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SPS-Prepared High-Entropy (Bi0.2Na0.2Sr0.2Ba0.2Ca0.2)TiO3 Lead-Free Relaxor-Ferroelectric Ceramics with High Energy Storage Density

Compared to batteries and electrochemical capacitors, dielectric capacitors are widely studied because of their huge advantages in terms of charging/discharging speed and power density. In this work, high-entropy (Bi0.2Na0.2Sr0.2Ba0.2Ca0.2)TiO3 lead-free relaxor-ferroelectric ceramics were prepared by both conventional sintering (CS) and spark plasma sintering (SPS). The results showed that the ceramic prepared by SPS obtained a high energy storage density of 6.66 J/cm3 and a satisfied energy storage efficiency of 77.2% under an electric field of 430 kV/cm. This is directly related to the high density, fine grains, number of oxygen vacancies, and composition uniformity of the SPS samples. This study provides a new path for the preparation of high-entropy dielectric energy storage ceramics with high energy storage properties.

Determining the number of clusters, before finding clusters, from the susceptibility of the similarity matrix

Clustering represents a fundamental procedure to provide users with meaningful insights from an original data set. The quality of the resulting clusters is largely dependent on the correct estimation of their number, K∗, which must be provided as an input parameter in many clustering algorithms. Only very few techniques provide an automatic detection of K∗ and are usually based on cluster validity indexes which are expensive with regard to computation time. Here, we present a new algorithm which allows one to obtain an accurate estimate of K∗, without partitioning data into the different clusters. This makes the algorithm particularly efficient in handling large-scale data sets from both the perspective of time and space complexity. The algorithm, indeed, highlights the block structure which is implicitly present in the similarity matrix, and associates K∗ to the number of blocks in the matrix. We test the algorithm on synthetic data sets with or without a hierarchical organization of elements. We explore a wide range of K∗ and show the effectiveness of the proposed algorithm to identify K∗, even more accurate than existing methods based on standard internal validity indexes, with a huge advantage in terms of computation time and memory storage. We also discuss the application of the novel algorithm to the de-clustering of instrumental earthquake catalogs, a procedure finalized to identify the level of background seismic activity useful for seismic hazard assessment.

One-Dimensional π-d Conjugated Conductive Metal-Organic Framework with Dual Redox-Active Sites for High-Capacity and Durable Cathodes for Aqueous Zinc Batteries.

Aqueous Zn-based batteries (ZIBs) possess huge advantages in terms of high safety, low cost, and environmental friendliness. However, the lack of suitable cathodes with high-capacity, long-cycling, and high-rate capability limits their practical application. Herein, we present a highly crystalline one-dimensional π-d conjugated conductive metal-organic framework by coordinating ultrasmall 1,2,4,5-benzenetetramine (BTA) linkers with copper ions (Cu-BTA-H), as a cathode for ZIBs. The large ratio of active sites and dual redox mechanism of Cu-BTA-H, including the one-electron-redox reaction over copper ions (via Cu2+/Cu+) and the two-electron-redox reaction over organic ligands (via C═N/C-N), effectively enhance its reversible capacity. Meanwhile, the abundant porosity, small band gap, high crystallinity, and stable coordination structure of Cu-BTA-H endow it with fast ion/electron transport and effectively hinder the dissolution of organic ligands during cycling, respectively. Consequently, Cu-BTA-H possesses a high reversible capacity of 330 mAh g-1 at 200 mA g-1 and excellent rate performance and long-cycle stability, with a high capacity of 106.1 mAh g-1 at 2.0 A g-1 after 500 cycles and a high Coulombic efficiency of ∼100%. The proposed conductive MOFs with dual redox-active sites provide an efficient approach for constructing fast, stable, and high-capacity energy storage devices.

The power play of natural gas and crude oil in the move towards the financialization of the energy market

The financialization of the energy market has capacitated energy commodities to affect economic activities unfathomably. Despite the availability of rich literature investigating crude oil and developing its strong ties with economic and financial stability and climate change, little attention has been paid to natural gas (NG or LNG) and its potential to affect economies. Natural gas is not merely an alternative fossil fuel and a substitute for crude oil but it also presents huge advantages in terms of cost and low carbon emission. This study contributes to the literature and provides meaningful evidence on how changes in natural gas futures prices affect the equity returns in G-5 economies using the data covering almost two decades. Our results present a few exciting insights. First, we find that oil and gas prices are weakly correlated and do not reflect the dependence of gas on oil as reported in previous studies. Second, we find that although gas price changes negatively affect equity market returns in the short run, it subsides substantially in the long run. Holistically, our findings identify the hedging potential that natural gas provides to cope with the volatility in crude oil prices. We also assert that substituting oil with natural gas may improve the sustainability and stability of financial markets in G-5 economies.

