8 resultados para Single Intraoperative Application
em Instituto Politécnico do Porto, Portugal
Resumo:
The electroactivity of butylate (BTL) is studied by cyclic voltammetry (CV) and square wave voltammetry (SWV) at a glassy carbon electrode (GCE) and a hanging mercury drop electrode (HMDE). Britton–Robinson buffer solutions of pH 1.9–11.5 are used as supporting electrolyte. CV voltammograms using GCE show a single anodic peak regarding the oxidation of BTL at +1.7V versus AgCl/ Ag, an irreversible process controlled by diffusion. Using a HMDE, a single cathodic peak is observed, at 1.0V versus AgCl/Ag. The reduction of BTL is irreversible and controlled by adsorption. Mechanism proposals are presented for these redox transformations. Optimisation is carried out univaryingly. Linearity ranges were 0.10–0.50 mmol L-1 and 2.0–9.0 µmolL-1 for anodic and cathodic peaks, respectively. The proposed method is applied to the determination of BTL in waters. Analytical results compare well with those obtained by an HPLC method.
Resumo:
A flow injection analysis (FIA) system having a chlormequat selective electrode is proposed. Several electrodes with poly(vinyl chloride) based membranes were constructed for this purpose. Comparative characterization suggestedthe use of membrane with chlormequat tetraphenylborate and dibutylphthalate. On a single-line FIA set-up, operating with 1x10-2 mol L-1 ionic strength and 6.3 pH, calibration curves presented slopes of 53.6±0.4mV decade-1 within 5.0x10-6 and1.0x10-3 mol L-1, andsquaredcorrelation coefficients >0.9953. The detection limit was 2.2x10-6 mol L-1 and the repeatability equal to ±0.68mV (0.7%). A dual-channel FIA manifold was therefore constructed, enabling automatic attainment of previous ionic strength andpH conditions and thus eliminating sample preparation steps. Slopes of 45.5±0.2mV decade -1 along a concentration range of 8.0x10-6 to 1.0x10-3 mol L-1 with a repeatability ±0.4mV (0.69%) were obtained. Analyses of real samples were performed, and recovery gave results ranging from 96.6 to 101.1%.
Resumo:
In this paper we survey the most relevant results for the prioritybased schedulability analysis of real-time tasks, both for the fixed and dynamic priority assignment schemes. We give emphasis to the worst-case response time analysis in non-preemptive contexts, which is fundamental for the communication schedulability analysis. We define an architecture to support priority-based scheduling of messages at the application process level of a specific fieldbus communication network, the PROFIBUS. The proposed architecture improves the worst-case messages’ response time, overcoming the limitation of the first-come-first-served (FCFS) PROFIBUS queue implementations.
Resumo:
Many-core platforms based on Network-on-Chip (NoC [Benini and De Micheli 2002]) present an emerging technology in the real-time embedded domain. Although the idea to group the applications previously executed on separated single-core devices, and accommodate them on an individual many-core chip offers various options for power savings, cost reductions and contributes to the overall system flexibility, its implementation is a non-trivial task. In this paper we address the issue of application mapping onto a NoCbased many-core platform when considering fundamentals and trends of current many-core operating systems, specifically, we elaborate on a limited migrative application model encompassing a message-passing paradigm as a communication primitive. As the main contribution, we formulate the problem of real-time application mapping, and propose a three-stage process to efficiently solve it. Through analysis it is assured that derived solutions guarantee the fulfilment of posed time constraints regarding worst-case communication latencies, and at the same time provide an environment to perform load balancing for e.g. thermal, energy, fault tolerance or performance reasons.We also propose several constraints regarding the topological structure of the application mapping, as well as the inter- and intra-application communication patterns, which efficiently solve the issues of pessimism and/or intractability when performing the analysis.
