of multiple P functional genes.

Chiral discrimination of cyclodecapeptide to anti-COVID-19 clinical candidates: a theoretical study.

Various undesirable side effects are frequently associated with isomers of chiral clinical agents. The separation of chiral medicines remains a challenging issue in the medicines research. In this work, we employed cyclic decapeptide as the host molecule and the M06-2X theoretical computational method for chiral recognition of four clinical candidate guests and their isomers, including bucillamine, molnupiravir, azvudine, and VV116, which are relevant for the treatment of COVID-19. The obtained results indicated that bucillamine and molnupiravir and their respective isomers may be distinguished by cyclic decapeptide and that some of the isomers of Azvudine and VV116 may be discriminated by cyclic decapeptide. The inclusion conformation, deformation analysis, and electrostatic potential analysis also visualized the binding modes and binding sites between cyclic peptides and medicine candidates. A series of weak interaction analyses suggest that hydrogen bonding and dispersion interactions may be the primary factors for the recognition and separation of the clinical candidates by cyclic decapeptides. Visualized analyses of noncovalent interaction, hydrogen bond interaction, and NBO, AIM topological demonstrated that the difference of dispersion interaction is not obvious between the complexes, while the type and number of hydrogen bonds are very different, hinting that hydrogen bonds might be crucial for the differentiation of molnupiravir and its isomers. These findings might provide a theoretical reference for the identification and separation of chiral compounds in host-guest interaction. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-023-02149-5.

Quantitative prediction of potential areas likely to yield Se-rich and Cd-low rice using fuzzy weights-of-evidence method.

The research of high-quality agricultural products rich in selenium and low in cadmium (Se-rich and Cd-low, respectively) is related directly to the value of agricultural products and people's food safety. Now it is still challenging to carry out development planning for Se-rich rice. By fuzzy weights-of-evidence method, the geochemical soil survey data of Se and Cd from 27,833 surface soil samples and 804 rice samples was used to predict the probability of areas, in Hubei Province, China, that will likely yield (a) Se-rich and Cd-low rice, (b) Se-rich and Cd-normal rice and (c) Se-rich and Cd-high rice. The areas predicted to likely yield Se-rich and Cd-high rice, Se-rich and Cd-normal rice, and high quality (i.e., Se-rich and Cd-low) rice cover 6542.3 km2 (5.9 %), 35,845.9 km2 (32.6 %), 12,379.7 km2 (11.3 %), respectively, of the surveyed region. According to the predictive distribution probability mapping of Se and Cd, this paper gives preliminary suggestions on the use of endogenous and exogenous Se, and Cd-reduction measures in planting Se-rich rice in different regions of Hubei Province. This study provides a new perspective for rational rice planting of Se-rich agricultural products, and it lays a foundation for the effective implementation of a geochemical soil investigation engineering project, which is of great significance for improving the economic value of Se-rich agricultural products and sustainable utilization of Se land resources.

Glutaraldehyde Crosslinked High Content of Amylose/Polyvinyl Alcohol Blend Films with Potent Tensile Strength and Young's Modulus.

In recent years, with the development of green environmental protection, starch film has become of interest due to the wide availability of sources, low price, and biodegradability. Amylose/polyvinyl alcohol (PVA) blend films crosslinked with different amounts of glutaraldehyde (GLU) were prepared by a solution casting method. The cross-linking degree, water sorption, tensile property, crystallization and section morphology of the films were examined. With the increase in glutaraldehyde concentration, the cross-linking degree of the blend film was improved. The wide-angle X-ray scattering (WAXS) result indicated that cross-linking hindered the crystallization of film. The section morphology of films was examined by scanning electron microscope (SEM). The results showed that the cross-linking degree of amylose film improved while the crystallinity decreased with the increase in glutaraldehyde content. Cross-linking had no obvious effect on the water sorption property of the blend films. The cross-linking modification significantly enhanced the tensile strength and Young's modulus, while it reduced the elongation at break of the blend films. It was found that the film with 0.5 wt % glutaraldehyde possessed the best performance: the tensile strength increased by 115%, while the elongation at break decreased by 18% even at high relative humidity (RH) of 90% compared to non-crosslinked films. The developed amylose/PVA blend films have promising application prospects as agricultural mulch films and packaging materials.