Growing Bacterial Cellulose-Based Sustainable Functional Bulk Nanocomposites by Biosynthesis: Recent Advances and Perspectives

ConspectusIn recent years, with the continuous development of nanomaterials and composite preparation methods, nanocomposites are playing an increasingly important role as structural materials or functional materials due to their outstanding properties. As an important part of nanocomposites, polymer-based nanocomposites have attracted the interest of researchers, whose preparation methods have been significantly expanded with the rapid development of polymer chemistry and physics. At present, nanomaterials can be combined with the polymer matrix to form various nanocomposite materials in multiple ways, ranging from simple blending to complex processes combining surface modification and in situ polymerization. However, these polymer-based nanocomposites are still limited by their incompatibility of structural strength and functionality and unsustainability of the preparation processes.As a new type of polymer-based materials, the polymers synthesized by microorganisms, especially bacterial cellulose (BC) produced by bacteria such as Gluconacetobacter xylinus, have great potential to overcome the above-mentioned problems. On the one hand, BC has a fine nanoscale three-dimensional (3D) network with extremely high strength. The continuous nanoscale 3D network means that the nanomaterials composited with BC can be interspersed in a 3D network, effectively exerting their functionality without compromising the strength of the composites. On the other hand, the process of microbial production of BC is completed under normal pressure and temperature without using toxic reagents and solvents, which gives it a huge advantage in terms of sustainability over traditional polymer synthesis. Unfortunately, the high-density 3D nanoscale network of BC is too tight to allow the entry of nanomaterials in a homogeneous way, which brings great difficulties to the composite process of BC and nanomaterials.Challenging this problem, a new in situ composite preparation strategy called aerosol-assisted biosynthesis (AABS) strategy has been proposed as a solution. This strategy cleverly combines the deposition of nanomaterial-contained aerosol and microbial synthesis of BC, leading to uniform dispersion of various nanomaterials in the BC network and the formation of nanocomposites. In this Account, we review the development history of composite preparation methods and discuss its trend from the perspective of matching of its critical factors in dimensions of space and time. From the view of the spatiotemporal overlap of the factors, we analyze how this AABS strategy combines the BC synthesis process and the composite formation in the dimensions of space and time, resulting in the universality, tunability, designability, scalability, sustainability, and other advantages of the AABS strategy. Furthermore, we look forward to the future where AABS-based composite preparation systems can be combined with artificial intelligence and automation systems to realize fully automatic and sustainable biointelligent fabrication.

Improved Identification of Proteoforms in Top-Down Proteomics Using FAIMS with Internal CV Stepping.

In top-down (TD) proteomics, prefractionation prior to mass spectrometric (MS) analysis is a crucial step for both the high confidence identification of proteoforms and increased proteome coverage. In addition to liquid-phase separations, gas-phase fractionation strategies such as field asymmetric ion mobility spectrometry (FAIMS) have been shown to be highly beneficial in TD proteomics. However, so far, only external compensation voltage (CV) stepping has been demonstrated for TD proteomics, i.e., single CVs were applied for each run. Here, we investigated the use of internal CV stepping (multiple CVs per acquisition) for single-shot TD analysis, which has huge advantages in terms of measurement time and the amount of sample required. In addition, MS parameters were optimized for the individual CVs since different CVs target certain mass ranges. For example, small proteoforms identified mainly with more negative CVs can be identified with lower resolution and number of microscans than larger proteins identified primarily via less negative CVs. We investigated the optimal combination and number of CVs for different gradient lengths and validated the optimized settings with the low-molecular-weight proteome of CaCo-2 cells obtained using a range of different sample preparation techniques. Compared to measurements without FAIMS, both the number of identified protein groups (+60-94%) and proteoforms (+46-127%) and their confidence were significantly increased, while the measurement time remained identical. In total, we identified 684 protein groups and 2675 proteoforms from CaCo-2 cells in less than 24 h using the optimized multi-CV method.

