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Microplastics

Microplastics are tiny plastic particles less than 5 millimeters in size, derived from the breakdown of larger plastic items.
These microscopic pollutants have become a growing environmental concern, as they can be ingested by a wide range of organisms, posing potential risks to ecosysytems and human health.
Microplastics are found in air, water, soil, and even food, presenting challenges for researchers studying their prevalence, distribution, and impacts.
Effective research methodologies are crucial to understand and mitigate the spread of these pervasive contaminants.

Most cited protocols related to «Microplastics»

1
Quantifying Global Ocean Microplastics
In this study we determined abundances and mass of microplastics starting at the lowest size of 0.33 mm, which is a commonly used lower limit for pelagic microplastics [31] (link). The prefixes micro, meso and macro in relation to plastic pollution are poorly defined. Generally accepted microplastic boundaries are based on typical neuston net mesh size (0.33 mm) and an upper boundary of approximately 5.0 mm [31] (link). We have used 4.75 mm as our upper boundary for microplastic because this is a size for standard sieves used for sample analysis in most of the expeditions contributing data to this manuscript. Mesoplastic has a lower limit of 4.75 mm, and no defined upper limit. In this current study we set the upper boundary of mesoplastic at 200 mm, which represents a typical plastic water bottle, chosen because of its ubiquity in the ocean. Macroplastic has no established lower boundary, though we set it at 200 mm, while the upper boundary is unlimited. There is a clear need for consistent measures in the field [31] (link), and herein we followed a practical approach using commonly employed boundaries and logistic considerations (net and sieve sizes) in order to integrate an extensive dataset that covers the entire global ocean, including areas that have never been sampled before.
Of the 1571 field locations that contributed count data (Fig. 1), a total of 1333 stations also had weight data (Fig. S4). All these data were used to calibrate the numerical model prediction of plastic count and weight density [28] . For the comparison, we fit the model results to measured data by a linear system of equations of the form:
where yi is the logarithm of a measured value of plastic count density (pieces km−2) or weight density (g km−2) for each of the N number of samples. K is the number of model output cases with sij a dimensionless model solution at the location of sample yi. βk and εN are the computed weighting coefficients and the error terms for a particular dimensionless model solution sij. This method can be used to fit an arbitrary number of model output cases to any number of measured data points producing a weighting coefficient and error term for each case.
In the model we used a set of three model results (K = 3), corresponding to different input scenarios [28] : urban development within watersheds, coastal population and shipping traffic. Values of β and ε are determined for both the concentration distribution (pieces km−2) and the weight distribution (g km−2) of each of the four size classes based on the linear system of equations. To compare the model results directly to the measured data, the weighting coefficient βk computed above is used to scale the model output for each of the output scenarios.
Eriksen M., Lebreton L.C., Carson H.S., Thiel M., Moore C.J., Borerro J.C., Galgani F., Ryan P.G, & Reisser J. (2014). Plastic Pollution in the World's Oceans: More than 5 Trillion Plastic Pieces Weighing over 250,000 Tons Afloat at Sea. PLoS ONE, 9(12), e111913.
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Publication 2014
Mesoplastics Microplastics Urban Development
2
Collaborative Microplastic Research Methods
As a scientific community, we recognize that the need for reporting guidelines for microplastic methods is best addressed through a collaborative open science framework. With this goal in mind, the lead author sent out the following request on Twitter, and tagged several scientists in the microplastic community with a link to a collaborative document:
The collaborative document was hosted open access on Google Drive and researchers were invited to provide input on the reporting guidelines for microplastic research methods. Over the subsequent week, 15 contributors edited the shared document directly. After one week, all initial contributors were invited to be coauthors, and additional coauthors were invited by word of mouth throughout the process using an open-door policy. Overall, there were 23 authors on this project and 26 other people acknowledged for their assistance. In a meeting of coauthors, the threshold for co-authorship was set at one full day of effort (self-defined and self-reported), while the threshold for acknowledgement was to review the document at least once. Authors contributed to this publication and the reporting guideline documents. The first author, Win Cowger, led the collaboration and the author order after the first author was randomized by agreement of all coauthors.