Enhanced Cell Osteogenesis and Osteoimmunology Regulated by Piezoelectric Biomaterials with Controllable Surface Potential and Charges.

Bone regeneration is a well-orchestrated process involving electrical, biochemical, and mechanical multiple physiological cues. Electrical signals play a vital role in the process of bone repair. The endogenous potential will spontaneously form on defect sites, guide the cell behaviors, and mediate bone healing when the bone fracture occurs. However, the mechanism on how the surface charges of implant potentially guides osteogenesis and osteoimmunology has not been clearly revealed yet. In this study, piezoelectric BaTiO3/ß-TCP (BTCP) ceramics are prepared by two-step sintering, and different surface charges are established by polarization. In addition, the cell osteogenesis and osteoimmunology of BMSCs and RAW264.7 on different surface charges were explored. The results showed that the piezoelectric constant d33 of BTCP was controllable by adjusting the sintering temperature and rate. The polarized BTCP with a negative surface charge (BTCP-) promoted protein adsorption and BMSC extracellular Ca2+ influx. The attachment, spreading, migration, and osteogenic differentiation of BMSCs were enhanced on BTCP-. Additionally, the polarized BTCP ceramics with a positive surface charge (BTCP+) significantly inhibited M1 polarization of macrophages, affecting the expression of the M1 marker in macrophages and changing secretion of proinflammatory cytokines. It in turn enhanced osteogenic differentiation of BMSCs, suggesting that positive surface charges could modulate the bone immunoregulatory properties and shift the immune microenvironment to one that favored osteogenesis. The result provides an alternative method of synergistically modulating cellular immunity and the osteogenesis function and enhancing the bone regeneration by fabricating piezoelectric biomaterials with electrical signals.

Optimal Design of Acoustic Metamaterial of Multiple Parallel Hexagonal Helmholtz Resonators by Combination of Finite Element Simulation and Cuckoo Search Algorithm.

To achieve the broadband sound absorption at low frequencies within a limited space, an optimal design of joint simulation method incorporating the finite element simulation and cuckoo search algorithm was proposed. An acoustic metamaterial of multiple parallel hexagonal Helmholtz resonators with sub-wavelength dimensions was designed and optimized in this research. First, the initial geometric parameters of the investigated acoustic metamaterials were confirmed according to the actual noise reduction requirements to reduce the optimization burden and improve the optimization efficiency. Then, the acoustic metamaterial with the various depths of the necks was optimized by the joint simulation method, which combined the finite element simulation and the cuckoo search algorithm. The experimental sample was prepared using the 3D printer according to the obtained optimal parameters. The simulation results and experimental results exhibited excellent consistency. Compared with the derived sound absorption coefficients by theoretical modeling, those achieved in the finite element simulation were closer to the experimental results, which also verified the accuracy of this optimal design method. The results proved that the optimal design method was applicable to the achievement of broadband sound absorption with different low frequency ranges, which provided a novel method for the development and application of acoustic metamaterials.

Adversarial Reinforcement Learning With Object-Scene Relational Graph for Video Captioning.

