Gas sensors based on 1D and 2D materials

V této práci jsou popsány obecné vlastnosti základních senzorů plynů, zaměřuje se zejména na chemorezistivní typ a chemicky citlivý unipolární tranzistor, kterým se dále věnuje v praktické části. Dále popisuje vlastnosti vybraných 1D a 2D materiálů, metody jejich přípravy a přenosu. Praktická část práce popisuje návrh a výrobu čipů, které kombinují výše uvedené senzorické principy pro využití 1D a 2D materiálů jako aktivní vrstvy. Poté jsou popsány postupy přenosů jednotlivých materiálů na vyrobené čipy, a tyto materiály jsou charakterizovány pomocí Ramanovy spektroskopie a měření charakteristik unipolárního tranzistoru z těchto materiálů. Na závěr jsou měřeny odezvy zvolených materiálů na vybrané oxidační a redukční plyny.In this work, general properties of fundamental gas sensors are described. Thesis is mainly focused on chemoresistive and ChemFET types, which are further used in experimental part. Subsequently, properties, preparation and transfer methods of chosen 1D and 2D materials are described. Experimental part of this work describes design and fabrication of chips, which combine the sensing principals mentioned above for utilization of 1D and 2D materials as an active layer. Transfer methods of individual materials on fabricated chips are described and these materials are characterized by Raman spectroscopy and field effect transistor characteristics measurements. Finally, the response of chosen materials to oxidative and reductive gases is measured.

Machining of titanium alloys with modern tools

Diplomová práce pojednává o problematice obrábění titanových slitin. Teoretická část práce shrnuje podstatné informace o titanu a jeho slitinách se základním rozdělením, specifika obrábění těchto slitin i hlavních aplikací. Následující část je věnována moderním nástrojům pro dokončovací obrábění tvarově složitých dílů z titanových slitin s orientací na směry a velikosti silového působení těchto fréz při víceosém frézování. V další části práce jsou uvedeny výhody soudečkových nástrojů oproti kulové fréze a porovnání používaných CAM programů hyperMILL s vybraným softwarem PowerMill, včetně nabídky 5-osých strategií pro soudečkové frézy. Praktická část se zaměřuje na silovou analýzu kulové a tangenciální soudečkové frézy při jejich různých úhlech náklonů a dosahovaných parametrech drsnosti povrchu na nejznámější titanové slitině Ti-6Al-4V.The diploma thesis deals with the issue of machining titanium alloys. The theoretical part of the work summarizes essential information about titanium, its alloys with basic division, specifics of machining of these alloys and main applications. The following part is devoted to modern tools in the finishing machining of titanium alloys with an orientation on the directions and magnitudes of the force of these mills during multi-axis milling. The next part of the work presents the advantages of spherical tools over ball end mills and a comparison of the hyperMILL CAM programs used with selected PowerMill software, including a range of 5-axis strategies for end mills. The practical part focuses on the force analysis of spherical and tangential barrel milling cutters at their different angles of inclination and achieved parameters of surface roughness on the most famous alloy Ti-6Al-4V

Infinite selectivity of wet SiO2 etching in respect to Al

We propose and demonstrate an unconventional method suitable for releasing microelectromechanical systems devices containing an Al layer by wet etching using SiO2 as a sacrificial layer. We used 48% HF solution in combination with 20% oleum to keep the HF solution water-free and thus to prevent attack of the Al layer, achieving an outstanding etch rate of thermally grown SiO2 of 1 µm·min1. We also verified that this etching solution only minimally affected the Al layer, as the chip immersion for 9 min increased the Al layer sheet resistance by only 7.6%. The proposed etching method was performed in an ordinary fume hood in a polytetrafluorethylene beaker at elevated temperature of 70 °C using water bath on a hotplate. It allowed removal of the SiO2 sacrificial layer in the presence of Al without the necessity of handling highly toxic HF gas

Downsizing the Channel Length of Vertical Organic Electrochemical Transistors

Organic electrochemical transistors (OECTs) are promising building blocks for bioelectronic devices such as While the majority of OECTs use simple planar geometry, there is interest in exploring how these devices operate with much shorter channels on the submicron scale. Here, we show a practical route toward the minimization of the channel length of the transistor using traditional photolithography, enabling large-scale utilization. We describe the fabrication of such transistors using two types of conducting polymers. First, commercial solution-processed poly(dioxyethylenethiophene):poly(styrene sulfonate), PEDOT:PSS. Next, we also exploit the short channel length to support easy in situ electropolymerization of poly(dioxyethylenethiophene):tetrabutyl ammonium hexafluorophosphate, PEDOT:PF6. Both variants show different promising features, leading the way in terms of transconductance (gm), with the measured peak gm up to 68 mS for relatively thin (280 nm) channel layers on devices with the channel length of 350 nm and with widths of 50, 100, and 200 m. This result suggests that the use of electropolymerized semiconductors, which can be easily customized, is viable with vertical geometry, as uniform and thin layers can be created. Spin-coated PEDOT:PSS lags behind with the lower values of gm; however, it excels in terms of the speed of the device and also has a comparably lower off current (300 nA), leading to unusually high on/off ratio, with values up to 8.6 × 104. Our approach to vertical gap devices is simple, scalable, and can be extended to other applications where small electrochemical channels are desired

