Adsorption Science & Technology
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 Journal metrics
Acceptance rate22%
Submission to final decision34 days
Acceptance to publication22 days
CiteScore6.000
Journal Citation Indicator0.590
Impact Factor4.232

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 Journal profile

Adsorption Science & Technology publishes original research and review articles on the topic of adsorption.

 Editor spotlight

Chief Editor, Dr Ashleigh Fletcher, is based at the University of Strathclyde, UK. Her current research focuses on adsorption processes.

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We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

Citric Acid Promotes the Mobilization of Phosphorus under the Lower Concentration of Low Molecular Weight Organic Acids in Acidic Forest Soil

Low molecular weight organic acids (LMWOAs) secreted by plant roots enhanced the release of inorganic P (Pi) and organic P (Po) into the soil solution and thereby increased plant-available Pi in soils. Not the effect of LMWOAs on inducing organic P (Po) released into soil solution through soil microorganisms at different temperatures was poorly understood, but the transform mechanism for P fraction was also not well explained. This study used three experiments to determine the release of P and the transformation mechanism of P fractions induced by oxalic acid, citric acid, and malic acid in acidic forest soils. The results showed that LMWOAs, as carbon sources for microorganisms, mobilize Po more effectively than glucose. Inorganic P and organic P were released by LMWOAs followed by if the substrates of P and LMWOAs were enough. There may be a critical threshold for the concentration of citric acid and oxalic acid between 10 mM and 25 mM to require for the solution of adsorbed and precipitated P, respectively. In all, LMWOAs increased the concentration of labile P by decreasing the concentration of stable P. The results indicated that LMWOAs can significantly promote P availability in acidic forests soils, and the effect of microorganisms on soil available P was more inclined to use LMWOAs than glucose.

Research Article

Gaussian Process Regression and Machine Learning Methods for Carbon-Based Material Adsorption

Antibiotics have received a lot of attention as promising contaminants because of their ecotoxicological and long-term chemical stability in the atmosphere. Antibiotic adsorption on carbon-based materials (CBMs) such as charcoal and activated carbon has been identified as mainly effective for treating the wastewater strategies. Machine learning (ML) approaches were used to create generalized computation methods for tetracycline (TC) and sulfamethoxazole (SMX) adsorption in CBMs in this investigation. In the existing system, random forest and ANN methods were used for TC and SMX for predicting the quantities of antibiotics in the CBMs. For reducing the antibiotics from the industrial wastewater, the broadcast efforts of the experiments are a little complicated. In the proposed method, Gaussian process regression (GPR), active learning (AL), and ANN are used for predicting the antibiotic levels in the industrial wastewater. Below a variety of environmental parameters (e.g., warmth, solution pH) and adsorbent varieties, the created Ml algorithms outperformed classic isotherm models in conditions of generalisation. To evaluate TC and SMX adsorption on CBMs, we used comparative significance investigation and partial trust plots based on ML models. The proposed GPR reduces the antibiotics in wastewater; minimal experimental screening and the comparative significance and partial trust plot help in the treatment of wastewater.

Research Article

Semiconductor Polymer Carbon Composite Coated Fabric for Warm Beds in Hospital

Patients suffering from diseases that occur due to spreading of virus like fever and cold will have decrease in body temperature. They feel cold in the normal body and room temperature conditions. For the comfort of these patients, an electric under blanket is designed which warms up the patient to maintain the normal body temperature. The heated under body supports include a heater assembly and a layer of compressible support material. The heater assembly includes a flexible heating element, multiplex polyester, and a temperature sensor. The flexible heater element may include a fabric, which coated with a conductive or semiconductive polymer. The heated under body support may also include a water resistant shell, whereas it may encase the heater assembly and the compressible support material. The material used for outer shell and inner heating element has simulated in COMSOL tool for analyzing the heat transfer between them. The proto type model has simulated in PROTEUS software, which includes Arduino UNO and thermistor. This analysis will give the result whether the material can be used as the under garment for warming the patient.