Existing video captioning methods usually ignore the important fine-grained semantic attributes, the video diversity, as well as the association and motion state between objects within and between frames. Thus, they cannot adapt to small sample data sets. To solve the above problems, this paper proposes a novel video captioning model and an adversarial reinforcement learning strategy. Firstly, an object-scene relational graph model is designed based on the object detector and scene segmenter to express the association features. The graph is encoded by the graph neural network to enrich the expression of visual features. Meanwhile, a trajectory-based feature representation model is designed to replace the previous data-driven method to extract motion and attribute information, so as to analyze the object motion in the time domain and establish the connection between the visual content and language under small data sets. Finally, an adversarial reinforcement learning strategy and a multi- branch discriminator are designed to learn the relationship between the visual content and corresponding words so that rich language knowledge is integrated into the model. Experimental results on three standard datasets and one small sample dataset indicate that our proposed method achieves state-of-the-art performance. Also, the ablation experiments and visualization results verify the effectiveness of proposed each strategy.

Genetic and Epigenetic Diversity of Protein Molecules Related to SARSCoV- 2 Entry: Where We Are and Where We Should Go?

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), has swept the whole world and brought about public health crisis of unprecedented proportions. In the process of SARS-CoV-2 entry, angiotensin-converting enzyme 2 plays a key role. In addition, other protein molecules, such as transmembrane protease/serine 2, FURIN, Cathepsin L, and a disintegrin and metalloproteinase 17 will also affect the interaction between virus and host cells. Since the variations in the virus and human populations could determine the transmissibility of the virus and influence an individual's susceptibility to SARS-CoV-2 infection and disease outcome, research on the variations of the above protein molecules and their role in COVID-19 is in full swing. In this review, we systematically reviewed viral and host genetic variations related to SARSCoV- 2 entry, as well as the relationship between the diversity of these variations and the COVID-19 pandemic. We aim to provide better insights into the transmission and pathogenesis of COVID-19 from the perspective of genetic variants and epigenetic factors so as to prevent, control, and treat COVID-19, especially among high-risk populations with genetic risk variants.

Regulation of Inflammatory Response and Osteogenesis to Citrate-Based Biomaterials through Incorporation of Alkaline Fragments.

A proper pH microenvironment is crucial to mobilizing regeneration function of biomaterials. Neutralizing the acidity in bone defects with alkaline substances is a promising strategy to create favorable environments for cell proliferation and bone repair. In this study, to neutralize the acidity and reduce the inflammation caused by the rapid release of citric acid, a novel citrate-based biodegradable elastomeric poly(citric acid-1,8-octanediol-1,4-bis(2-hydroxyethyl)piperazine (BHEp)) (POPC) is synthesized with the introduction of the alkaline fragment BHEp, and then POPC/ß-tricalcium phosphate (ß-TCP) porous scaffolds are fabricated by 3D printing technique. The results reveal that the alkaline fragment BHEp effectively corrects the acid environment and improves the biocompatibility, cells affinity and promoted cell adhesion, and proliferation of POPC. Furthermore, the improved pH of POPC15/ß-TCP (PTCP15) enhances the adhesion and the proliferation of rabbit bone marrow mesenchymal stem cells, and the expression of osteogenesis-related genes. Moreover, PTCP15 scaffolds relieve inflammatory response and switch RAW 264.7 toward a prohealing extreme. The rat femoral defect model further demonstrates good biocompatibility and enhanced bone regeneration of PTCP15. In conclusion, the results offer a promising approach for biodegradable polymers to address the degradation acidity issue. Meanwhile, a positive regulation strategy is provided for biopolymer to enhance cell proliferation, osteogenic differentiation, and bone repair.

Object Detection Based on Faster R-CNN Algorithm with Skip Pooling and Fusion of Contextual Information.