Sulfur-doped biomass carbon as anode for high temperature potassium ion full cells

Potassium-ion batteries (PIBs) have huge advantages in terms of price and resource abundance, and are ideal substitutes for lithium-ion batteries (LIBs), but there is currently little research on their high-temperature fields. In this work, the sulfur-doped biomass carbon (CHP/S) is synthesized from biomass hemp stalks as the carbon source and sulfur powder as the sulfur source, with a high sulfur content of 18.6%. Sulfur mainly exist in the form of bonded sulfur (S-C, S-O) and have quite favorable stability. As a PIBs anode, it exhibits a specific capacity of 589 mAh g−1 (30 mA g−1) at 60 °C, which is 1.5 times the capacity at 25 °C. Electrochemical analysis and kinetic analysis indicate that the increased capacity mainly comes from the “arousal effect” of sulfur under high temperature conditions, and an increased temperature has a positive effect on the absorption and desorption performance of the material. In addition, the full cell assembled with Perylene-3, 4, 9, 10-tetracarboxylic dianhydride (PTCDA) cathode shows greater capacity (~80 mAh g−1) at 60 °C than at 25 °C. Unfortunately, the electrolyte decomposition problem caused by high temperature is easily dangerous. The result is of great significance to the research of high-temperature PIBs.

Fault diagnosis of electric submersible pump tubing string leakage

With the rapid development of the offshore oil industry, electric submersible pumps have become more and more important. They are the main pumping equipment in oil well production and have huge advantages in terms of displacement and production costs. Due to the complex structure of the electric submersible pump, the bad working environment will cause failures. The failure of tubing string leakage is a common failure in oilfields; tubing string leakage of the electric submersible pump will reduce oil production. In order to reduce the economic loss of oil well production. This paper uses PCA and Mahalanobis distance to make the tubing Fault diagnosis of leakage. The feasibility of the algorithm is verified through experiments. The result shows that it can diagnose the failure time of pipe string leakage in advance and hence help us to reduce the maintenance cost of offshore oilfields.

An effective intrusion detection approach using SVM with naïve Bayes feature embedding

Network security has become increasingly important in recent decades, while intrusion detection system plays a critical role in protecting it. Various machine learning techniques have been applied to intrusion detection, among which SVM has been considered as an effective method. However, existing studies rarely take the data quality into consideration, which is essential for constructing a well-performed intrusion detection system beyond machine learning techniques. In this paper, we propose an effective intrusion detection framework based on SVM with naïve Bayes feature embedding. Specifically, the naïve Bayes feature transformation technique is implemented on the original features to generate new data with high quality; then, an SVM classifier is trained using the transformed data to build the intrusion detection model. Experiments on multiple datasets in intrusion detection domain validate that the proposed detection method can achieve good and robust performances, with 93.75% accuracy on UNSW-NB15 dataset, 98.92% accuracy on CICIDS2017 dataset, 99.35% accuracy on NSL-KDD dataset and 98.58% accuracy on Kyoto 2006+ dataset. Furthermore, our method possesses huge advantages in terms of accuracy, detection rate and false alarm rate when compared to other methods.

Automatic Prediction and Assessment of Treatment Response in Patients with Hodgkin’s Lymphoma Using a Whole-Body DW-MRI Based Approach

The lack of validation and standardization represents the main drawback for a clear role of whole-body diffusion weighted imaging (WB-DWI) for prediction and assessment of treatment response in Hodgkin’s lymphoma (HL). We explored the reliability of an automatic approach based on the WB-DWI technique for prediction and assessment of response to treatment in patients with HL. The study included 20 HL patients, who had whole-body positron emission tomography (PET)/ magnetic resonance Imaging (MRI) performed before, during and after chemotherapy. Using the syngo.via MR Total Tumor Load tool, we automatically extracted values of diffusion volume (DV) and its associated histogram features by WB-DWI images, and evaluated their utility in predicting and assessing interim and end-of-treatment (EOT) response. The Mann–Whitney test followed by receiver operator characteristic (ROC) analysis was performed between features and their inter-time point percentage differences for patients having a complete or partial treatment response, revealing that several WB-DWI associated features allowed for prediction of interim response and both prediction and assessment of EOT response. Our proposed method offers huge advantages in terms of saving time and work, enabling clinicians to draw conclusions relating to HL treatment response in a fully automatic way, and encloses, also, all DWI advantages compared to PET/ computed tomography (CT).

