Please login first

List of accepted submissions

Show results per page
Find papers
  • Open access
  • 42 Reads
Use of low-cost green adsorbent for the simultaneous removal of toxic industrial dyes from the wastewater: A sustainable solution to environmental remediation
, , ,

Over thousands of dyes are widely used in textile, chemical, paper, and pulp industries. These dyes are a major constituent of industrial effluent and are considered a major water pollutant. In the present study, coconut fiber has been explored for the simultaneous removal of toxic industrial dyes from their aqueous solutions. Coconut fiber is a commonly available low-cost bio-waste, which can be easily processed as a potential absorbent for the abatement of water pollution. In this conference we preset 3 different but related communications (part 1, 2, 3). Each communication will be presented in one different MOL2NET congress (NANOBIOMAT, CATCHTOHIT, and MODECO).

In the first communication (part 1), coconut fiber was thoroughly washed, dried, and crushed into the powder form as adsorbents and has been analyzed for the functional groups, morphology, and zero point charge using Fourier Transform Infrared spectroscopy, field emission scanning electron microscopy, and pH measurements, respectively.

In the second communication (part 2), It was found that the adsorbent exhibits high % removal capacity of 96.25±0.001, 81.24±0.015 and 89.81±0.004 for methylene blue, rhodamine B, and crystal violet dye, respectively in the individual dye removal studies. The simultaneous removal of the mixture of MB, RhB, and CV with the adsorbent showed an efficiency of 81±0.005% in 60 minutes. The adsorption studies were optimized for pH and dosage. This open a door to study the potential of this material for development of industrial product by an small-medium sized (SME), start-up, or chemical company.

In the third communication (part 3), further, the potential of coconut powder has been explored for the removal of a model agrochemical molecule (Chlorpyrifos, removal % 24.50±0.04) and toxic metal (Cr(VI), removal % 99.02±0.045%). The findings of the study demonstrate the potential of coconut fibre as an abundant, low-cost, multifunctional adsorbent for the treatment of wastewater for a range of industrial and agricultural contaminants.

  • Open access
  • 44 Reads
A Machine Learning Approach for The Brain Tumor Classification Using MR Imaging

This research presents a novel approach for denoising, extracting, and detecting tumors on MRI images. Images obtained from an MRI scanner are helpful to medical professionals in the research and diagnosis of brain disorders and malignancies. This activity aims to assist the radiologist and the physician in obtaining a second opinion on the diagnosis. The ambiguity that existed in the characteristics of magnetic resonance (MR) images has been resolved more straightforwardly. In the paper, the magnetic resonance imaging (MRI) image that was obtained from the machine is analyzed. The study takes advantage of the data collected in real-time. A variety of noise-reduction filters are used throughout the execution of the fundamental preprocessing steps. After the image has been de-noised, it is segmented, and then feature extraction is carried out. The wavelet transform is used in order to extract the features. The wavelet transform is superior to other techniques in terms of its applicability to the MRI image feature extraction process. The characteristics are then sent to the classifier, which conducts classification via Random Forest. A comparison is made between the categorization procedure and more traditional approaches.

  • Open access
  • 76 Reads
New magnetic zeolite-based nanocomposites for photocatalysis, part 2: photocatalytic treatment of wastewater.

Photocatalysis is considered to be the most efficient treatment as compared to the other methods and is suitable for highly cost-sensitive and energy-restrictive applications. In this research, first, we synthesized magnetic nanoparticles and reported their applications. We are going to publish it in to related communications (part 1 and part 2). In part 1, we synthesized magnetic nanoparticles of Fe3O4 and the versatile ZSM-5/Fe3O4 magnetic nanocomposite for the photocatalytic treatment of wastewater. This paper specifically reports the varying ratios of ZSM-5 and Fe3O4 in the nanocomposites that are 1:1, 1:2 and 1:0.5 and as the concentration of Fe3O4 varied, the properties of the nanocomposite changed as well. Further, these nanocomposites are characterized by X-ray diffraction (XRD), Fourier-transform infrared (FTIR), and several advanced spectroscopy techniques.

In addition to this, in a second communication (part 2), a comparison study is done between the three nanocomposites to study their magnetic behavior and photocatalytic efficiency to treat wastewater. Since these materials are magnetic in nature, therefore, after photocatalysis the material can be easily removed with the help of external magnets. Our approach provides an efficient and comparable synthesis process having photocatalytic applications in treating wastewater.

  • Open access
  • 25 Reads
Diurnal behaviour of the Aythya ferina in the Tonga Lake (Northeast of Algeria)

This study was conducted on Scaup
Pochard Aythya ferina for a study
Diurnal behavior and counting of
the species during the winter
(September 2015 to March 2016) in Lake Tonga
in the National Park of El kala.
The anatidae begins to arrive at the lake
at the earliest in November; the maximum number
230 individuals were observed in early January.
Showing the diurnal behavior of the species
that Tonga Lake was mainly used
during the day to rest
(sleeping 32%) followed by feeding 27%.