Deep learning is currently the mainstream method of object detection. Faster region-based convolutional neural network (Faster R-CNN) has a pivotal position in deep learning. It has impressive detection effects in ordinary scenes. However, under special conditions, there can still be unsatisfactory detection performance, such as the object having problems like occlusion, deformation, or small size. This paper proposes a novel and improved algorithm based on the Faster R-CNN framework combined with the Faster R-CNN algorithm with skip pooling and fusion of contextual information. This algorithm can improve the detection performance under special conditions on the basis of Faster R-CNN. The improvement mainly has three parts: The first part adds a context information feature extraction model after the conv5_3 of the convolutional layer; the second part adds skip pooling so that the former can fully obtain the contextual information of the object, especially for situations where the object is occluded and deformed; and the third part replaces the region proposal network (RPN) with a more efficient guided anchor RPN (GA-RPN), which can maintain the recall rate while improving the detection performance. The latter can obtain more detailed information from different feature layers of the deep neural network algorithm, and is especially aimed at scenes with small objects. Compared with Faster R-CNN, you only look once series (such as: YOLOv3), single shot detector (such as: SSD512), and other object detection algorithms, the algorithm proposed in this paper has an average improvement of 6.857% on the mean average precision (mAP) evaluation index while maintaining a certain recall rate. This strongly proves that the proposed method has higher detection rate and detection efficiency in this case.

Spatial-Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking.

Multi-object tracking (MOT) plays a crucial role in various platforms. Occlusion and insertion among targets, complex backgrounds and higher real-time requirements increase the difficulty of MOT problems. Most state-of-the-art MOT approaches adopt the tracking-by-detection strategy, which relies on compute-intensive sliding windows or anchoring schemes to detect matching targets or candidates in each frame. In this work, we introduce a more efficient and effective spatial-temporal attention scheme to track multiple objects in various scenarios. Using a semantic-feature-based spatial attention mechanism and a novel Motion Model, we address the insertion and location of candidates. Some online-learned target-specific convolutional neural networks (CNNs) were used to estimate target occlusion and classify by adapting the appearance model. A temporal attention mechanism was adopted to update the online module by balancing current and history frames. Extensive experiments were performed on Karlsruhe Institute of Technologyand Toyota Technological Institute (KITTI) benchmarks and an Armored Target Tracking Dataset (ATTD) built for ground-armored targets. Experimental results show that the proposed method achieved outstanding tracking performance and met the actual application requirements.

Improved Faster R-CNN Traffic Sign Detection Based on a Second Region of Interest and Highly Possible Regions Proposal Network.

Traffic sign detection systems provide important road control information for unmanned driving systems or auxiliary driving. In this paper, the Faster region with a convolutional neural network (R-CNN) for traffic sign detection in real traffic situations has been systematically improved. First, a first step region proposal algorithm based on simplified Gabor wavelets (SGWs) and maximally stable extremal regions (MSERs) is proposed. In this way, the region proposal a priori information is obtained and will be used for improving the Faster R-CNN. This part of our method is named as the highly possible regions proposal network (HP-RPN). Second, in order to solve the problem that the Faster R-CNN cannot effectively detect small targets, a method that combines the features of the third, fourth, and fifth layers of VGG16 to enrich the features of small targets is proposed. Third, the secondary region of interest method to enhance the feature of detection objects and improve the classification capability of the Faster R-CNN is proposed. Finally, a method of merging the German traffic sign detection benchmark (GTSDB) and Chinese traffic sign dataset (CTSD) databases into one larger database to increase the number of database samples is proposed. Experimental results show that our method improves the detection performance, especially for small targets.

Analysis of Weak Fault in Hydraulic System Based on Multi-scale Permutation Entropy of Fault-Sensitive Intrinsic Mode Function and Deep Belief Network.

With the aim of automatic recognition of weak faults in hydraulic systems, this paper proposes an identification method based on multi-scale permutation entropy feature extraction of fault-sensitive intrinsic mode function (IMF) and deep belief network (DBN). In this method, the leakage fault signal is first decomposed by empirical mode decomposition (EMD), and fault-sensitive IMF components are screened by adopting the correlation analysis method. The multi-scale entropy feature of each screened IMF is then extracted and features closely related to the weak fault information are then obtained. Finally, DBN is used for identification of fault diagnosis. Experimental results prove that this identification method has an ideal recognition effect. It can accurately judge whether there is a leakage fault, determine the degree of severity of the fault, and can diagnose and analyze hydraulic weak faults in general.