A Fast Inverse Scattering Imaging Method by Applying Virtual Experiments to T-Matrix Scheme

As a non-iterative linear inverse scattering imaging method, the linear sampling method (LSM) has the merits of easy implementation and high computational efficiency. In this article, based on the LSM and T-matrix method, we propose a fast method to reconstruct the target medium parameters. First, we use the LSM qualitative imaging method to estimate the contour of the target. Then, the preprocessing information of the LSM is applied to the virtual experiment circles in the framework of T-matrix method. In this way, we avoid the inversion of the matrix and only need to solve a series of univariate equations. Numerical simulation results show that the reconstruction accuracy of the proposed method is similar to that of LSM quantitative imaging method, but it has higher calculation accuracy and wider application range. Although the calculation accuracy is not as good as the contrast source inversion (CSI) method, the proposed method has huge advantages in terms of calculation efficiency.

Optimizing enzymatic dyeing of wool and leather

This work reports on the environmental friendly enzymatic dyeing of wool and leather performed at low temperature and mild pH conditions without any dyeing auxiliaries. The substrates have been dyed with “in situ” generated pigment by means of laccase-catalyzed oxidative coupling of dye modifier 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) and dye precursor 1,3-benzenediol in a batchwise process. The process reaction variables (laccase, precursor and modifier concentrations, temperature and dyeing time) were optimized by response surface methodology using an appropriate experimental design. The temperature, precursor concentration, interaction between precursor and modifier and time are the most important factors in the dyeing process. The best-optimized wool dyeing conditions (2 h reaction time, 50 μl laccase (0.1 U), 500 mM precursor, 10 mM modifier at 40 °C) were then successfully applied onto leather material. The enzymatic-dyeing optimized process can be successfully performed on wool and leather at low temperature and mild pH obtaining different hues and depths of shades by varying the modifier concentration and time. The colouring enzymatic system has a good reusability (which has a huge advantage in terms of cost reduction) and washing durability and is comparable in terms of fastness properties to the traditional dyeing process for both wool and leather.

Base Metal Catalysis in Directed C(sp3)−H Functionalisation

AbstractDirected C(sp3)−H functionalisation has made enormous progress in recent years, but has largely been restricted to catalysis using noble metals, particularly palladium. However, since 2013, there have been prominent advances that exploit the reactivity of abundant first row transition metals for a multitude of new bond formations. The use of base metal catalysis for C−H functionalisation can provide huge advantages in terms of cost and sustainability compared to methods using noble metals. This review covers all examples, to the end 2018, of auxiliary‐assisted, base metal‐catalysed C(sp3)−H functionalisation reactions. Successful examples are reported for Fe, Co, Ni and Cu catalysis with monodentate or bidentate directing groups for C−N, C−O, C−S and C−C bond forming reactions. This review aims to highlight the current state of this field and potential for expansion and so scope and limitations are highlighted. Notably, examples to date have required sterically activated α‐disubstituted substrates, particularly propanamide derivatives with bidentate directing groups, such as 8‐aminoquinoline amides. Monodentate quinoline and thioamide directing groups have also been used with Co catalysis for C−N and C−C bond formations. Mechanistic details are provided to outline the nature of the proposed organometallic intermediates and potential reaction pathways. We hope this review will stimulate further developments in this growing and important field.magnified image

Spatial Interference Nulling Before RF Frontend for Fully Digital Phased Arrays

Fully digital arrays offer huge advantages in terms of flexibility and performance; however, they may suffer from dynamic range issues when used in the presence of in-band interferers. Higher dynamic range components can be used, but are more costly and power hungry, making the implementation of such technology impractical. This paper presents a way to mitigate those interferers by creating a spatial notch at the RF front-end with an antenna-agnostic circuit placed at the feeding network of the antenna array. This circuit creates a steerable null in the embedded element pattern that mitigates interferers at a specified incoming angle. A full mathematical model and closed form expressions of the behavior of the circuit are obtained and compared to simulated and measured results, where up to a 20 dB null in the embedded element pattern of an 1$\times$ 8 array is achieved with less than 1.5 dB of insertion loss. Finally, a real case scenario is set up with a desired signal and an interferer, which is initially saturating the receiver. The receiver successfully demodulates the signal after the null is placed in the direction of the interferer.

