Electron beam lithography-assisted fabrication of Au nano-dot array as a substrate of a correlated AFM and confocal Raman spectroscopy

This paper documents a study of an Au nano-dot array that was fabricated by electron beam lithography on a glass wafer. The patterns that had features of 100nm dots in diameter with a 2-mum pitch comprised a total area of 200x200mum(2). The dot-shaped Cr underlayer was open to the air after developing Poly(methyl methacrylate) (PMMA). When dipped into the Cr etchant, the exposed Cr layer was eliminated from the glass wafer in a short period of time. In order to ultimately fabricate the Ti/Au dot arrays, Ti and Au were deposited onto the arrays with a thickness of 2 and 40nm, respectively. The lift-off procedure was carried out in the Cr etchant using sonication in order to completely remove the residual Cr/PMMA layer. The fabricated Au nano-dot array was then immersed in an Ag enhancing solution and then into an ethanol solution containing (N-(6-(Biotinamido)hexyl)-3'-(2'-pyridyldithio)-propionamide (Biotin-HPDP). The substrate was analyzed using a correlated atomic force microscopy (AFM) and confocal Raman spectroscopy. Through this procedure, position-dependent surface-enhanced Raman spectroscopy (SERS) signals could be obtained.     

Developable polynomial surface approximation to smooth surfaces for fabrication parameters of a large curved shell plate by Differential Evolution

This paper presents a simple and efficient method to approximate a developable surface to a compound design surface by a polynomial. It is required to predict a final shape of roll bending in the fabrication of a curved shell plate. The roll bending process usually makes the cylindrical or conical curvature from an initial flat plate. It means that the final shape is developable or the surface representation has zero Gaussian curvature. The fabrication shape is important in order to estimate process parameters of roller bending.An optimization problem is formulated to determine the polynomial surface which is in the closest proximity to the design surface or the given shell plate, which is subjected to developability. The results and the efficiency of this algorithm are verified and evaluated by applying it to some shell plates which are obtained from a real ship model. The predicted bending shape becomes fundamental information in determining more process parameters for the fabrication of a compound curved shell plate.     

Effect of deposition temperature on dielectric properties of PECVD Ta2O5 thin film

Tantalum oxide film formation by plasma-enhanced chemical vapour deposition (PECVD) using TaCl₅ as a source material was examined. The effects of deposition temperature on the formation, structure and electric properties of the Ta₂O₅ film were investigated for Al/Ta₂O₅/ p-Si (MTS) capacitors. The deposition rate and refractive index increased with increasing deposition temperature. It was found that the structure of Ta₂O₅ deposited by PECVD was amorphous as-deposited. However, crystalline -Ta₂O₅ of hexagonal structure was formed by a 700 °C, 1 h heat treatment in argon. Capacitance and relative dielectric constant of the PECVD Ta₂O₅ were found to be 2.54 fF m^–2 and 23.5, respectively. The PECVD films obtained in this study have higher dielectric constants and remarkably better general film characteristics than those obtained by other deposition methods.     

Preparation and characterization of poly(2,6-dimethyl-1,4-phenylene oxide) and Nylon 6 graft copolymer

Summary A poly(2,6-dimethyl-1,4-phenylene oxide) (PPO, 1) macroinitiator having carbonylcaprolactam groups was prepared through metalation of methyl group of PPO and subsequent modification to introduce carboxyl group, acid chloride group, and finally carbonylcaprolactam group. Anionic ring opening copolymerization of -caprolactam took place onto the macroinitiator to give a graft copolymer of PPO and Nylon 6. The structure of intermediate materials and the graft copolymer were characterized by ¹H NMR, ¹³C NMR, and IR spectroscopy. Glass transition temperatures and melting temperatures of these materials are also reported. It is found that the copolymer has a microphase-separated morphology even with segment molecular weight as low as 3000.     