The reporting guidelines were identified by referencing standard operating procedures used by various authors and other peer-reviewed publications. All authors agreed not to use language that would imply an intent to standardize methodology or recommend specific methods over others; this was beyond the scope of the work. The task of the authors in developing the reporting guidelines was to outline what should be reported about a method when the method was used to make the method reproducible and comparable. To determine which guidelines were essential to add to the documents, each author was asked to fill out a Google Form survey where they designated each reporting category as required or not. The final reporting guidelines were formed by keeping only the guidelines that 51% or more of the authors agreed upon. During the review process, we received requests by reviewers to add additional reporting requirements. Where they were not already accounted for, we added them to the reporting guidelines and indicated those additions using an asterisk throughout the produced documents. The final reporting guidelines were packaged into three documents which have the same information summarized with specific user groups in mind: (i) thorough, a Detailed Document, (ii) quick and simple, a Checklist (Table I), and (iii) interactive, an online Mind Map (Figure 2).
The reporting guidelines were sent out to other colleagues in the field for an endorsement and critique designated as signatories in the acknowledgments. After the first week, we received 19 endorsements. The manuscript and supporting information were also subject to internal review at the National Institute of Standards and Technology (NIST) and single blind peer review from Applied Spectroscopy. In these ways, we attempted to receive as much feedback as we could to develop reporting guidelines that reflect the diverse group of experts and the broad scope of methods in microplastic research. This framework represents an example of a way that scientists in any field can develop robust collaborations by sharing ideas and learning from one another while developing useful reference documents, even if they have not met before.
Cowger W., Booth A.M., Hamilton B.M., Thaysen C., Primpke S., Munno K., Lusher A.L., Dehaut A., Vaz V.P., Liboiron M., Devriese L.I., Hermabessiere L., Rochman C., Athey S.N., Lynch J.M., De Frond H., Gray A., Jones O.A., Brander S., Steele C., Moore S., Sanchez A, & Nel H. (2020). Reporting Guidelines to Increase the Reproducibility and Comparability of Research on Microplastics. Applied spectroscopy, 74(9), 1066-1077.
Publication 2020
Microplastics Oral Cavity Peer Review Spectrum Analysis Visually Impaired Persons
3
Efficient Airborne Microplastics Quantification
Microplastics and microfibers, deriving from modern products and activities, are released to the atmospheric compartment [1 (link)]. Currently, only 4 works have been published sampling airborne microplastics and microfibers by active sampling [[1] (link), [2] , [3] (link), [4] (link)]. These works rely on visual identification and chemical characterization by micro-Fourier transform infrared spectroscopy (micro-FTIR). However, chemical identification by spectroscopic methods, such as micro-Raman spectroscopy or micro-FTIR, is time consuming and not always available [5 (link)]. Visual characterization of microfibers as synthetic or natural is difficult, especially when lacking concrete parameters. Although identification of microplastics can be aided using staining dyes, namely Nile Red, and the use of automated software, such as MP-VAT, individualized fibers do not present fluorescence following current staining protocols [6 (link)]. Identification is even more complex in the presence of organic and mineral contaminants.
Sampling of passive deposition of atmospheric particles and collection of street dust rich in organic matter led to the development of a method including organic matter removal and density separation [7 (link)]. However, the original protocol was complex as it required several passages and drying steps that consumed a lot of time and increased the possibility of contamination. Instead, this protocol was simplified and the number of steps reduced. Shortly, air sampling is conducted over a relevant period of time, in this case 48 h (phase 1), followed by sample transfer by washing of the quartz fiber filters to glass beakers where H2O2 is added to achieve a concentration of 15 % and left to react for 8 days to allow removal of natural organic matter (phase 2). This solution is then filtered and the sample transferred again to allow density separation through the use of 1.6 g cm−3 NaI, removing higher density particles such as inorganic matter (phase 3), finally followed by filtration, drying and manual counting under a stereomicroscope following a comprehensive diagram that aids the visual classification of fibers into natural or synthetic (phase 4). The suitability of this protocol was then tested using spiked samples, with known numbers, and with indoor and outdoor particulate matter samples. The objective of this protocol was the removal of organic matter and dark particles coating the filter, likely comprised of carbonaceous mater, that hindered quantification and characterization of microfibers and suspected microplastics.
Prata J.C., Castro J.L., da Costa J.P., Duarte A.C., Cerqueira M, & Rocha-Santos T. (2019). An easy method for processing and identification of natural and synthetic microfibers and microplastics in indoor and outdoor air. MethodsX, 7, 1-9.
