Cellulose Acetate Microbeads for Controlled Delivery of Essential Micronutrients

The controlled delivery of micronutrients to soil and plants is essential to increase agricultural yields. However, this is today achieved using fossil fuel-derived plastic carriers, posing environmental risks and contributing to global carbon emissions. In this work, a novel and efficient way to prepare biodegradable zinc-impregnated cellulose acetate beads for use as controlled release fertilizers is presented. Cellulose acetate solutions in DMSO were dropped into aqueous antisolvent solutions of different zinc salts. The droplets underwent phase inversion, forming solid cellulose acetate beads containing zinc, as a function of zinc salt type and concentration. Even higher values of zinc uptake (up to 15.5%) were obtained when zinc acetate was added to the cellulose acetate-DMSO solution, prior to dropping in aqueous zinc salt antisolvent solutions. The release profile in water of the beads prepared using the different solvents was linked to the properties of the counter-ions via the Hofmeister series. Studies in soil showed the potential for longer release times, up to 130 days for zinc sulfate beads. These results, together with the efficient bead production method, demonstrate the potential of zinc-impregnated cellulose acetate beads to replace the plastic-based controlled delivery products used today, contributing to the reduction of carbon emissions and potential environmental impacts due to the uptake of plastic in plants and animals.

Correction: Increased photocorrosion resistance of ZnO foams via transition metal doping

[This corrects the article DOI: 10.1039/D2RA06730G.].

Increased photocorrosion resistance of ZnO foams via transition metal doping

ZnO is a widely studied photocatalyst, but practical use is hindered by its low resistance to photocorrosion in water, which leads to metal leaching and loss of performance over time. In this work, highly porous and mechanically stable ZnO foams, called MolFoams, were doped by adding 1% or 2% Co, Ni or Cu salts to the starting Zn salt, followed by air insufflation during a sol-gel rection and sintering. The resulting doped foams showed a major increase in stability, with a 60-85% reduction in Zn²⁺ leaching after irradiation, albeit with a reduction in photocatalytic activity. A systematic analysis using XRD, Raman, XPS and XANES allowed for the identification of dopant species in the foams revealing the presence of Co₃O₄, NiO and Cu₂O within the ZnO lattice with doping leading to a reduced band gap and significant increases in the resistance to photocorrosion of ZnO while identifying the cause of the reduction in photocatalytic activity to be shifting of the band edge positions. These results provide a pathway to significantly reduce the photocorrosion of ZnO in water, with further work required to maintain the photocatalytic activity of undoped ZnO.

Continuous production of cellulose microbeads by rotary jet atomization

The replacement of plastic microbeads with biodegradable alternatives is essential due to the environmental persistence of plastics and their accumulation within the human food chain.

HYPOTHESIS

Cellulose microbeads could be such alternative, but their production is hindered by the high viscosity of cellulose solutions. It is expected that this viscosity can be harnessed to induce filament thinning of jets of cellulose solutions to create droplets with diameters within the micrometre range, which can then be converted to solid cellulose microbeads via phase inversion.

EXPERIMENTS

A 3D printed rotating multi-nozzle system was used to generate jets of cellulose dissolved in solutions of [EMIm][OAc] and DMSO. The jets were subject to Rayleigh breakup to generate droplets which were captured in an ethanol anti-solvent bath, initiating phase-inversion, and resulting in regeneration of the cellulose into beads.

FINDINGS

Control of both process (e.g. nozzle dimensions) and operational (e.g. rotational speed and pressure) parameters has allowed suppression of both satellite droplets generation and secondary droplet break-up, and tuning of the filament thinning process. This resulted in the continuous fabrication of cellulose microbeads in the size range 40-500 μm with narrow size distributions. This method can produce beads in size ranges not attainable by existing technologies.