The expanding small heat-shock protein family, and structure predictions of the conserved "alpha-crystallin domain"

The ever-increasing number of proteins identified as belonging to the family of small heat-shock proteins (shsps) and alpha-crystallins enables us to reassess the phylogeny of this ubiquitous protein family. While the prokaryotic and fungal representatives are not properly resolved, most of the plant and animal shsps and related proteins are clearly grouped in distinct clades, reflecting a history of repeated gene duplications. The members of the shsp family are characterized by the presence of a conserved homologous "alpha-crystallin domain," which sometimes is present in duplicate. Predictions are made of secondary structure and solvent accessibility of this domain, which together with hydropathy profiles and intron positions support the presence of two similar hydrophobic beta-sheet-rich motifs, connected by a hydrophilic alpha-helical region. Together with an overview of the newly characterized members of the shsp family, these data help to define this family as being involved as stable structural proteins and as molecular chaperones during normal development and induced under pathological and stressful conditions.     

Room-Temperature-Processable Highly Reliable Resistive Switching Memory with Reconfigurability for Neuromorphic Computing and Ultrasonic Tissue Classification

Reversible metal-filamentary mechanism has been widely investigated to design an analog resistive switching memory (RSM) for neuromorphic hardware-implementation. However, uncontrollable filament-formation, inducing its reliability issues, has been a fundamental challenge. Here, an analog RSM with 3D ion transport channels that can provide unprecedentedly high reliability and robustness is demonstrated. This architecture is realized by a laser-assisted photo-thermochemical process, compatible with the back-end-of-line process and even applicable to a flexible format. These superior characteristics also lead to the proposal of a practical adaptive learning rule for hardware neural networks that can significantly simplify the voltage pulse application methodology even with high computing accuracy. A neural network, which can perform the biological tissue classification task using the ultrasound signals, is designed, and the simulation results confirm that this practical adaptive learning rule is efficient enough to classify these weak and complicated signals with high accuracy (97%). Furthermore, the proposed RSM can work as a diffusive-memristor at the opposite voltage polarity, exhibiting extremely stable threshold switching characteristics. In this mode, several crucial operations in biological nervous systems, such as Ca²⁺ dynamics and nonlinear integrate-and-fire functions of neurons, are successfully emulated. This reconfigurability is also exceedingly beneficial for decreasing the complexity of systems—requiring both drift- and diffusive-memristors.     

Towards Sustainable Agricultural Systems: A Lightweight Deep Learning Model for Plant Disease Detection

A country’s economy heavily depends on agricultural development. However, due to several plant diseases, crop growth rate and quality are highly suffered. Accurate identification of these diseases via a manual procedure is very challenging and time-consuming because of the deficiency of domain experts and low-contrast information. Therefore, the agricultural management system is searching for an automatic early disease detection technique. To this end, an efficient and lightweight Deep Learning (DL)-based framework (E-GreenNet) is proposed to overcome these problems and precisely classify the various diseases. In the end-to-end architecture, a MobileNetV3Small model is utilized as a backbone that generates refined, discriminative, and prominent features. Moreover, the proposed model is trained over the PlantVillage (PV), Data Repository of Leaf Images (DRLI), and a new Plant Composite (PC) dataset individually, and later on test samples, its actual performance is evaluated. After extensive experimental analysis, the proposed model obtained 1.00%, 0.96% and 0.99% accuracies on all three included datasets. Moreover, the proposed method achieves better inference speed when compared with other State-Of-The-Art (SOTA) approaches. In addition, a comparative analysis is conducted where the proposed strategy shows tremendous discriminative scores as compared to the various pre-trained models and other Machine Learning (ML) and DL methods.     

Photonic Reactions Leading to Fluorescence in a Polymeric System Induced by the Photothermal Effect of Magnetite Nanoparticles Using a 780 nm Multiphoton Laser

Recently, polymer‐coated magnetite (Fe₃O₄) nanoparticles (NPs) are extensively studied for applications in therapeutics or diagnostics using photothermal effect. Therefore, it is essential to understand the interactions between Fe₃O₄ NPs and polymers when optical stimuli are applied. Herein, the photonic reactions of Fe₃O₄ NPs and polymer composites upon application of a 780 nm multiphoton laser are analyzed. The photonic reactions produce unique results including fluorescence from conformationally changed polymer and low‐temperature phase transformation of Fe₃O₄ NPs. Typically, π‐conjugated chains are formed, inducing fluorescence through a series of main and side‐chain cleavage reactions of polymers with the aliphatic chain. In addition, fluorescence is detected in the cellular system by photonic reactions between Fe₃O₄ NPs and biomolecules. After multiphoton laser irradiation, light emission is detected near the intracellular Fe₃O₄ NPs, and a stronger intensity is observed in large‐sized NPs.