Heterostructured ZnFe2O4/Fe2TiO5/TiO2 Composite Nanotube Arrays with an Improved Photocatalysis Degradation Efficiency Under Simulated Sunlight Irradiation.

To improve the visible light absorption and photocatalytic activity of titanium dioxide nanotube arrays (TONTAs), ZnFe2O4 (ZFO) nanocrystals were perfused into pristine TONTA pipelines using a novel bias voltage-assisted perfusion method. ZFO nanocrystals were well anchored on the inner walls of the pristine TONTAs when the ZFO suspensions (0.025 mg mL-1) were kept under a 60 V bias voltage for 1 h. After annealing at 750 °C for 2 h, the heterostructured ZFO/Fe2TiO5 (FTO)/TiO2 composite nanotube arrays were successfully obtained. Furthermore, Fe3+ was reduced to Fe2+ when solid solution reactions occurred at the interface of ZFO and the pristine TONTAs. Introducing ZFO significantly enhanced the visible light absorption of the ZFO/FTO/TONTAs relative to that of the annealed TONTAs. The coexistence of type I and staggered type II band alignment in the ZFO/FTO/TONTAs facilitated the separation of photogenerated electrons and holes, thereby improving the efficiency of the ZFO/FTO/TONTAs for photocatalytic degradation of methylene blue when irradiated with simulated sunlight.

Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs.

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.

Highly improved ethanol gas-sensing performance of mesoporous nickel oxides nanowires with the stannum donor doping.

Mesoporous nickel oxides (NiO) and stannum(Sn)-doped NiO nanowires (NWs) were synthesized by using SBA-15 templates with the nanocasting method. X-ray diffraction, transmission electron microscope, energy dispersive spectrometry, nitrogen adsorption/desorption isotherm and UV-vis spectrum were used to characterize the phase structure, components and microstructure of the as-prepared samples. The gas-sensing analysis indicated that the Sn-doping could greatly improve the ethanol sensitivity for mesoporous NiO NWs. With the increasing Sn content, the ethanol sensitivity increased from 2.16 for NiO NWs up to the maximum of 15.60 for Ni0.962Sn0.038O1.038, and then decreased to 12.24 for Ni0.946Sn0.054O1.054 to 100 ppm ethanol gas at 340 °C. The high surface area from the Sn-doping improved the adsorption of oxygen on the surface of NiO NWs, resulting in the smaller surface resistance in air. Furthermore, owing to the recombination of the holes in hole-accumulation lay with the electrons from the donor impurity level and the increasing the body defects for Sn-doping, the total resistance in ethanol gas enhanced greatly. It was concluded that the sensitivity of Sn-doped NiO NWs based sensor could be greatly improved by the higher surface area and high-valence donor substitution from Sn-doping.

Facile Fabrication of Ordered Anodized Aluminum Oxide Membranes with Controlled Pore Size by Improved Hard Anodization.

We have fabricated highly ordered anodized aluminum oxide (AAO) membranes with different diameter through improved hard anodization (HA) at high temperature. This process can generate thick AAO membranes (30 µm) in a short anodizing time with high growth rate 20-60 µm h-1 which is much faster than that in traditional mild two-step anodization. We enlarged the AAO pore diameter by adjusting the voltage rise rate at the same time, which has a great influence on current density and temperature. The AAO pore diameter varies from 60-110 nm to 160-190 nm. The pore diameter (Dp) of the AAO prepared by this improved process is much larger than that prepared by HA (40-60 nm) when H2C2O4 as electrolyte. It can expand potential use of the AAO membranes such as for the template-based synthesis of nanowires or nanotubes with modulated diameters and also for practical separation technology. We also has used the AAO with different diameters prepared by this improved HA to fabricate Co nanowires and γ-Fe2O3 superparamagnetic nanorods.