Low Frequency Brain Oscillations during the execution and imagination of simple hand movements for Brain-Computer Interface applications

Low Frequency Brain Oscillations (LFOs) are brief periods of oscillatory activity in delta and lower theta band that appear at motor cortical areas before and around movement onset. It has been shown that LFO power decreases in post-stroke patients and re-emerges with motor functional recovery. To date, LFOs have not yet been explored during the motor execution (ME) and imagination (MI) of simple hand movements, often used in BCI-supported motor rehabilitation protocols post-stroke. This study aims at analyzing the LFOs during the ME and MI of the finger extension task in a sample of 10 healthy subjects and 2 stroke patients in subacute phase. The results showed that LFO power peaks occur in the preparatory phase of both ME and MI tasks on the sensorimotor channels in healthy subjects and their alterations in stroke patients. Clinical Relevance- Results suggest that LFOs could be explored as biomarker of the motor function recovery in rehabilitative protocols based on the movement imagination.

Cortico-Muscular Coupling Allows to Discriminate Different Types of Hand Movements

Cortico-muscular coupling (CMC) could be used as potential input of a novel hybrid Brain-Computer Interface (hBCI) for motor re-learning after stroke. Here, we aim of addressing the design of a hBCI able to classify different movement tasks taking into account the interplay between the cerebral and residual or recovered muscular activity involved in a given movement. Hence, we compared the performances of four classification methods based on CMC features to evaluate their ability in discriminating finger extension from grasping movements executed by 17 healthy subjects. We also explored how the variation in the dimensionality of the feature domain would influence the different classifier performances. Results showed that, regardless of the model, few CMC features (up to 10) allow for a successful classification of two different movements type. Moreover, support vector machine classifier with linear kernel showed the best trade-off between performances and system usability (few electrodes). Thus, these results suggest that a hBCI based on brain-muscular interplay holds the potential to enable more informed neural plasticity and functional motor recovery after stroke. Furthermore, this CMC-based BCI could also allow for a more "natural control" (l.e., that resembling physiological control) of prosthetic devices.

Distinctive physiological muscle synergy patterns define the Box and Block Task execution as revealed by electromyographic features

Stroke survivors experience muscular pattern alterations of the upper limb that decrease their ability to perform daily-living activities. The Box and Block test (BBT) is widely used to assess the unilateral manual dexterity. Although BBT provides insights into functional performance, it returns limited information about the mechanisms contributing to the impaired movement. This study aims at exploring the BBT by means of muscle synergies analysis during the execution of BBT in a sample of 12 healthy participants with their dominant and non-dominant upper limb. Results revealed that: (i) the BBT can be described by 1 or 2 synergies; the number of synergies (ii) does not differ between dominant and non-dominant sides and (iii) varies considering each phase of the task; (iv) the transfer phase requires more synergies. Clinical Relevance- This preliminary study characterizes muscular synergies during the BBT task in order to establish normative patterns that could assist in understanding the neuromuscular demands and support future evaluations of stroke deficits.

Stable Cellulose Nanofibril Microcapsules from Pickering Emulsion Templates

Electrostatic attractions are essential in any complex formation between the nanofibrils of the opposite charge for a specific application, such as microcapsule production. Here, we used cationized cellulose nanofibril (CCNF)-stabilized Pickering emulsions (PEs) as templates, and the electrostatic interactions were induced by adding oxidized cellulose nanofibrils (OCNFs) at the oil-water interface to form microcapsules (MCs). The oppositely charged cellulose nanofibrils enhanced the solidity of interfaces, allowing the encapsulation of Nile red (NR) in sunflower oil droplets. Microcapsules exhibited a low and controlled release of NR at room temperature. Furthermore, membrane emulsification was employed to scale up the preparation of microcapsules with sunflower oil (SFO) encapsulated by CCNF/OCNF complex networks.

Metal Oxide Foams for Pharmaceutical Amorphization

The amorphization of pharmaceutical crystals is an effective strategy to enhance the bioavailability of poorly soluble active pharmaceutical ingredients (APIs). However, this process can be challenging as these supramolecular structures...