Research Article

The Synthesis of Magnetic Nitrogen-Doped Graphene Oxide Nanocomposite for the Removal of Reactive Orange 12 Dye

Herein, we report the nanofabrication of magnetic calcium ferrite (CaFe2O4) with nitrogen-doped graphene oxide (N-GO) via facile ultrasonication method to produce CaFe2O4/N-GO nanocomposite for the potential removal of reactive orange 12 (RO12) dye from aqueous solution. The successful construction of the nanocomposite was confirmed using different characterization techniques including scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier transform-infrared spectroscopy (FT-IR), and X-ray diffraction (XRD). The magnetic properties were studied using vibrating sample magnetometer (VSM) indicating ferromagnetic behavior of the synthesized materials that facilitate their separation using an external magnetic field after adsorption treatment. The addition of N-GO to CaFe2O4 nanoparticles enhanced the BET surface area from 24 to 52.93 m2/g as resulted from the N2 adsorption-desorption isotherm. The adsorption of the synthesized nanomaterials is controlled by several parameters (initial concentration of dye, contact time, adsorbent dosage, and pH), and the RO12 dye removal on the surface of CaFe2O4 nanoparticles and CaFe2O4/N-GO nanocomposite was reached through the chemisorption process as indicated from the kinetic study. The adsorption isotherm study indicated that the adsorption process of RO12 dye was best described through the Langmuir isotherm approving the monolayer adsorption. According to the Langmuir model, the maximum adsorption capacity for RO12 was 250 and 333.33 mg/g for CaFe2O4 nanoparticles and CaFe2O4/N-GO nanocomposite, respectively. The adsorption capacity offered by CaFe2O4/N-GO nanocomposite was higher than reported in the literature for adsorbent materials. Additionally, the regeneration study indicated that CaFe2O4/N-GO nanocomposite is reusable and cost-effective adsorbent. Therefore, the nanofabricated CaFe2O4/N-GO hybrid material is a promising adsorbent for water treatment.

Research Article

A Universal Synergistic Rule of Cd(II)-Sb(V) Coadsorption to Typical Soil Mineral and Organic Components

Heavy metals and metalloids are common cooccurrence in contaminated soils, making their behaviors more complex than their individual presences. Adsorption to soil minerals and organic components determines the solubility and mobility of heavy metals. However, little information is available regarding coadsorbing metals (e.g., Cd) and metalloids (e.g., Sb) to soil components, and whether there is a universal coadsorption rule needs to be illuminated. This study investigated the coadsorption behaviors of Cd(II) and Sb(V) to goethite, kaolinite, and bacteria (Bacillus cereus) at both acidic (pH 4.5) and alkaline pH (pH 8.5). Equilibrium adsorption experiments, coupled with scanning electron microscopy- (SEM-) energy-dispersive X-ray spectrum (EDS) and X-ray photoelectron spectroscopy (XPS), were applied to determine the batch adsorption phenomena and possible mechanisms. Batch results showed that Cd(II) adsorption was greater at pH 8.5 whereas Sb(V) adsorption was greater at pH 4.5. The presence of Cd or Sb promoted each other’s adsorption to goethite, kaolinite, and bacteria, but slight differences were that Sb(V) preferred to enhance Cd(II) adsorption at acidic pH, whereas Cd(II) was more able to increase Sb(V) adsorption at alkaline pH. SEM-EDS analyses further showed that the distribution of Cd and Sb was colocalized. The surface FeOH, AlOH, and COOH groups participated in the binding of Cd(II) and Sb(V), probably through the formation of inner-sphere complexes. Two possible ternary complexes, i.e., sorbent-Cd2+-Sb(OH)6 and sorbent-Sb(OH)6-Cd2+, were possibly formed. Both the charge effect and the formation of ternary complexes were responsible for the collaborative coadsorbing of Cd-Sb. The universal synergistic rule obtained suggests that current models for predicting Cd(II) or Sb(V) sequestration based on single systems may underestimate their solid-to-liquid distribution ratio in a coexistence situation. The results obtained have important implications for understanding the chemical behavior of Sb and Cd in contaminated soils.

Research Article

Intelligent CO2 Monitoring for Diagnosis of Sleep Apnea Using Neural Cryptography Techniques

In biomass wastage, carbon is one of the adsorbent materials. Biomass wastage contains complex materials, and pressure, various temperatures, and presence of various chemical components which are subjected to the adsorption of carbon are a tedious task, and it is used in the sustainable waste management system. While screening the biomass wastage management system, prediction of activated carbon’s quality and understanding of the mechanism of adsorption of are a complicated task. Many research works have been developed; the main issues are inaccurate and inefficient prediction of carbon available in the various feedstock of biomass wastage. To overcome these issues, this paper proposed gene expression programming (GEP) with -nearest neighbour (GEP-KNN). The key advantage of the proposed work shows excellent performance in the prediction of adsorbing carbon and accuracy. The accuracy of the GEP-KNN algorithm with different values produced the highest accuracy at and of 95.12% and 95.67%; the lowest accuracy is of 65.34%.

Adsorption Science & Technology
Publishing Collaboration
More info
Sage logo
 Journal metrics
Acceptance rate22%
Submission to final decision34 days
Acceptance to publication22 days
CiteScore6.000
Journal Citation Indicator0.590
Impact Factor4.232
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.