Resumo:
Adhesive bonding as a joining or repair method has a wide application in many industries. Repairs with bonded patches are often carried out to re-establish the stiffness at critical regions or spots of corrosion and/or fatigue cracks. Single and double-strap repairs (SS and DS, respectively) are a viable option for repairing. For the SS repairs, a patch is adhesively-bonded on one of the structure faces. SS repairs are easy to execute, but the load eccentricity leads to peel peak stresses at the overlap edges. DS repairs involve the use of two patches, one on each face of the structure. These are more efficient than SS repairs, due to the doubling of the bonding area and suppression of the transverse deflection of the adherends. Shear stresses also become more uniform as a result of smaller differential straining. The experimental and Finite Element (FE) study presented here for strength prediction and design optimization of bonded repairs includes SS and DS solutions with different values of overlap length (LO). The examined values of LO include 10, 20 and 30 mm. The failure strengths of the SS and DS repairs were compared with FE results by using the Abaqus® FE software. A Cohesive Zone Model (CZM) with a triangular shape in pure tensile and shear modes, including the mixed-mode possibility for crack growth, was used to simulate fracture of the adhesive layer. A good agreement was found between the experiments and the FE simulations on the failure modes, elastic stiffness and strength of the repairs, showing the effectiveness and applicability of the proposed FE technique in predicting strength of bonded repairs. Furthermore, some optimization principles were proposed to repair structures with adhesively-bonded patches that will allow repair designers to effectively design bonded repairs.
Resumo:
Selenium modified ruthenium electrocatalysts supported on carbon black were synthesized using NaBH4 reduction of the metal precursor. Prepared Ru/C electrocatalysts showed high dispersion and very small averaged particle size. These Ru/C electrocatalysts were subsequently modified with Se following two procedures: (a) preformed Ru/carbon catalyst was mixed with SeO2 in xylene and reduced in H2 and (b) Ru metal precursor was mixed with SeO2 followed by reduction with NaBH4. The XRD patterns indicate that a pyrite-type structure was obtained at higher annealing temperatures, regardless of the Ru:Se molar ratio used in the preparation step. A pyrite-type structure also emerged in samples that were not calcined; however, in this case, the pyrite-type structure was only prominent for samples with higher Ru:Se ratios. The characterization of the RuSe/C electrocatalysts suggested that the Se in noncalcined samples was present mainly as an amorphous skin. Preliminary study of activity toward oxygen reduction reaction (ORR) using electrocatalysts with a Ru:Se ratio of 1:0.7 indicated that annealing after modification with Se had a detrimental effect on their activity. This result could be related to the increased particle size of crystalline RuSe2 in heat-treated samples. Higher activity of not annealed RuSe/C catalysts could also be a result of the structure containing amorphous Se skin on the Ru crystal. The electrode obtained using not calcined RuSe showed a very promising performance with a slightly lower activity and higher overpotential in comparison with a commercial Pt/C electrode. Single wall carbon nanohorns (SWNH) were considered for application as ORR electrocatalysts' supports. The characterization of SWNH was carried out regarding their tolerance toward strong catalyzed corrosion conditions. Tests indicated that SWNH have a three times higher electrochemical surface area (ESA) loss than carbon black or Pt commercial electrodes.
Resumo:
Monitoring organic environmental contaminants is of crucial importance to ensure public health. This requires simple, portable and robust devices to carry out on-site analysis. For this purpose, a low-temperature co-fired ceramics (LTCC) microfluidic potentiometric device (LTCC/μPOT) was developed for the first time for an organic compound: sulfamethoxazole (SMX). Sensory materials relied on newly designed plastic antibodies. Sol–gel, self-assembling monolayer and molecular-imprinting techniques were merged for this purpose. Silica beads were amine-modified and linked to SMX via glutaraldehyde modification. Condensation polymerization was conducted around SMX to fill the vacant spaces. SMX was removed after, leaving behind imprinted sites of complementary shape. The obtained particles were used as ionophores in plasticized PVC membranes. The most suitable membrane composition was selected in steady-state assays. Its suitability to flow analysis was verified in flow-injection studies with regular tubular electrodes. The LTCC/μPOT device integrated a bidimensional mixer, an embedded reference electrode based on Ag/AgCl and an Ag-based contact screen-printed under a micromachined cavity of 600 μm depth. The sensing membranes were deposited over this contact and acted as indicating electrodes. Under optimum conditions, the SMX sensor displayed slopes of about −58.7 mV/decade in a range from 12.7 to 250 μg/mL, providing a detection limit of 3.85 μg/mL and a sampling throughput of 36 samples/h with a reagent consumption of 3.3 mL per sample. The system was adjusted later to multiple analyte detection by including a second potentiometric cell on the LTCC/μPOT device. No additional reference electrode was required. This concept was applied to Trimethoprim (TMP), always administered concomitantly with sulphonamide drugs, and tested in fish-farming waters. The biparametric microanalyzer displayed Nernstian behaviour, with average slopes −54.7 (SMX) and +57.8 (TMP) mV/decade. To demonstrate the microanalyzer capabilities for real applications, it was successfully applied to single and simultaneous determination of SMX and TMP in aquaculture waters.
Resumo:
High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.