Enhancing the efficiency of alternating current driven organic light-emitting devices by optimizing the operation frequency

We report efficient and bright organic light-emitting devices operated by capacitive energy coupling. In this approach, the organic layers are enclosed between sputter-deposited hafnium dioxide layers to prevent charge carrier injection. When a sinusoidal voltage signal is applied to the electrodes, the devices emit bright green light whereas no detectable emission is generated upon application of a constant voltage. The efficiency of the process depends heavily on the frequency of the applied voltage signal. By optimizing the driving scheme, a record luminous efficacy for AC driven OLEDs of 2.7 lm/W at 500 cd/m² is achieved.     

Oxime-Modified Aluminum(III) Isopropoxide: A Promising Sol–Gel Precursor for Corrosion Resistive Nano-Alumina Coating on an Aluminum Alloy

Protection of Metals and Physical Chemistry of Surfaces - Dense alumina coatings were fabricated over aluminum alloy via dip coating method using oxime-modified aluminum(III) isopropoxide as a...     

CAMP: cluster aided multi-path routing protocol for wireless sensor networks

Wireless Networks - In this article, we propose a novel routing algorithm for wireless sensor network, which achieves uniform energy depletion across all the nodes and thus leading to prolonged...     

A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks

Real-time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO-SVM) as a binary classifier with optimally pruned extreme learning machine (OP-ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance-based lightweight K-Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79% and detection rate, i.e., 94.6% with a reduced false positive rate as compared to existing hybrid methods.     

Mechanical Characterization of Austempered Ductile Iron Obtained by Two Step Austempering Process

Austempered ductile iron (ADI) is known to have a good combination of mechanical properties due its unique ausferrite microstructure. The strength of ADI is mainly a function of the austempering temperature and the stability of ausferrite matrix. To increase the stability of the ausferritic matrix, two stage austempering processes was developed. During this investigation, in the Ist step, ductile iron specimens were austenitized at 900 °C for 60 min followed by quenching to 250 °C in salt bath. In the IInd step, after quenching at 250 °C, the salt bath was gradually heated to 350 °C, 400 °C and 450 °C respectively where specimen were soaked for 120 min. The tensile strength and impact strength were evaluated according to ASTM standards. The results were compared with that obtained by conventional austempering process by quenching directly into salt bath at 400 °C for 120 min. Both tensile and impact strength were found to have improved by two step austempering process. During Ist stage of austempering, martensite was observed while during IInd stage of austempering microstructures revealed acicular ferrite and carbon stabilized austenite. The fractographic examination revealed mixed type of fracture mode and intergranular fracture was seen under SEM. It was further observed that the tensile strength decreased whereas the impact strength increased with IInd stage of austempering temperature.     

A hybrid Taguchi-artificial neural network approach to predict surface roughness during electric discharge machining of titanium alloys

In the present study, electric discharge machining process was used for machining of titanium alloys. Eight process parameters were varied during the process. Experimental results showed that current and pulse-on-time significantly affected the performance characteristics. Artificial neural network coupled with Taguchi approach was applied for optimization and prediction of surface roughness. The experimental results and the predicted results showed good agreement. SEM was used to investigate the surface integrity. Analysis for migration of different chemical elements and formation of compounds on the surface was performed using EDS and XRD pattern. The results showed that high discharge energy caused surface defects such as cracks, craters, thick recast layer, micro pores, pin holes, residual stresses and debris. Also, migration of chemical elements both from electrode and dielectric media were observed during EDS analysis. Presence of carbon was seen on the machined surface. XRD results showed formation of titanium carbide compound which precipitated on the machined surface.