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Publication 2019
Acquired Immunodeficiency Syndrome Dyes Fibrosis Filtration Fluorescence Microplastics Minerals Mothers Peroxide, Hydrogen Quartz Spectroscopy, Fourier Transform Infrared Spectrum Analysis Spectrum Analysis, Raman
4
Microplastic Extraction and Identification in Marine Sediments
All sample processing (extraction and picking) was conducted in a clean laboratory, where extreme care was taken to avoid any contamination. Checks for contamination during processing were made by exposing damp filter paper to the air in the laboratory, whenever samples were open to the laboratory environment. As an additional precaution, those handling the samples wore only natural fibre clothing, and were protected with 100% cotton laboratory coats and headwear, and latex gloves, for all laboratory processing and during the JC76T research cruise.
Microplastics were extracted from the sediment using two methodologies. For samples 1, 2 and 4–9, extraction was done by Plymouth University (PU) using a concentrated NaCl solution and filtering with three sequential extractions [5 (link)]. The PU method employs supernatant filtering through a Whatman GF/A filter. For samples 3 and 10–12, particle extraction was conducted at the Natural History Museum (NHM) using an adapted Ludox-TM 40 extraction method [15 ] employing eight centrifuge cycles and a 32 μm sieve to separate the microplastics from the sand grains. The substances chosen to isolate the fibres (NaCl or Ludox-TM 40) had similar specific gravities, namely 1.2 and 1.16, for PU and NHM, respectively. We are therefore confident that the same fractions and types of microplastics were isolated.
Using an entomological pin, microplastic fibres from coral specimens 13–16 were removed under a binocular microscope and placed into clean vials containing Millipore water. The fibres were not extracted quantitatively. The corals were of different sizes, and not all fibres present on the corals were removed, therefore just the presence or absence of microplastic accumulation on living coral was recorded.
All sediment samples were examined under a binocular microscope, and any objects that were of unnatural appearance based on shape and colour (potential microplastics) were transferred to sealed containers and subsequently identified [5 (link)] by spectrometry.
Woodall L.C., Sanchez-Vidal A., Canals M., Paterson G.L., Coppock R., Sleight V., Calafat A., Rogers A.D., Narayanaswamy B.E, & Thompson R.C. (2014). The deep sea is a major sink for microplastic debris. Royal Society Open Science, 1(4), 140317.
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Publication 2014
Alarmins Cereals Coral Fibrosis Gossypium Latex Microplastics Microscopy Self Confidence Sodium Chloride Spectrometry
5
Microplastic Quantification in Copepods
The optimized enzymatic digestion protocol was further considered as a technique for detecting microplastics ingested by marine organisms. Specimens of the marine copepod Temora longicornis were isolated from a zooplankton trawl, and then three individuals (n = 5) placed in a Petri-dish containing 20 mL of filtered seawater containing fluorescent polystyrene beads (100 microplastics mL−1) overnight at ambient sea surface temperature. Post-exposure, specimens were retained on a mesh-filter, preserved using 4% formalin and rinsed thoroughly with Milli-Q. Copepods were visualized under a microscope (fitted with fluorescence) to quantify the number of specimens that had ingested the polystyrene beads and it was confirmed that no external microplastics were present. T. longicornis were enzymatically digested per the standardized protocol, using smaller volumes of homogenizing solution, Proteinase-K and sodium perchlorate owing to the smaller mass of biological material being digested. Digested extract was filtered onto a 0.2 μm glass fibre filter (GF/F) and residue visualized under a microscope to enumerate and photograph the microplastics that had been previously internalized by the copepods.
Cole M., Webb H., Lindeque P.K., Fileman E.S., Halsband C, & Galloway T.S. (2014). Isolation of microplastics in biota-rich seawater samples and marine organisms. Scientific Reports, 4, 4528.
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Publication 2014
Biopharmaceuticals Copepoda Digestion Endopeptidase K Enzymes Fluorescence Formalin Hyperostosis, Diffuse Idiopathic Skeletal Marine Organisms Marines Microplastics Microscopy Polystyrenes sodium perchlorate Zooplankton

Most recents protocols related to «Microplastics»

1
Microplastic Extraction from Avian Gastrointestinal Tracts
The contents of all upper GITs and
separately a subsample of 20 intestines (10 from fledglings and 10
from older birds) were transferred into glass beakers and a 10% solution
of potassium hydroxide (KOH) was added to digest soft organic tissue.39 (link),40 (link) KOH was chosen for its efficiency to digest organic matter while
preserving the mass, morphology, and the chemical integrity of many
plastic polymers, even when heated up to 40 °C and shaken to
200 or 300 rpm, as evidenced by several studies.41 (link)−43 (link) In this study,
the beakers were kept on a low-profile shaker (IKA HS 501 digital,
Staufen, Germany) at 100 rpm for at least 2 days (max 3 days) to enhance
the digestion process, at room temperature. Thereafter, the mixtures
of KOH solution and GIT content were filtered through a stainless-steel
sieve (mesh size: 20 μm) and then vacuum-filtered through a
filtering membrane (cellulose acetate filter, pore size 5 μm,
Sartorius Stedim Biotech, Göttingen). The extracted particles
were visually sorted, and only the plastic-like particles were further
analyzed by spectroscopy. Particles from natural origin, e.g., squid
beaks, exoskeleton of crustaceans, and other prey items that remained
after KOH digestion as well as stones, etc., were not analyzed by
FTIR spectroscopy, but thoroughly checked for hidden plastic particles.
