The present work aims to evaluate the treatment of the effluent from the textile industry via advanced oxidative processes of photo-Fenton assisted by different sources (natural sunlight, UV-A or ...visible LED lamps). To identify the best operating conditions, a factorial design was carried out for each process. It was observed that after the optimization of the processes, chemical oxygen demand (COD) removals greater than 88% were achieved. In addition, it was observed that the use of the LED lamp required lower reagent concentrations compared to solar and UV-A sources. A kinetic study was carried out under the best conditions obtained and it was observed that the sources showed rapid evolution, reaching a COD removal equilibrium with 30 min of reaction. Reagent monitoring was also carried out, and it was observed that they were not limiting to the reaction. Phytotoxicity analysis was also satisfactory since the treated effluents allowed a higher relative growth and germination index of the cucumber roots compared to the raw effluent. Finally, the cost analysis indicated that the use of LED lamps resulted in a reduction in electrical consumption compared to the UV-A lamp, as well as a reduction in the cost of reagents due to the lower concentration of reagents required compared to processes assisted by natural sunlight and UV-A.
After preparing composite PSGO films by coating electrospun polystyrene (PS) fibers with graphene oxide (GO), we examined their use as dye adsorbents for water remediation. The GO, which was ...synthesized via a modified Hummers' method, was adsorbed on the surface of the PS fibers. Through X-ray diffraction (XRD), Fourier Transform Infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and thermogravimetry (TGA) analyses, we characterized the structure and morphology of the composite films, confirming that the GO was successfully incorporated onto the PS fibers. SEM images revealed that the PS fibers exhibited a smooth surface and that the GO was uniformly deposited on them. TGA analysis indicated that the PSGO is composed of ∼13 wt% GO and ∼87 wt% PS, and that both components exhibited similar thermogravimetric behavior. We examined the removal of the methylene blue (MB) dye from aqueous solutions as a model system to assess the adsorptive properties of the PSGO films. The composite films had a removal capacity that was approximately 2.3 times greater than that of pure PS membranes. For all MB concentrations investigated, the removal of the dye, which was very fast in the first 30 min, the equilibrium value of the adsorption capacity (qe = 114 mg g−1) was reached after 120 min. The kinetics of the adsorption process was best described by the pseudo-second-order (PSO) model, which predicted an adsorption capacity (qt) of 116.69 mg g−1.
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•Fabrication and use of electrospun polystyrene fiber covered with graphene oxide.•GO-coated polystyrene is a promising adsorbent of the Methylene Blue dye.•Outstanding adsorptive capacity in comparison to other reported adsorbents.
Textile effluent is one of the most hazardous types of wastewater for both the environment and human health when discharged without proper treatment. This work stands out as one of the first to ...evaluate the parameters for the application of graphene oxide (GO) to treat real textile wastewater. A comparative analysis was conducted to investigate the removal efficiencies of turbidity and apparent colour from raw textile wastewater using GO. The effects of different parameters, such as GO dosage, pH, and contact time were discussed, considering a removal mechanism based on the salting out effect. Results regarding treatment using GO followed by centrifugation showed that in >1 hour nearly 90% turbidity was decreased, and an apparent colour removal efficiency over 76% was recorded, which is twice the value obtained with the conventional treatment applied in textile mills. Over 60% chemical oxygen demand was reduced. Tests using GO followed by sedimentation also revealed promising results, showing removal efficiencies of 66% and 88% for apparent colour and turbidity, respectively. These results suggest that GO could be promising for real wastewater treatment.
Real textile wastewater treatment using graphene‐based material.
ABSTRACT The functionalization of graphene nanosheets is the cutting edge of materials sciences nowadays. Such research promotes the development of innovative, low cost and highly capable sorbents. ...This review article aims to assemble the available information on functionalized graphene used for the adsorption of organic pollutants and establishes a critical comparison between the data reported in the literature. Various optimal experimental conditions (pH, temperature, contact time, adsorbent dosage) and adsorbent characterization methods (FTIR, Raman, XPS spectra, XRD, TEM and AFM) have been listed to enlighten adsorption mechanisms, capacity and limiting aspects. Moreover, adsorption isotherms, kinetics and thermodynamic data of different functionalized graphene-based materials towards a wide range of organic pollutants were analyzed and tabulated. In each evaluation topic, environmental and human health protection is subject for discussion, as well as the scientific breakthrough works available in high impact journals in the field.
