FIB-fabricated complex-shaped 3D chiral photonic silicon nanostructures.

Technologies capable of fabricating complex shaped silicon metasurfaces attract increasing attention. The focused ion beam fabrication technique is considered traditionally as causing thick damaged layers in silicon resulting in a significant rise of the optical absorption loss. We examine the structure of the FIB-fabricated nanostructures on the silicon-on-sapphire (SOS) platform and its optical characteristics before and after thermal oxidation. We show that being thermally oxidised the FIB-patterned silicon subwavelength nanostructure tends to regain its chiral optical features. The impact of the oxidation process on the silicon nanostructure optical behaviour is discussed.

Chemiluminescent multiassay of pesticides with horseradish peroxidase as a label.

Competitive chemiluminescent immunoassay based on a combination of five antibodies was used in a combination with neural network to identify and estimate amounts of three cross-reacting s-triazines (atrazine, terbythylazine and ametryn). Antibodies with different cross-reactivity towards s-triazines were immobilized in separate wells an eight-well microtiter strip. Training of neural networks was carried out with four different learning procedures. The best topology for the data measured was a net with two hidden layers with ten neurons in the first and 15 in the second layer trained with the Schmidhuber method. s-Triazine classification of environmental samples containing various analyte mixtures was correct in 70-100% of all cases depending on the type of analyte. The test developed can be proposed as an alternative field test for multianalyte environmental monitoring.

Object Generation with Neural Networks (When Spurious Memories are Useful).

Object generation constitutes a new class of problems which can be solved using neural networks. It is reverse to classification and makes a good use of the stable network modes generally considered as undesired (spurious memories). Single-class networks are considered as basic elements instead of multiclass ones, and attractors of the former are treated as potential objects of the corresponding class. Development of multiple attractors reflects the network generalization abilities and converts the single-class network into the active generator of templates. Object generating networks can also be used as moduli of recognition systems which are free from some disadvantages inherent in multi-class discriminant networks. Copyright 1996 Elsevier Science Ltd.