Keratin-Chitosan Microcapsules via Membrane Emulsification and Interfacial Complexation

The continuous fabrication via membrane emulsification of stable microcapsules using renewable, biodegradable biopolymer wall materials keratin and chitosan is reported here for the first time. Microcapsule formation was based on opposite charge interactions between keratin and chitosan, which formed polyelectrolyte complexes when solutions were mixed at pH 5.5. Interfacial complexation was induced by transfer of keratin-stabilized primary emulsion droplets to chitosan solution, where the deposition of chitosan around droplets formed a core-shell structure. Capsule formation was demonstrated both in batch and continuous systems, with the latter showing a productivity up to 4.5 million capsules per minute. Keratin-chitosan microcapsules (in the 30-120 μm range) released less encapsulated nile red than the keratin-only emulsion, whereas microcapsules cross-linked with glutaraldehyde were stable for at least 6 months, and a greater amount of cross-linker was associated with enhanced dye release under the application of force due to increased shell brittleness. In light of recent bans involving microplastics in cosmetics, applications may be found in skin-pH formulas for the protection of oils or oil-soluble compounds, with a possible mechanical rupture release mechanism (e.g., rubbing on skin).

Untangling the physics of water transport in boron nitride nanotubes

Carbon nanotubes (CNTs) have long been heralded as the material of choice for next-generation membranes. Some studies have suggested that boron nitride nanotubes (BNNTs) may offer higher transport of pure water than CNTs, while others conclude otherwise. In this work, we use a combination of simulations and experimental data to uncover the causes of this discrepancy and investigate the flow resistance through BNNT membranes in detail. By dividing the resistance of the nanotube membranes into their contributing components, we study the effects of pore end configuration, membrane length, and BNNT atom partial charges. Most molecular simulation studies of BNNT membranes use short membranes connected to high and low pressure reservoirs. Here we find that flow resistances in these short membranes are dominated by the resistance at the pore ends, which can obscure the understanding of water transport performance through the nanotubes and comparison between different nanotube materials. In contrast, it is the flow resistance inside the nanotubes that dominates microscale-thick laboratory membranes, and end resistances tend to be negligible. Judged by the nanotube flow resistance alone, we therefore find that CNTs are likely to consistently outperform BNNTs. Furthermore, we find a large role played by the choice of partial charges on the BN atoms in the flow resistance measurements in our molecular simulations. This paper highlights a way forward for comparing molecular simulations and experimental results.

Enzyme-Functionalized Cellulose Beads as a Promising Antimicrobial Material

The extensive use of antibiotics over the last decades is responsible for the emergence of multidrug-resistant (MDR) microorganisms that are challenging health care systems worldwide. The use of alternative antimicrobial materials could mitigate the selection of new MDR strains by reducing antibiotic overuse. This paper describes the design of enzyme-based antimicrobial cellulose beads containing a covalently coupled glucose oxidase from Aspergillus niger (GOx) able to release antimicrobial concentrations of hydrogen peroxide (H₂O₂) (≈ 1.8 mM). The material preparation was optimized to obtain the best performance in terms of mechanical resistance, shelf life, and H₂O₂ production. As a proof of concept, agar inhibition halo assays (Kirby-Bauer test) against model pathogens were performed. The two most relevant factors affecting the bead functionalization process were the degree of oxidation and the pH used for the enzyme binding process. Slightly acidic conditions during the functionalization process (pH 6) showed the best results for the GOx/cellulose system. The functionalized beads inhibited the growth of all the microorganisms assayed, confirming the release of sufficient antimicrobial levels of H₂O₂. The maximum inhibition efficiency was exhibited toward Pseudomonas aeruginosa (P. aeruginosa) and Escherichia coli (E. coli), although significant inhibitory effects toward methicillin-resistant Staphylococcus aureus (MRSA) and S. aureus were also observed. These enzyme-functionalized cellulose beads represent an inexpensive, sustainable, and biocompatible antimicrobial material with potential use in many applications, including the manufacturing of biomedical products and additives for food preservation.

Multienzyme Cellulose Films as Sustainable and Self-Degradable Hydrogen Peroxide-Producing Material