An electrical-heating and self-sensing shape memory polymer composite incorporated with carbon fiber felt

Shape memory polymers (SMPs) have the ability to adjust their stiffness, lock a temporary shape, and recover the permanent shape upon imposing an appropriate stimulus. They have found their way into the field of morphing structures. The electrically Joule resistive heating of the conductive composite can be a desirable stimulus to activate the shape memory effect of SMPs without external heating equipment. Electro-induced SMP composites incorporated with carbon fiber felt (CFF) were explored in this work. The CFF is an excellent conductive filler which can easily spread throughout the composite. It has a huge advantage in terms of low cost, simple manufacturing process, and uniform and tunable temperature distribution while heating. A continuous and compact conductive network made of carbon fibers and the overlap joints among them was observed from the microscopy images, and this network contributes to the high conductive properties of the CFF/SMP composites. The CFF/SMP composites can be electrical-heated rapidly and uniformly, and its’ shape recovery effect can be actuated by the electrical resistance Joule heating of the CFF without an external heater. The CFF/SMP composite get higher modulus and higher strength than the pure SMP without losing any strain recovery property. The high dependence of temperature and strain on the electrical resistance also make the composite a good self-sensing material. In general, the CFF/SMP composite shows great prospects as a potential material for the future morphing structures.

How should we measure proportionality on relative gene expression data?

Correlation is ubiquitously used in gene expression analysis although its validity as an objective criterion is often questionable. If no normalization reflecting the original mRNA counts in the cells is available, correlation between genes becomes spurious. Yet the need for normalization can be bypassed using a relative analysis approach called log-ratio analysis. This approach can be used to identify proportional gene pairs, i.e. a subset of pairs whose correlation can be inferred correctly from unnormalized data due to their vanishing log-ratio variance. To interpret the size of non-zero log-ratio variances, a proposal for a scaling with respect to the variance of one member of the gene pair was recently made by Lovell et al. Here we derive analytically how spurious proportionality is introduced when using a scaling. We base our analysis on a symmetric proportionality coefficient (briefly mentioned in Lovell et al.) that has a number of advantages over their statistic. We show in detail how the choice of reference needed for the scaling determines which gene pairs are identified as proportional. We demonstrate that using an unchanged gene as a reference has huge advantages in terms of sensitivity. We also explore the link between proportionality and partial correlation and derive expressions for a partial proportionality coefficient. A brief data-analysis part puts the discussed concepts into practice.

High-Performance Lithium-Sulfur Batteries with a Self-Assembled Multiwall Carbon Nanotube Interlayer and a Robust Electrode-Electrolyte Interface.

Elemental sulfur electrode has a huge advantage in terms of charge-storage capacity. However, the lack of electrical conductivity results in poor electrochemical utilization of sulfur and performance. This problem has been overcome to some extent previously by using a bare multiwall carbon nanotube (MWCNT) paper interlayer between the sulfur cathode and the polymeric separator, resulting in good electron transport and adsorption of dissolved polysulfides. To advance the interlayer concept further, we present here a self-assembled MWCNT interlayer fabricated by a facile, low-cost process. The Li-S cells fabricated with the self-assembled MWCNT interlayer and a high loading of 3 mg cm(-2) sulfur exhibit a first discharge specific capacity of 1112 mAh g(-1) at 0.1 C rate and retain 95.8% of the capacity at 0.5 C rate after 100 cycles as the self-assembled MWCNT interlayer facilitates good interfacial contact between the interlayer and the sulfur cathode and fast electron and lithium-ion transport while trapping and reutilizing the migrating polysulfides. The approach presented here has the potential to advance the commercialization feasibility of the Li-S batteries.

B-Cell Epitope Mapping of Severe Hemophilia a Plasma Using Well Characterized Murine Anti-Factor VIII Monoclonal Antibodies