Filter papers were kept and can be used in future microplastic studies.
Tulatz F., Gabrielsen G.W., Bourgeon S., Herzke D., Krapp R., Langset M., Neumann S., Lippold A, & Collard F. (2023). Implications of Regurgitative Feeding on Plastic Loads in Northern Fulmars (Fulmarus glacialis): A Study from Svalbard. Environmental Science & Technology, 57(9), 3562-3570.
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Publication 2023
acetylcellulose Aves Calculi Crustacea Digestion Digestive System Processes Intestines Microplastics Natural Childbirth Polymers potassium hydroxide Spectrum Analysis Tissue, Membrane Tissues Vacuum
2
Comprehensive Mapping of Ocean Plastic Pollution
We compiled data on OSL plastic abundance and distribution from published literature and unpublished sources, totaling 11,777 stations used in this trend analysis (Fig 1; S1 Dataset and on GitHub https://github.com/wincowgerDEV/ocean_plastic_modeling). Data were aggregated primarily from peer-reviewed manuscripts and previously unpublished data from 5 Gyres Institute expeditions. The data were collected with multiple methods of sea-surface sampling: manta trawl [8 (link), 16 (link)], AVANI trawl [17 (link)], or a rectangular neuston net [7 (link), 18 (link)]. We filtered the data to include samples with a lower mesh size range between 53μm and 505μm. Of the 11,777 samples in this study, 0.2% of samples were collected with a 53μm mesh (n = 27), 2.5% used 200μm (n = 303), 2.5% used 500–505μm (n = 293), and 94.7% used a 320–350μm (n = 11,154). The upper range of net openings was between 0.5m and 1m (S1 Table).
Although there was some variability in the methodology for each dataset, the methods typically were as follows: with the aid of a dissecting microscope, microplastics were manually separated from natural debris after being sorted through sieves [19 (link), 20 (link)], then counted individually, before all microplastics from each size category were weighed together. To compute count data (in pieces km-2) we divided the total count of plastics collected by the surface area of water that the trawl went through. If this metric was not provided, we computed it using trawl dimensions and distance sampled as reported in the corresponding literature. If only the month and year were provided for the date, we used the 15th of the month specified as the day. Mass was estimated with a common conversion rate reported in the literature (1.36 x 10−2 g particle-1) by Morét-Ferguson et al. [21 (link)].
Eriksen M., Cowger W., Erdle L.M., Coffin S., Villarrubia-Gómez P., Moore C.J., Carpenter E.J., Day R.H., Thiel M, & Wilcox C. (2023). A growing plastic smog, now estimated to be over 170 trillion plastic particles afloat in the world’s oceans—Urgent solutions required. PLOS ONE, 18(3), e0281596.
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Publication 2023
Microplastics Microscopy
3
Modeling Temporal Trends of Oceanic Plastic
We modeled OSL temporal trends in plastic abundance and distribution with the above datasets corrected for wind and basin over time. Although there are many examples of global and regional plastic abundance and distribution estimates at a single point in time, temporal trends proved more challenging to produce.
