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Magnesium ferrite (MgFe2O4) and polyethylene glycol (PEG) are materials known for their versatility in various applications. This study presents a comprehensive comparative analysis of the electrical conductivity and dielectric relaxation of nanostructured MgFe2O4 and its composites with PEG. Through experimentation, it was observed that incorporating PEG into MgFe2O4 did not lead to a high relative observed decrease or increase in electrical conductivity at room temperature. The study revealed that the composites maintained stable electrical behavior at room temperature, with a dielectric constant value of around 9 and a loss tangent value of around 0.1 at high frequency (around 7 MHz). The electron-hole hopping mechanism was identified as the underlying cause for the strong dielectric dispersion with frequency. The low dielectric loss and conductivity of the MgFe2O4 and PEG/ferrite composites make them promising candidates for high-frequency switching applications and microelectronic devices, particularly in scenarios where negligible eddy currents are essential. Additionally, complex impedance data analysis demonstrated that the capacitive and resistive properties of the composites are primarily attributed to grain boundary processes. This study provides a comprehensive analysis of the electrical and dielectric properties of MgFe2O4 and PEG composites and highlights their potential for many applications in materials science, particularly in electrical and electronic devices.

Seasonal and interannual variability in the biogenic particle sinking flux was recorded using multi-year bottom-tethered sediment trap mooring systems in the Northwind Abyssal Plain (Station NAP: 75° N, 162° W, 1975 m water depth) of the western Arctic Chukchi Borderland. Trapped particle flux at a median depth of 184 m had an obvious peak and dominance of sea ice-related diatom assemblages in August 2011. The observed particle flux was considerably suppressed throughout summer 2012. In the present study, the response of ice algal production and biomass to wind-driven changes in the physical environment was addressed using a pan-Arctic sea ice–ocean modeling approach. A sea ice ecosystem with ice algae was newly incorporated into the lower-trophic marine ecosystem model, which was previously coupled with a high-resolution (i.e., 5 km horizontal grid size) sea ice–ocean general circulation model. Seasonal model experiments covering 2-year mooring periods indicated that primary productivity of ice algae around the Chukchi Borderland depended on basin-scale wind patterns via various processes. Easterly winds in the southern part of a distinct Beaufort High supplied nutrient-rich water for euphotic zones of the NAP region via both surface Ekman transport of Chukchi shelf water and vertical turbulent mixing with underlying nutricline water in 2011. In contrast, northwesterly winds flowing in the northern part of an extended Siberian High transported oligotrophic water within the Beaufort Gyre circulation toward the NAP region in 2012. The modeled ice algal biomass during summer reflected the differences in nutrient distribution. The modeled sinking flux of particulate organic nitrogen (PON) was comparable with the time series obtained from sediment trap data in summer 2011. In contrast, lateral advection of ice algal patches of shelf origin during a great cyclone event may have caused a modeled PON flux bias in 2012. Sensitivity experiments revealed several uncertainties of model configurations of ice algal productivity, particle sinking speed, and grazing activities. Extending the year-long measurements is expected to help illustrate the more general features of ice-related biological processes in the Arctic Ocean.

We investigated the mechanisms of phytoplankton competition during the spring bloom, one of the most dramatic seasonal events in lower-trophic-level ecosystems, in four state-of-the-art plankton functional type (PFT) models: PISCES, NEMURO, PlankTOM5 and CCSM-BEC. In particular, we investigated the relative importance of different ecophysiological processes on the determination of the community structure, focusing both on the bottom-up and the top-down controls. The models reasonably reproduced the observed global distribution and seasonal variation of phytoplankton biomass. The fraction of diatoms with respect to the total phytoplankton biomass increases with the magnitude of the spring bloom in all models. However, the governing mechanisms differ between models, despite the fact that current PFT models represent ecophysiological processes using the same types of parameterizations. The increasing trend in the percentage of diatoms with increasing bloom magnitude is mainly caused by a stronger nutrient dependence of diatom growth compared to nanophytoplankton (bottom-up control). The difference in the maximum growth rate plays an important role in NEMURO and PlankTOM5 and determines the absolute values of the percentage of diatoms during the bloom. In CCSM-BEC, the light dependency of growth plays an important role in the North Atlantic and the Southern Ocean. The grazing pressure by zooplankton (top-down control), however, strongly contributes to the dominance of diatoms in PISCES and CCSM-BEC. The regional differences in the percentage of diatoms in PlankTOM5 are mainly determined by top-down control. These differences in the mechanisms suggest that the response of marine ecosystems to climate change could significantly differ among models, even if the present-day ecosystem is reproduced to a similar degree of confidence. For further understanding of plankton competition and for the prediction of future change in marine ecosystems, it is important to understand the relative differences in each physiological rate and life history rate in the bottom-up and the top-down controls between PFTs.

Error-quantified, synoptic-scale relationships between chlorophyll-a (Chl-a) and phytoplankton pigment groups at the sea surface are presented. A total of ten pigment groups were considered to represent three Phytoplankton Size Classes (PSCs, micro-, nano- and picoplankton) and seven Phytoplankton Functional Types (PFTs, i.e. diatoms, dinoflagellates, green algae, prymnesiophytes (haptophytes), pico-eukaryotes, prokaryotes and Prochlorococcus sp.). The observed relationships between Chl-a and PSCs/PFTs were well-defined at the global scale to show that a community shift of phytoplankton at the basin and global scales is reflected by a change in Chl-a of the total community. Thus, Chl-a of the total community can be used as an index of not only phytoplankton biomass but also of their community structure. Within these relationships, we also found non-monotonic variations with Chl-a for certain pico-sized phytoplankton (pico-eukaryotes, Prokaryotes and Prochlorococcus sp.) and nano-sized phytoplankton (Green algae, prymnesiophytes). The relationships were quantified with a least-square fitting approach in order to enable an estimation of the PFTs from Chl-a where PFTs are expressed as a percentage of the total Chl-a. The estimated uncertainty of the relationships depends on both PFT and Chl-a concentration. Maximum uncertainty of 31.8% was found for diatoms at Chl-a = 0.49 mg m−3. However, the mean uncertainty of the relationships over all PFTs was 5.9% over the entire Chl-a range observed in situ (0.02 < Chl-a < 4.26 mg m−3). The relationships were applied to SeaWiFS satellite Chl-a data from 1998 to 2009 to show the global climatological fields of the surface distribution of PFTs. Results show that microplankton are present in the mid and high latitudes, constituting only ~10.9% of the entire phytoplankton community in the mean field for 1998–2009, in which diatoms explain ~7.5%. Nanoplankton are ubiquitous throughout the global surface oceans, except the subtropical gyres, constituting ~45.5%, of which prymnesiophytes (haptophytes) are the major group explaining ~31.7% while green algae contribute ~13.9%. Picoplankton are dominant in the subtropical gyres, but constitute ~43.6% globally, of which prokaryotes are the major group explaining ~26.5% (Prochlorococcus sp. explaining 22.8%), while pico-eukaryotes explain ~17.2% and are relatively abundant in the South Pacific. These results may be of use to evaluate global marine ecosystem models.