The use of hydrogen peroxide-releasing enzymes as a component to produce alternative and sustainable antimicrobial materials has aroused interest in the scientific community. However, the preparation of such materials requires an effective enzyme binding method that often involves the use of expensive and toxic chemicals. Here, we describe the development of an enzyme-based hydrogen peroxide-producing regenerated cellulose film (RCF) in which a cellobiohydrolase (TrCBHI) and a cellobiose dehydrogenase (MtCDHA) were efficiently adsorbed, 90.38 ± 2.2 and 82.40 ± 5.7%, respectively, without making use of cross-linkers. The enzyme adsorption kinetics and binding isotherm experiments showed high affinity of the proteins possessing cellulose-binding modules for RCF, suggesting that binding on regenerated cellulose via specific interactions can be an alternative method for enzyme immobilization. Resistance to compression and porosity at a micrometer scale were found to be tunable by changing cellulose concentration prior to film regeneration. The self-degradation process, triggered by stacking TrCBHI and MtCDHA (previously immobilized onto separate RCF), produced 0.15 nmol/min·cm² of H₂O₂. Moreover, the production of H₂O₂ was sustained for at least 24 h reaching a concentration of ∼2 mM. The activity of MtCDHA immobilized on RCF was not affected by reuse for at least 3 days (1 cycle/day), suggesting that no significant enzyme leakage occurred in that timeframe. In the material herein designed, cellulose (regenerated from a 1-ethyl-3-methylimidazolium acetate/dimethyl sulfoxide (DMSO) solution) serves both as support and substrate for the immobilized enzymes. The sequential reaction led to the production of H₂O₂ at a micromolar-millimolar level revealing the potential use of the material as a self-degradable antimicrobial agent.

Enhanced nanoparticle rejection in aligned boron nitride nanotube membranes

The rejection of particles with different charges and sizes, ranging from a few Ångstroms to tens of nanometers, is key to a wide range of industrial applications, from wastewater treatment to product purification in biotech processes. Carbon nanotubes (CNTs) have long held the promise to revolutionize filtration, with orders of magnitude higher fluxes compared to commercial membranes. CNTs, however, can only reject particles and ions wider than their internal diameter. In this work, the fabrication of aligned boron nitride nanotube (BNNT) membranes capable of rejecting nanoparticles smaller than their internal diameter is reported for the first time. This is due to a mechanism of charge-based rejection in addition to the size-based one, enabled by the BNNTs surface structure and chemistry and elucidated here using high fidelity molecular dynamics and Brownian dynamics simulations. This results in ∼40% higher rejection of the same particles by BNNT membranes than CNT ones with comparable nanotube diameter. Furthermore, since permeance is proportional to the square of the nanotubes' diameter, using BNNT membranes with ∼30% larger nanotube diameter than a CNT membrane with comparable rejection would result in up to 70% higher permeance. These results open the way to the design of more effective nanotube membranes, capable of high particle rejection and, at the same time, high water permeance.

Shedding Light Onto the Nature of Iron Decorated Graphene and Graphite Oxide Nanohybrids for CO2 Conversion at Atmospheric Pressure

We report on the design and testing of new graphite and graphene oxide-based extended π-conjugated synthetic scaffolds for applications in sustainable chemistry transformations. Nanoparticle-functionalised carbonaceous catalysts for new Fischer Tropsch and Reverse GasWater Shift (RGWS) transformations were prepared: functional graphene oxides emerged from graphite powders via an adapted Hummer's method and subsequently impregnated with uniform-sized nanoparticles. Then the resulting nanomaterials were imaged by TEM, SEM, EDX, AFM and characterised by IR, XPS and Raman spectroscopies prior to incorporation of Pd(II) promoters and further microscopic and spectroscopic analysis. Newly synthesised 2D and 3D layered nanostructures incorporating carbon-supported iron oxide nanoparticulate pre-catalysts were tested, upon hydrogen reduction in situ, for the conversion of CO2 to CO as well as for the selective formation of CH4 and longer chain hydrocarbons. The reduction reaction was also carried out and the catalytic species isolated and fully characterised. The catalytic activity of a graphene oxide-supported iron oxide pre-catalyst converted CO2 into hydrocarbons at different temperatures (305, 335, 370 and 405 °C), and its activity compared well with that of the analogues supported on graphite oxide, the 3-dimensional material precursor to the graphene oxide. Investigation into the use of graphene oxide as a framework for catalysis showed that it has promising activity with respect to reverse gas water shift (RWGS) reaction of CO2 to CO, even at the low levels of catalyst used and under the rather mild conditions employed at atmospheric pressure. Whilst the γ-Fe2O3 decorated graphene oxide-based pre-catalyst displays fairly constant activity up to 405 °C, it was found by GC-MS analysis to be unstable with respect to decomposition at higher temperatures. The addition of palladium as a promoter increased the activity of the iron functionalised graphite oxide in the RWGS. The activity of graphene oxide supported catalysts was found to be enhanced with respect to that of iron-functionalised graphite oxide with, or without palladium as a promoter, and comparable to that of Fe@carbon nanotube-based systems tested under analogous conditions. These results display a significant step forward for the catalytic activity estimations for the iron functionalised and rapidly processable and scalable graphene oxide. The hereby investigated phenomena are of particular relevance for the understanding of the intimate surface morphologies and the potential role of non-covalent interactions in the iron oxide-graphene oxide networks, which could inform the design of nano-materials with performance in future sustainable catalysis applications.