Direct measurement of oxygen reduction reactions at neurostimulation electrodes

Objective. Electric stimulation delivered by implantable electrodes is a key component of neural engineering. While factors affecting long-term stability, safety, and biocompatibility are a topic of continuous investigation, a widely-accepted principle is that charge injection should be reversible, with no net electrochemical products forming. We want to evaluate oxygen reduction reactions (ORR) occurring at different electrode materials when using established materials and stimulation protocols. Approach. As stimulation electrodes, we have tested platinum, gold, tungsten, nichrome, iridium oxide, titanium, titanium nitride, and poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate). We use cyclic voltammetry and voltage-step amperometry in oxygenated versus inert conditions to establish at which potentials ORR occurs, and the magnitudes of diffusion-limited ORR currents. We also benchmark the areal capacitance of each electrode material. We use amperometric probes (Clark-type electrodes) to quantify the O-2 and H2O2 concentrations in the vicinity of the electrode surface. O-2 and H2O2 concentrations are measured while applying DC current, or various biphasic charge-balanced pulses of amplitude in the range 10-30 mu C cm(-2)/phase. To corroborate experimental measurements, we employ finite element modelling to recreate 3D gradients of O-2 and H2O2. Main results. All electrode materials support ORR and can create hypoxic conditions near the electrode surface. We find that electrode materials differ significantly in their onset potentials for ORR, and in the extent to which they produce H2O2 as a by-product. A key result is that typical charge-balanced biphasic pulse protocols do lead to irreversible ORR. Some electrodes induce severely hypoxic conditions, others additionally produce an accumulation of hydrogen peroxide into the mM range. Significance. Our findings highlight faradaic ORR as a critical consideration for neural interface devices and show that the established biphasic/charge-balanced approach does not prevent irreversible changes in O-2 concentrations. Hypoxia and H2O2 can result in different (electro)physiological consequences

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of

A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat

A spatiotemporal machine learning framework for automated prediction and analysis of long-term Land Use/Land Cover dynamics is presented. The framework includes: (1) harmonization and preprocessing of spatial and spatiotemporal input datasets (GLAD Landsat, NPP/VIIRS) including five million harmonized LUCAS and CORINE Land Cover-derived training samples, (2) model building based on spatial k-fold cross-validation and hyper-parameter optimization, (3) prediction of the most probable class, class probabilities and model variance of predicted probabilities per pixel, (4) LULC change analysis on time-series of produced maps. The spatiotemporal ensemble model consists of a random forest, gradient boosted tree classifier, and an artificial neural network, with a logistic regressor as meta-learner. The results show that the most important variables for mapping LULC in Europe are: seasonal aggregates of Landsat green and near-infrared bands, multiple Landsat-derived spectral indices, long-term surface water probability, and elevation. Spatial cross-validation of the model indicates consistent performance across multiple years with overall accuracy (a weighted F1-score) of 0.49, 0.63, and 0.83 when predicting 43 (level-3), 14 (level-2), and five classes (level-1). Additional experiments show that spatiotemporal models generalize better to unknown years, outperforming single-year models on known-year classification by 2.7% and unknown-year classification by 3.5%. Results of the accuracy assessment using 48,365 independent test samples shows 87% match with the validation points. Results of time-series analysis (time-series of LULC probabilities and NDVI images) suggest forest loss in large parts of Sweden, the Alps, and Scotland. Positive and negative trends in NDVI in general match the land degradation and land restoration classes, with “urbanization” showing the most negative NDVI trend. An advantage of using spatiotemporal ML is that the fitted model can be used to predict LULC in years that were not included in its training dataset, allowing generalization to past and future periods, e.g. to predict LULC for years prior to 2000 and beyond 2020. The generated LULC time-series data stack (ODSE-LULC), including the training points, is publicly available via the ODSE Viewer. Functions used to prepare data and run modeling are available via the eumap library for Python

A global spectral library to characterize the world's soil

Soil provides ecosystem services, supports human health and habitation, stores carbon and regulates emissions of greenhouse gases. Unprecedented pressures on soil from degradation and urbanization are threatening agro-ecological balances and food security. It is important that we learn more about soil to sustainably manage and preserve it for future generations. To this end, we developed and analyzed a global soil visible-near infrared (vis-NIR) spectral library. It is currently the largest and most diverse database of its kind. We show that the information encoded in the spectra can describe soil composition and be associated to land cover and its global geographic distribution, which acts as a surrogate for global climate variability. We also show the usefulness of the global spectra for predicting soil attributes such as soil organic and inorganic carbon, clay, silt, sand and iron contents, cation exchange capacity, and pH. Using wavelets to treat the spectra, which were recorded in different laboratories using different spectrometers and methods, helped to improve the spectroscopic modelling. We found that modelling a diverse set of spectra with a machine learning algorithm can find the local relationships in the data to produce accurate predictions of soil properties. The spectroscopic models that we derived are parsimonious and robust, and using them we derived a harmonized global soil attribute dataset, which might serve to facilitate research on soil at the global scale. This spectroscopic approach should help to deal with the shortage of data on soil to better understand it and to meet the growing demand for information to assess and monitor soil at scales ranging from regional to global. New contributions to the library are encouraged so that this work and our collaboration might progress to develop a dynamic and easily updatable database with better global coverage. We hope that this work will reinvigorate our community's discussion towards larger, more coordinated collaborations. We also hope that use of the database will deepen our understanding of soil so that we might sustainably manage it and extend the research outcomes of the soil, earth and environmental sciences towards applications that we have not yet dreamed of