Here, we used a model that removed the effects of site-bias and non-detects from the observed concentrations. We corrected for non-detects by treating zeros in the data as censored observations, in which the actual observation is below the detection level (1/volume of water sampled), instead of being considered a true zero value. We estimated the likely values of the zero records with the cenros function in the NADA package [25 ]; this function is a regression on ordered statistics that fits a model to the observed data and their quantiles to extrapolate the left-censored values. After correcting for non-detects with the cenros function, we estimated the actual values of the zeros to include in the model (S1 Fig). Plotting log-transformed observed concentration and expected concentration from an oceanographic transport model revealed an ocean-basin and concentration bias (quantile–quantile (qq) plot) (S2 Fig). To correct the collinear effect of expected concentration, we first fit a generalized additive model (GAM) model (gamma distribution) between log-transformed observed concentration and expected concentration from the van Sebille model [9 (link)]. We fit the model (concentration = expected concentration + basin) to the observed concentrations to get the residuals. Here the predicted density has no temporal component, only a spatial one, so the residuals from this relationship give a measure of how much an observation diverges from the relative concentration across space that we would expect. We then explored support for a time trend by modeling the residuals from the model above using a smooth function on time in a generalized additive model (S3 Fig). This approach is similar to adding all variables to a single model but allows us to interpret more precisely the effect of time by itself without influence from the other variables due to collinearity. We then estimated the average concentration change through time by multiplying the mean concentration observed by the reverse log-transformed residual fit. This approach allowed us to predict global plastic quantity weighted by observed mean plastic concentrations without influence from spatial effects.
Modeled results of floating microplastic item count (trillion of particles) and mass (millions of tonnes) globally for each year were calculated by averaging daily model results. Daily model results for particle counts were calculated by multiplying model results (particles km-2) x 361,900,000 km2 (ocean surface-area). Daily model results for mass were calculated by multiplying 1.36 x 10−2 g (average particle mass and weight from [21 (link)] x the number of particles.
Eriksen M., Cowger W., Erdle L.M., Coffin S., Villarrubia-Gómez P., Moore C.J., Carpenter E.J., Day R.H., Thiel M, & Wilcox C. (2023). A growing plastic smog, now estimated to be over 170 trillion plastic particles afloat in the world’s oceans—Urgent solutions required. PLOS ONE, 18(3), e0281596.
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Publication 2023
Gamma Rays Microplastics Wind
4
Microplastic Characterization and Quantification
The physical analysis of all microplastic samples was performed in Portugal. Samples were first processed to remove organic material. A chemical digestion using potassium hydroxide (KOH) was used to extract the microplastics from biological matrices, according to (Pagter et al., 2018 (link); Supplementary information, protocol, SI-P1) and analyzed in entirety except for the Yarkon samples from summer which due to the high amount of microplastics present were subsampled six times using a Folsom Plankton Splitter (Wildco® 1831-F10). All plastic particles were observed using a stereoscopic microscope (Leica DFC480) equipped with a camera (Leica Flexacam). The particles were then counted, measured, and separated according to their typology (fragment, filament, pellet, film, microbead, and sponge/foam) and color (black, blue, white, transparent, red, green, multicolor, and others). Examples for typologies and colors are presented in Supplementary Figure S1.
ImageJ software (v1.48, National Institutes of Health) and Leica LAS-X measurement software (Leica Application Suite X-LAS X) were used to measure the microplastics. The plastic particles that were removed for DNA extraction were also included in the total plastic particles. A blank was used (filter exposed to air) throughout the procedure and microplastics found in the blanks that were similar to those found in the samples were removed from the analyzes.
IBM® SPSS® Statistics 25.0 software was used for the statistical analysis of the microplastics. Kolmogorov–Smirnov and Shapiro–Wilk tests were used to test the normality of variables. Nonparametric tests were selected since the data did not follow a normal distribution. To compare the abundance of plastics, between seasons (summer and winter) and locations (Yarkon and Sado) the non-parametric Kruskal-Wallis H (KW) test was used. Mann Whitney U-test was used to perform Pairwise comparisons between two independent groups. A significance level of 0.05 was considered for all statistical analyzes.
Fourier transformed infrared spectroscopy (FTIR Spectrum Two™ ATR Universal spectrometer, Perkin Elmer, United States) in attenuated total reflectance (ATR) was used to collect spectra in the absorbance mode in the region of 4,000 cm-1 to 450 cm-1 with a data range of cm-1. The resolution was fixed at 4 cm-1 (4 scans). Plastic samples were pressed against the diamond crystal with a force of 80–120 N. Spectra were obtained by absorbance (A) and analyzed using PerkinElmer Spectrum IR software (version 10.7.2). The results were compared with several reference spectra from different Perkin Elmer databases and pre-existing spectral libraries (Hummel, 2002 ; Coates, 2006 ; Jung et al., 2018 (link)) to identify the chemical nature of the plastics. The acceptance level was established at >90% similarity match, between the sample spectrum and the reference spectra database. Statistical analyzes were performed using IBM® SPSS® Statistics 25.0 software (Chicago, IL, United States).