The development of anti-factor VIII (fVIII) antibodies is a significant complication in the management of patients with hemophilia A leading to increased morbidity and treatment cost. Physicians typically base treatment decisions on inhibitor titer as measured by the Bethesda assay. Patients with inhibitor titers greater than 10 Bethesda units (BU/ml) are unlikely to respond to fVIII and are treated with bypassing agents. Using a panel of anti-fVIII monoclonal antibodies (MAbs) to different B-Cell epitopes on fVIII, we have shown that epitope specificity, inhibitor kinetics, and time to maximum inhibition are more important than inhibitor titer in predicting response to fVIII. We have previously reported the mapping of B-Cell epitopes using competition ELISA with murine anti-fVIII MAbs. Although successful, these assays required high volumes of patient plasma. The ability to map the epitope spectrum of patient plasma using a clinically feasible assay may fundamentally change how clinicians approach treatment of high titer inhibitor patients.Methods:B-Cell epitopes were mapped using competition ELISA and a slide based microarray. For the microarray, 15 anti-fVIII MAbs were printed on nitrocellulose slides at concentrations up to 1.0 mg/ml. Inhibitor patient or control samples were prepared at various dilutions containing 8μg/ml biotinylated fVIII (B-fVIII). Required plasma volumes ranged from 3.6-47.5μl. Following a 120' incubation at 37°C, test sample was added directly to the slide. After 60' at room temperature, capture of B-fVIII was detected with 0.5μg/ml Cy-5 conjugated streptavidin. Test samples were considered positive for the presence of antibody with a competing epitope when the average fluorescence intensity was reduced to less than 25% of the control value.Competition ELISAs were performed using our previously published method. For each epitope tested, a single biotinylated anti-fVIII MAb was competed against antibodies present in test plasma. Samples were considered positive when a 3 fold or greater increase in antibody titer over controls was observed.Results:To determine if the microarray method could accurately detect anti-fVIII MAbs in plasma we chose five commercially available anti-fVIII MAbs. We created control inhibitor plasmas by adding 10μg/ml of a single MAb to fVIII deficient plasma. Antibodies selected were against 4 fVIII domains, the A1, A2, C1, and C2. Two MAbs were selected to the C2 domain as previous studies indicate overlapping epitopes. Inhibitor titer for the control MAbs ranged from <2 to 23,300 BU/mg IgG. The microarray accurately detected the presence of all control MAbs tested as measured by a signal reduction greater than or equal to 75% of control. The overlapping epitopes in the C2 domain were also resolved in the microarray as signals in the overlapping portions were significantly decreased. We also tested a patient plasma using both the microarray and ELISA against this panel of five MAbs. The assays showed agreement with detection of antibodies that overlapped C1 antibody 2A9. Neither of the C2 epitopes tested were detected by the microarray. ELISA results confirmed that our test sample was negative for competing antibodies against the C2 epitopes tested by microarray, but positive for other portions of the C2 domain.Conclusions:We have shown that the B-Cell epitope mapping microarray is capable of accurately detecting anti-fVIII antibodies in patient plasma. Obtaining equivalent data by ELISA requires 37 times greater plasma volume. Although initial development testing used 5 MAbs scaling up to the full panel of 13 non-overlapping epitopes requires no larger plasma sample. The ability to map the B-cell epitopes across the fVIII protein in a single assay confers a huge advantage in terms of time, resources, and clinical feasibility. Using well-characertized murine MAbs to define B-cell epitopes may allow for functional correlations to be made. The ability to track the evolution of an individual patients' antibody epitope spectrum will allow for personalized treatment plans that could result in significantly better treatment outcomes for patients with severe Hemophilia A. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Efficient Computation of State Derivatives for Multi-Rate Integration of Object-Oriented Models

Multi-rate integration algorithms offer huge advantages in terms of simulation time savings when large, loosely coupled systems or systems with mixed fast and slow dynamics are considered. Such algorithms require the computation of different sub-sets of the state derivative vector at each time step. This paper presents a method to efficiently compute these sub-sets, starting from a continuous-time, object-oriented dynamic model. The method is demonstrated on a simple test case.

On Efficient Design of LDPC Decoders for Wireless Sensor Networks

Low density parity check (LDPC) codes are error-correcting codes that offer huge advantages in terms of coding gain, throughput and power dissipation in digital communication systems. Error correction algorithms are often implemented in hardware for fast processing to meet the real-time needs of communication systems. However,traditional hardware implementation of LDPC decoders require large amount of resources, rendering them unsuitable for use in energy constrained sensor nodes of wireless sensor networks (WSN). This paper investigates the use of short-length LDPC codes for error correction in WSN. It presents the LDPC decoder designs, implementation, resource requirement and power consumption to judge their suitability for use in the sensor nodes of WSN. Due to the complex interconnections among the variable and check nodes of LDPC decoders, it is very time consuming to use traditional hardware description language (HDL) based approach to design these decoders. This paper presents an efficient automated high-level approach to designing LDPC decoders using a collection of high-level modelingtools. The automated high-level design methodology provides a complete design flow to quickly and automatically generate, test and investigate the optimum (length) LDPC codes for wireless sensor networks to satisfy the energy constraints while providing acceptable bit-error-rate performance.