Changes in electroencephalography (EEG) amplitude modulations have recently been linked with early-stage Alzheimer's disease (AD). Existing tools available to perform such analysis (e.g., detrended ...fluctuation analysis), however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD) showed significant differences in amplitude modulations between the three groups. Most notably, i) delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD), ii) delta modulation of the theta band appeared with an increase in severity, and iii) delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer's disease, but also to monitor its progression.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The Internet of Things (IoT) involves a wide variety of heterogeneous technologies and resource-constrained devices that interact with each other. Due to such constraints, IoT devices usually require ...lightweight protocols that optimize the use of resources and energy consumption. Among the different commercial IoT devices, Bluetooth and Bluetooth Low Energy (BLE)-based beacons, which broadcast periodically certain data packets to notify their presence, have experienced a remarkable growth, specially due to their application in indoor positioning systems. This article proposes a family of protocols named Lightweight Protocol for Sensors (LP4S) that provides fast responses and enables plug-and-play mechanisms that allow IoT telemetry systems to discover new nodes and to describe and auto-register the sensors and actuators connected to a beacon. Thus, three protocols are defined depending on the beacon hardware characteristics: LP4S-6 (for resource-constraint beacons), LP4S-X (for more powerful beacons) and LP4S-J (for beacons able to run complex firmware). In order to demonstrate the capabilities of the designed protocols, the most restrictive (LP4S-6) is tested after implementing it for a telemetry application in a beacon based on Eddystone (Google's open beacon format). Thus, the beacon specification is extended in order to increase its ability to manage unlimited sensors in a telemetry system without interfering in its normal operation with Eddystone frames. The performed experiments show the feasibility of the proposed solution and its superiority, in terms of latency and energy consumption, with respect to approaches based on Generic Attribute Profile (GATT) when multiple users connect to a mote or in scenarios where latency is not a restriction, but where low-energy consumption is essential.
Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to ...several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system "semi-automated." Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.
•EEG signal enhancement with novel modulation filtering technique.•Feature extraction with overlapping frequency bands.•Two-stage classification scheme for final four-class decision.•Outperformed the ...first-place winner of the BCI competition IV by 3.5 %.
Electroencephalogram (EEG) based brain-computer interfaces (BCI) monitor neural activity and translate these signals into actions and/or decisions, with the final goal of enabling users to interact with a computer using only their thoughts. To this end, users must produce specific neural activity patterns that are used by the system as control signals. A common task used to elicit such signals is motor imagery (MI), where specific patterns are elicited in the sensorimotor cortex during imagination of movements (e.g., of the hands, arms, feet or tongue). The processing pipeline typically used in EEG-BCIs consists of three stages: pre-processing, feature extraction, and classification. Here, we propose innovations in pre-processing and classification and quantify the gains achieved on 4-class MI-based BCI performance. More specifically, for the pre-processing stage, we propose the concept of spectro-temporal filtering as we show that MI-elicited neural patterns have varying amplitude modulation variations relative to artifacts. For the classification stage, in turn, a two-step classification method is proposed. First, LDA classifiers are used to discriminate between different pair-wise MI tasks. Next, a naive Bayes classifier is used to predict the final task performed by the user based on the weighted outputs of the LDA classifiers. Experimental results showed that the proposed system outperformed the first-place winner of the BCI competition IV by 3.5 %.
The drilling process of composite materials, such as the carbon fiber reinforced polymer (CFRP), constitutes a challenging task due to their inhomogeneous and anisotropic characteristics, besides the ...highly abrasive wear behaviour of their fibers. Accordingly, machining parameters should be carefully studied to optimize the process, leading to a better surface quality (avoiding defects in the CFRP) and to a lower wear behaviour of the cutting tool. This study proposed to test the drilling of a CFRP with a thermoplastic matrix using two different tool geometries (conventional and double-point angle drill) and varying two parameters, feed (f) and spindle speed (n), each one with two levels. It was concluded that the double-point angle drill with lower spindle speeds generates lower thrust force and torque values, as well as better hole quality. Higher spindle speeds combined with lower feeds result in fractured chips, in contrast with continuous chips for the other combinations.
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