Enhancing the photo-corrosion resistance of ZnO nanowire photocatalysts

Zinc oxide (ZnO) displays superior properties as a photocatalyst, compared to the more widely used TiO₂. However, widespread application of ZnO is hampered by its high photo-corrosion in aqueous environments under UV irradiation. A systematic investigation of the effect of (i) post-production annealing, (ii) dissolved oxygen levels during photocatalysis and (iii) reactor configuration on the stability and photocatalytic activity (PCA) of ZnO nanowires, grown on either flat or circular supports, was conducted. Results show, for the first time, that it is possible to significantly enhance the photo-corrosion resistance of ZnO in water under UV irradiation while also increasing PCA. Oxygen plasma post-annealing of ZnO nanowire films led to a 46% higher photocatalytic degradation of phenol compared to as-produced films. In oxygen-saturated solutions, both thermally and oxygen plasma annealed ZnO revealed similar photo-corrosion resistance. Switching from a batch to a flow-through reactor, tripled phenol photodegradation under the same irradiation conditions from 19 to 57% due to enhanced mass transfer, while significantly increasing the stability and re-usability of the ZnO, with 5 repeat uses over 3 days showing no decrease in PCA. These results pave the way to more widespread use of photo-corrosion resistant ZnO in the degradation of organic pollutants in water.

3D Printed Fouling-Resistant Composite Membranes

Fouling remains a long-standing unsolved problem that hinders the widespread use of membrane applications in industry. This article reports the use of numerical simulations coupled with extensive material synthesis and characterization to fabricate fouling-resistant 3D printed composite membranes. The membranes consist of a thin polyethersulfone selective layer deposited onto a 3D printed flat and double sinusoidal (wavy) support. Fouling and cleaning of the composite membranes were tested by using bovine serum albumin solution in a cross-flow ultrafiltration setup. The transmembrane pressure was regulated at 1 bar and the cross-flow Reynolds number (Re) varied between 400 and 1000. In comparison to the flat membrane, the wavy membrane showed superior performance in terms of pure water permeance (PWP) (10% higher) and permeance recovery ratio (87% vs 53%) after the first filtration cycle at Re = 1000. Prolong testing showed that the wavy membrane could retain approximately 87% of its initial PWP after 10 complete filtration cycles. This impressive fouling-resistant behavior is attributed to the localized fluid turbulence induced by the 3D printed wavy structure. These results show that not only the lifetime of membrane operations could be favorably extended but also the operational costs and environmental damage of membrane-based processes could also be significantly reduced.

The Optimal Setting for Multilayer Modularity Optimization in Multilayer Brain Networks

Community detection plays a key role in the study of brain networks, as mechanisms of modular integration and segregation are known to characterize the brain functioning. Moreover, brain networks are intrinsically multilayer: they can vary across time, frequency, subjects, conditions, and meaning, according to different definitions of connectivity. Several algorithms for the multilayer community detection were defined to identify communities in time-varying networks. The most used one is based on the optimization of a multilayer formulation of the modularity, in which two parameters linked to the spatial (γ) and temporal (ω) resolution of the uncovered communities can be set. While the effect of different γ-values has been largely explored, which ω-values are most suitable to different purposes and conditions is still an open issue. In this work, we test the algorithm performances under different values of ω by means of ad hoc implemented benchmark graphs that cover a wide range of conditions. Results provide a guide to the choice of the ω-values according to the network features. Finally, we show an application of the algorithm to real functional brain networks estimated from electro-encephalographic (EEG) signals collected at rest with closed and open eyes. The application to real data agrees with the results of the simulation study and confirms the conclusion drawn from it.