  • Open access
  • 27 Reads
Role of graphene oxide to overcome antimicrobial resistance

The prevalence of multi-drug resistant bacteria has increased significantly in recent years and becoming a significant threat to world health. This issue is greatly aggravated by the incorrect prescribing and excessive use of antibiotics. The rapid development of nanoscience and nanotechnology in the recent era has resulted in the emergence of various antimicrobial nanomaterials like Graphene, Graphene Oxide (GO), and Reduced Graphene Oxide (rGO). Due to their exceptional qualities, which include large surface areas and distinctive thermal, electrical, and physicomechanical properties, graphene oxide has lately been proven as an effective antimicrobials. Graphene oxide and reduced Graphene oxide unveiled toxicity to both Gram-positive and Gram-negative bacteria. Through the production of reactive oxygen species (ROS), physical destruction, chemical oxidation, and effects on the cell membrane and cell wall of microbes, GO causes microbial mortality and reduces microbial resistance. Three possible antimicrobial mechanisms of Graphene have been proposed: (i) inducing cellular damage with sharp edges of the nanomaterial; (ii) oxidative stress caused by the generation of superoxides with the treatment of Graphene nanomaterials; and (iii) wrapping the bacteria, and limiting physical movements and metabolism of bacteria. The combination of nanoparticles (NPs) and GO offers excellent potential in antimicrobial therapy and aids in overcoming antimicrobial resistance.

  • Open access
  • 32 Reads
Machine Learning Methods Using Texture Feature Selection in Diagnosis of Liver Cancer

The liver is essential to a wide variety of physiological and metabolic activities that take place in our bodies. A lack of liver function or liver dysfunction can lead to various health issues, the most serious of which is the illness. Early identification of liver illness can help shorten the treatment duration and minimize liver damage by decreasing the usage of drugs that are not essential. The application of machine learning techniques in medicine has shown promising results in illness diagnosis due to advances in technology. This research aimed to discover the essential characteristics of the data set, using textural feature selection methods so that they could be applied in the early diagnosis of liver disorders. This was done in order to diagnose liver failure disease. The model in machine learning methods has been improved, which has led to an improvement in the success rate of illness detection. The results obtained were compared with the findings of other research published in the literature that used the same data set. The diagnostic success rate for liver failure illness, as determined by applying machine learning algorithms known as Decision Trees (DT), was 94.67%. It is hoped that the developed models may assist medical professionals in the early detection of liver illness.

  • Open access
  • 22 Reads
Analysis and Identification of Nephrolithiasis from Ultrasound Images using Machine Learning Approach

Nephrolithiasis, commonly known as kidney stone disease, is a disorder in which the deposition of certain minerals causes a stone to develop in the urinary tract. Urolithiasis is another name for nephrolithiasis. Most of the time, kidney calculi form in the renal system and are eliminated through the urine system. Even a tiny stone can readily travel through the urine without any issues. More than 5 millimeters (0.2 inches) in diameter, fully grown calculi can clog the urinary tract, which can cause intense pain in the lumbar region or the stomach. Calculi can result in problems such as dysuria, vomiting, and hematuria. The recommended approach for automatically segmenting kidney stones is based on a four-stage framework, the first of which calls for pre-processing kidney pictures for better enhancement and is followed by the active contour method for automatically segmenting kidney stones. In the future, an assessment will be performed depending on the size and kind of stone.

  • Open access
  • 34 Reads
A network pharmacology-based analysis of the main pharmacological pathways of Cardiospermum Halicacabum acting on Human Breast Cancer with computational and experimental validation

The discovery of medicines based on natural products is thriving in the world. India is rich in medicinal plants, which are known to treat various diseases. One among Cardiospermum halicacabum or balloon wine is a climbing plant. It belongs to the family Sapindaceae and it’s known for various pharmacological activities. In total 170 active ingredients were curated from the Collective Molecular Activities of Useful Plants(CMAUP) platform and literature research is funneled for >50% drug-likeness via BIOVIA Discovery studio small molecule tool. The OMIM, Therapeutic Targets database, DrugBank and GeneCard were used to establish a database of breast cancer targets. The interactive compound-gene target was created with Cytoscape software (Version 3.6.1). Further, the protein-protein interaction network was mapped with the STRING database, and the related protein interaction relationship was analyzed. KEGG enrichment and Gene Ontology functional analysis for main drug targets pathways are involved Estrogen signaling pathway, Ras signaling pathway, and Proteoglycans in cancer. Finally, molecular docking of the active ingredients with the primary drug target was carried out. Also, the cell line study was carried out in human breast cancer cell lines MCF7 to investigate the level of cytotoxicity.