ISFET responses on a stepwise change in electrolyte concentration at constant pH

Responses on a stepwise increase of the electrolyte concentration of bare ISFETs can interfere with responses of an ISFET with an affinity membrane deposited on the gate. In this paper the responses of bare ISFETs are studied. Results of experiments and simulations are presented and the mechanism is explained

Convolutional Neural Networks for Named Entity Recognition in Images of Documents

This work researches named entity recognition (NER) with respect to images of documents with a domain-specific layout, by means of Convolutional Neural Networks (CNNs). Examples of such documents are receipts, invoices, forms and scientific papers, the latter of which are used in this work. An NER task is first performed statically, where a static number of entity classes is extracted per document. Networks based on the deep VGG-16 network are used for this task. Here, experimental evaluation shows that framing the task as a classification task, where the network classifies each bounding box coordinate separately, leads to the best network performance. Also, a multi-headed architecture is introduced, where the network has an independent fully-connected classification head per entity. VGG-16 achieves better performance with the multi-headed architecture than with its default, single-headed architecture. Additionally, it is shown that transfer learning does not improve performance of these networks. Analysis suggests that the networks trained for the static NER task learn to recognise document templates, rather than the entities themselves, and therefore do not generalize well to new, unseen templates. For a dynamic NER task, where the type and number of entity classes vary per document, experimental evaluation shows that, on large entities in the document, the Faster R-CNN object detection framework achieves comparable performance to the networks trained on the static task. Analysis suggests that Faster R-CNN generalizes better to new templates than the networks trained for the static task, as Faster R-CNN is trained on local features rather than the full document template. Finally, analysis shows that Faster R-CNN performs poorly on small entities in the image and suggestions are made to improve its performance

Tungsten trioxide (WO3) as an actuator electrode material for ISFET-based coulometric sensor-actuator systems

Acid or base concentrations can be determined by performing an acid-base titration with OH− or H+ ions, coulometrically generated by the electrolysis of water at a noble metal actuator electrode. This can be done very rapidly if the actuator electrode is in close proximity to an ISFET which is used as the indicator electrode to detect the equivalence point in the titration curve. In order to restrict the effect of interfering redox reactions at the actuator electrode during coulometric generation, electroactive actuator materials have been studied which can exchange H+ ions at a lower electrode potential than the potential of anodic water electrolysis. In this paper, electrochemically grown tungsten trioxide (WO3) is proposed as an actuator electrode material. At a WO3 electrode, H+ ions can be generated by a redox reaction at approximately 0.1 V versus SCE in a mildly alkaline solution (0.5–7 mM KOH) (anodic water electrolysis at a Pt electrode occurs at 1.5 V versus SCE). The observed thermodynamic and kinetic behaviour of the redox reaction is in good agreement with the theoretical predictions. Disadvantages of WO3 are its slow dissolution in aqueous solutions and the restriction that a titration at a WO3 electrode can only be performed in alkaline solutions