Adaptive learning in the detection of Movement Related Cortical Potentials improves usability of associative Brain-Computer Interfaces

Brain-computer interfaces have increasingly found applications in motor function recovery in stroke patients. In this context, it has been demonstrated that associative-BCI protocols, implemented by means the movement related cortical potentials (MRCPs), induce significant cortical plasticity. To date, no methods have been proposed to deal with brain signal (i.e. MRCP feature) non-stationarity. This study introduces adaptive learning methods in MRCP detection and aims at comparing a no-adaptive approach based on the Locality Sensitive Discriminant Analysis (LSDA) with three LSDA-based adaptive approaches. As a proof of concept, EEG and force data were collected from six healthy subjects while performing isometric ankle dorsiflexion. Results revealed that adaptive algorithms increase the number of true detections and decrease the number of false positives per minute. Moreover, the markedly reduction of BCI system calibration time suggests that these methods have the potential to improve the usability of associative-BCI in post-stroke motor recovery.

Surface-Controlled Water Flow in Nanotube Membranes

The independent effect of nanotube surface chemistry and structure on the flow of water under nanoscale confinement is demonstrated in this paper for the first time via the synthesis of novel carbon nitride nanotube (CNNT) membranes. Using a combination of experiments and high-fidelity molecular dynamics (MD) simulations, it is shown here that the hydrophilization of the sp² carbon structure, induced by the presence of the C-N bonds, decreases the pure water permeance in CNNTs when compared with pristine and turbostratic carbon nanotubes (CNTs). The MD simulations are based on a model true to the chemical structure of the synthesized nanotubes, built from spectroscopy measurements and calibrated potentials using droplet experiments. The effect on permeance is explained in terms of solid-liquid interactions at the nanotube wall with increased water viscosity and decreased surface diffusion near the CNNT wall, when compared to CNTs. A model directly linking the solid-liquid interactions to the water permeance is presented, showing good agreement with both experiments and MD simulations. This work opens the way to tailoring surface chemistry and structure inside nanotube membranes for a wide range of transport and separation processes.

Continuous-flow liquid-phase dehydrogenation of 1,4-cyclohexanedione in a structured multichannel reactor

A highly selective continuous-flow process is developed for liquid-phase dehydrogenation to produce a high yield of hydroquinone in a millimetre-scale structured multichannel reactor.

Transcranial cerebellar direct current stimulation: Effects on brain resting state oscillatory and network activity

Transcranial cerebellar direct current stimulation (tcDCS) can offer new insights into the cerebellar function and disorders, by modulating noninvasively the activity of cerebellar networks. Taking into account the functional interplay between the cerebellum and the cerebral cortex, we addressed the effects of unilateral tcDCS (active electrode positioned over the right cerebellar hemisphere) on the electroencephalographic (EEG) oscillatory activity and on the cortical network organization at resting state. Effects on spectral (de)synchronizations and functional connectivity after anodal and cathodal stimulation were assessed with respect to a sham condition. A lateralized synchronization over the sensorimotor area in gamma band, as well as an increase of the network segregation in sensory-motor rhythms and a higher communication between hemispheres in gamma band, were detected after anodal stimulation. The same measures after cathodal tcDCS returned responses similar to the sham condition.

Bean Seedling Growth Enhancement Using Magnetite Nanoparticles

Advanced fertilizers are one of the top requirements to address rising global food demand. This study investigates the effect of bare and polyethylene glycol-coated Fe₃O₄ nanoparticles on the germination and seedling development of Phaseolus vulgaris L. Although the germination rate was not affected by the treatments (1 to 1 000 mg Fe L^-1), seed soaking in Fe₃O₄-PEG at 1 000 mg Fe L^-1 increased radicle elongation (8.1 ± 1.1 cm vs 5.9 ± 1.0 cm for the control). Conversely, Fe²⁺/Fe³⁺_(aq) and bare Fe₃O₄ at 1 000 mg Fe L^-1 prevented the growth. X-ray spectroscopy and tomography showed that Fe penetrated in the seed. Enzymatic assays showed that Fe₃O₄-PEG was the least harmful treatment to α-amylase. The growth promoted by the Fe₃O₄-PEG might be related to water uptake enhancement induced by the PEG coating. These results show the potential of using coated iron nanoparticles to enhance the growth of common food crops.