Marsay K.S., Ambrosino A.C., Koucherov Y., Davidov K., Figueiredo N., Yakovenko I., Itzahri S., Martins M., Sobral P, & Oren M. (2023). The geographical and seasonal effects on the composition of marine microplastic and its microbial communities: The case study of Israel and Portugal. Frontiers in Microbiology, 14, 1089926.
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Publication 2023
Biopharmaceuticals Cytoskeletal Filaments Diamond Digestion Microplastics Microscopy Microspheres Physical Examination Plankton Porifera potassium hydroxide Radionuclide Imaging Spectroscopy, Fourier Transform Infrared Spectrum Analysis
5
Microplastic and Marine Microbial Diversity
The microplastic and seawater were sampled from two sites representing two separate marine environments–one in the Atlantic Ocean (Portugal site) and one in the eastern Mediterranean Sea (Israel site). The exact locations were 0.5–1.2 km from the shoreline of Yarkon estuary, Tel Aviv, Israel (32°06′35.7”N, 34°46′18.7″E) and of the Sado estuary, Setubal, Portugal (38°28′22.2”N 8°58′28.3”W; Figure 1). The samples were collected from the sea surface in both locations in the winter (February/March) and in the summer (July) of 2021 using a manta net (Hydro-Bios, Microplastic net, 438,217), with a mesh size of 300 μm and mouth opening of 30 × 15 cm. The manta net was towed from a research vessel at a speed of 2–3 knots for 20–30 min in parallel to the coastline (3 times on the same line). The water volume filtered through the net during each tow was calculated from flow meter counts (Hydro-Bios-438,110) using the expression number of revolutions x 0.3 x net opening area (m2)/1000 = volume m3 (Supplementary Table S1). Physico-chemical parameters (water temperature, salinity, and depth) were registered (Table 1) with a portable multiparameter probe (HI98194 Multiparameter, HANNA Instruments).
Microplastic particles recovered from the collecting cod-end of the net were rinsed in filtered artificial seawater. Seawater from the site was sampled using sterile sampling bottles. For DNA metabarcoding analysis of the plastic microbiome, 10 microplastic particles were randomly picked up from each sample and the rest was kept in sea water for the characterization of the microplastic. For DNA metabarcoding analysis of the water microbiome, 0.5 L of the sampled water was filtered onto a 0.22 μm polyethersulfone membrane (Millipore) using a 20 l/min pump (MRC).
Marsay K.S., Ambrosino A.C., Koucherov Y., Davidov K., Figueiredo N., Yakovenko I., Itzahri S., Martins M., Sobral P, & Oren M. (2023). The geographical and seasonal effects on the composition of marine microplastic and its microbial communities: The case study of Israel and Portugal. Frontiers in Microbiology, 14, 1089926.
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Publication 2023
Blood Vessel Estuaries Flowmeters Marines Microbiome Microplastics Oral Cavity polyether sulfone Salinity Sterility, Reproductive Tissue, Membrane

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More about "Microplastics"

Microplastics are a growing environmental concern, referring to tiny plastic particles less than 5 millimeters in size.
These microscopic pollutants, also known as microdebris or microplastic particles, are derived from the breakdown of larger plastic items.
Microplastics have become ubiquitous, found in air, water, soil, and even the food we consume, posing potential risks to ecosystems and human health.
Effective research methodologies are crucial to understanding and mitigating the spread of these pervasive contaminants.
Analytical techniques like Fourier-transform infrared spectroscopy (FTIR) using instruments like the Nicolet iN10 or Spectrum Two, and Raman spectroscopy with the LabSpec 6 system, can be used to identify and quantify microplastics.
Synthetic microplastic beads and Zn-TPP can serve as reference materials for these analyses.
Researchers studying the prevalence, distribution, and impacts of microplastics face unique challenges.
Advances in AI-driven protocol comparisons, such as those offered by PubCompare.ai, can help optimize research methodologies and enhance reproducibility.
By leveraging artificial intelligence to identify the most effective methods and products, researchers can streamline their microplastics research and make more informed decisions.
Whether you're analyzing microplastics in environmental samples or exploring their effects on ecosystems and human health, staying up-to-date with the latest research and tools is crucial.
By combining the insights from MeSH term descriptions, metadescriptions, and relevant software and instrumentation, you can navigate the complexities of microplastics research and contribute to the growing body of knowledge in this important field.