Sustainable Synthesis of Oxalic and Succinic Acid through Aerobic Oxidation of C6 Polyols Under Mild Conditions

The sustainable chemical industry encompasses a shift from the use of fossil carbon to renewable carbon. The synthesis of chemicals from nonedible biomass (cellulosic or oil) represents one of the key steps for "greening" the chemical industry. In this paper, we report the aerobic oxidative cleavage of C6 polyols (5-HMF, glucose, fructose and sucrose) to oxalic acid (OA) and succinic acid (SA) in water under mild conditions using M@CNT and M@NCNT (M=Fe, V; CNT=carbon nanotubes; NCNT=N-doped CNT), which, under suitable conditions, were recoverable and reusable without any loss of efficiency. The influence of the temperature, O₂ pressure (PO2 ), reaction time and stirring rate are discussed and the best reaction conditions are determined for an almost complete conversion of the starting material and a good OA yield of 48 %. SA and formic acid were the only co-products. The former could be further converted into OA by oxidation in the presence of formic acid, resulting in an overall OA yield of >62 %. This process was clean and did not produce organic waste nor gas emissions.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes.

Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used.

Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes.

Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

Multiscale design of ZnO nanostructured photocatalysts

Light absorbance and crystal orientation, not surface area, determine the photocatalytic efficiency of immobilize ZnO nanowire films.

Kinetics of CO2 Hydrogenation to Hydrocarbons over Iron-Silica Catalysts

The conversion of CO₂ to hydrocarbons is increasingly seen as a potential alternative source of fuel and chemicals, while at the same time contributing to addressing global warming effects. An understanding of kinetics and mass transfer limitations is vital to both optimise catalyst performance and to scale up the whole process. In this work we report on a systematic investigation of the influence of the different process parameters, including pore size, catalyst support particle diameter, reaction temperature, pressure and reactant flow rate on conversion and selectivity of iron nanoparticle -silica catalysts. The results provided on activation energy and mass transfer limitations represent the basis to fully design a reactor system for the effective catalytic conversion of CO₂ to hydrocarbons.

Community detection: Comparison among clustering algorithms and application to EEG-based brain networks

Community structure is a feature of complex networks that can be crucial for the understanding of their internal organization. This is particularly true for brain networks, as the brain functioning is thought to be based on a modular organization. In the last decades, many clustering algorithms were developed with the aim to identify communities in networks of different nature. However, there is still no agreement about which one is the most reliable, and to test and compare these algorithms under a variety of conditions would be beneficial to potential users. In this study, we performed a comparative analysis between six different clustering algorithms, analyzing their performances on a ground-truth consisting of simulated networks with properties spanning a wide range of conditions. Results show the effect of factors like the noise level, the number of clusters, the network dimension and density on the performances of the algorithms and provide some guidelines about the use of the more appropriate algorithm according to the different conditions. The best performances under a wide range of conditions were obtained by Louvain and Leicht & Newman algorithms, while Ronhovde and Infomap proved to be more appropriate in very noisy conditions. Finally, as a proof of concept, we applied the algorithms under exam to brain functional connectivity networks obtained from EEG signals recorded during a sustained movement of the right hand, obtaining a clustering of scalp electrodes which agrees with the results of the simulation study conducted.

EEG source estimation accuracy in presence of simulated cortical lesions

Methods to reconstruct the neuroelectrical activity in the brain source space can be used to improve the spatial resolution of scalp-recorded EEG and to estimate the locations of electrical sources in the brain. This procedure can improve the investigation of the functional organization of the human brain, exploiting the high temporal resolution of EEG to follow the temporal dynamics of information processing. As for today, the uncertainties about the effects of inhomogeneities due to brain lesions preclude the adoption of EEG functional mapping on patients with lesioned brain. The aim of this work is to quantify the accuracy of a distributed source localization method in recovering extended sources of activated cortex when cortical lesions of different dimensions are introduced in simulated data. For this purpose, EEG source-distributed activity estimated from real data was modified including silent lesion areas. Then, for each simulated lesion, forward and inverse calculations were carried out to localize the produced scalp activity and the reconstructed cortical activity. Finally, the error induced in the reconstruction by the presence of the lesion was computed and analyzed in relation to the number of electrodes and to the size of the simulated lesion. Results returned values of global error in the whole cortex and of error in the non-lesioned area which are strongly dependent on the number of recorded scalp sensors, as they increase when a lower spatial sampling is performed on the scalp (64 versus 32 EEG channels). For increasing spatial sampling frequencies, the accuracy of the source reconstruction improves and even the presence of small lesions induces significantly higher error levels with respect to the lesion-free condition.

Brain connectivity networks at the basis of human attention components: An EEG study

The Attention Network Task (ANT) was developed to disentangle the three components of attention identified in the Posner's theoretical model (alerting, orienting and executive control) and to measure the corresponding behavioral efficiency. Several fMRI studies have already provided evidences on the anatomical separability and interdependency of these three networks, and EEG studies have also unveiled the associated brain rhythms. What is still missing is a characterization of the brain circuits subtending the attentional components in terms of directed relationships between the brain areas and their frequency content. Here, we want to exploit the high temporal resolution of the EEG, improving its spatial resolution by means of advanced source localization methods, and to integrate the resulting information by a directed connectivity analysis. The results showed in the present study demonstrate the possibility to associate a specific directed brain circuit to each attention component and to identify synthetic indices able to selectively describe their neurophysiological, spatial and spectral properties.

Measuring the agreement between brain connectivity networks

Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing the estimated network with the corresponding ground-truth network; ii) in applications to real data, when it is necessary to compare the structure of a network obtained in a specific subject with a reference (e.g. a baseline condition or normative data). In the simulations, the level of similarity between two networks was manipulated through different factors. We then investigated the effect of such manipulations on the measures of association. Results showed how the three parameters modulated their values according to the level of similarity between the two networks. In particular, the AUC provided the better performances in terms of its capability to synthetize the similarity between two networks, showing high dynamic and sensitivity.

Nanostructured WO3 photoanodes for efficient water splitting via anodisation in citric acid

We report the production of nanostructured WO₃ photoanodes for solar water splitting produced via anodisation using for the first time citric acid, a safer and more environmentally friendly alternative to fluoride-based electrolytes.

Effect of inter-trials variability on the estimation of cortical connectivity by Partial Directed Coherence

Partial Directed Coherence (PDC) is a powerful estimator of effective connectivity. In neuroscience it is used in different applications with the aim to investigate the communication between brain regions during the execution of different motor or cognitive tasks. When multiple trials are available, PDC can be computed over multiple realizations, provided that the assumption of stationarity across trials is verified. This allows to improve the amount of data, which is an important constraint for the estimation accuracy. However, the stationarity of the data across trials is not always guaranteed, especially when dealing with patients. In this study we investigated how the inter-trials variability of an EEG dataset affects the PDC accuracy. Effects of density variations and of changes of connectivity values across trials were first investigated with a simulation study and then tested on real EEG data collected from two post-stroke patients during a motor imagery task and characterized by different inter-trials variability. Results showed the effect of different factors on the PDC accuracy and the robustness of such estimator in a range of conditions met in practical applications.

Graph theory in brain-to-brain connectivity: A simulation study and an application to an EEG hyperscanning experiment

Hyperscanning consists in the simultaneous recording of hemodynamic or neuroelectrical signals from two or more subjects acting in a social context. Well-established methodologies for connectivity estimation have already been adapted to hyperscanning purposes. The extension of graph theory approach to multi-subjects case is still a challenging issue. In the present work we aim to test the ability of the currently used graph theory global indices in describing the properties of a network given by two interacting subjects. The testing was conducted first on surrogate brain-to-brain networks reproducing typical social scenarios and then on real EEG hyperscanning data recorded during a Joint Action task. The results of the simulation study highlighted the ability of all the investigated indexes in modulating their values according to the level of interaction between subjects. However, only global efficiency and path length indexes demonstrated to be sensitive to an asymmetry in the communication between the two subjects. Such results were, then, confirmed by the application on real EEG data. Global efficiency modulated, in fact, their values according to the inter-brain density, assuming higher values in the social condition with respect to the non-social condition.

Interfacing brain with computer to improve communication and rehabilitation after brain damage

Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.