The metabolism of methane is an important part of the global carbon cycle. While deciphering the community function and the potential role of the different functional guilds is very difficult when considering native complex communities, synthetic communities, built of species originating from a study site in question, present a simplified model and allow specific questions to be addressed as to carbon, nitrogen, and other nutrient transfer among species in a controlled system. This study applies an ecophysiological approach, as a proof of principle, to an already well-studied model system, contributing to a better understanding of microbial community function and microbial ecosystem processes. Methylococcaceae methanotrophs and the Methylophilaceae methylotrophs, along with minor but persistent partners, members of Burkholderiales and Flavobacteriales. As a proof of concept, we established minimalist synthetic communities that were representative of the four functional guilds to demonstrate the dependency of the non-methane-utilizing species on the methanotrophs as the primary carbon-providing species. We observed that in communities consisting of multiple representatives of the key guilds, members of the same guild appeared to compete for resources. For example, when two methanotrophs of the same family were present, the two expressed similar key methanotrophy pathways and responded similarly to changing environmental conditions, suggesting that they perform a similar keystone function in situ. Similar observations were made for the Methylophilaceae. However, differences were noted in the expression of auxiliary and unique genes among strains of the same functional guild, reflecting differential adaptation and suggesting mechanisms for competition. At the same time, differences were also noted in the performances of partners with specific metabolic schemes. For example, a mutant of Methylotenera mobilis impaired in nitrate utilization behaved as a more efficient cooperator in methane consumption, suggesting that the loss of function may lead to changes in communal behavior. Overall, we demonstrate the robust nature of synthetic communities built of native lake sediment strains and their utility in addressing important ecological questions while using a simplified model.The factors and processes that influence the behavior and functionality of ecosystems inhabited by complex microbiomes are still far from being clearly understood. Synthetic microbial communities provide reduced-complexity models that allow an examination of ecological theories under defined and controlled conditions. In this study, we applied a multiphasic approach to study synthetic methane-oxidizing communities and species interactions as proxies to the natural communities. Our results confirm that, under selective pressures, natural-sediment communities of high complexity simplify rapidly, selecting for several major functional guilds, the major partners in methane oxidation being the  In this regard, a model synthetic system that links function to identity can help elucidate the relationships between microorganisms and ecosystem processes , suggesting that, likely, multicarbon substrates excreted by either Methylococcaceae or Methylophilaceae or both were supporting their growth. Several strains  of each species in cocultures steadily increased. The pure culture of Methylomonas sp. LW13 showed the highest optical densities compared to other coculture treatments (Methylomonas sp. LW13 to M. methylotrophus Q8 plus Acidovorax sp. 30 and Flavobacterium sp. 81) over the course of the experiment. As predicted, the nonmethanotroph partners  did not grow on methane as the sole source of carbon  DNA was observed over time  was employed. For this experiment, a four-strain coculture was established from ver time . Sequencn biases . Note thee bands  and C, ree bands . These DMethylococcaceae and some Methylophilaceae encode functions for either partial or complete respiratory denitrification  subunit  encodes functions for the  respiratory denitrification pathway , resulting in the loss of its ability to both grow with nitrate and to denitrify . Information on the strains employed in this study, along with the information on previously described Lake Washington sediment isolates, can be found in Burkholderiales nor Flavobacteriales encoded any known methylotrophy functions, and tests for growth on methane or methanol, respectively, were negative (data not shown).To test our concept of additional functional guilds as prominent members of methane-oxidizing communities, we carried out isolation experiments, aiming at obtaining pure cultures of mic data , 19. We mic data , 19, andmic data , 19. Dilmic data . Strains600) values. Prior to mixing, pure cultures were grown in either nitrate minimal salts (NMS) (4 was injected into the headspace. Tubes were incubated with shaking (200 rpm) at 18°C. The headspace gas composition was replenished every 24 h. The OD600 measurements were carried out using a Jenway 7300 spectrophotometer  every 4 to 6 h. The values presented represent the results of 3 measurements, reported with a standard error. For experiments involving Methylotenera mobilis JLW8, lanthanum (10 μM) was added to the medium, as this organism relies on lanthanides for growth on methanol   plus metts (NMS)  medium umethanol .To obtain real-time relative population abundances, cell numbers were determined by flow cytometry. The differences in cell shape and size allowed for separation of all four populations . Nine huMethylobacter tundripaludum 31/32, Methylophilus methylotrophus Q8, Acidovorax sp. 30s, and Flavobacterium sp. 81, were mixed at equal optical densities. Fifty-milliliter cocultures were grown in NMS medium in 250-ml vials, with 25% 13C-labeled methane/75% air (vol/vol) in the headspace for 24, 48, 72, and 96 h, using 3 vials per experiment. Their contents were mixed before DNA extraction. DNA was extracted using the MPBio FastDNA Spin kit for soil , following the manufacturer’s instructions. The heavy (13C-enriched) fractions of DNA were separated from the light (12C) fractions by CsCl-SYBR green dye density gradient ultracentrifugation, visualized under UV, and collected and purified following standard procedures, as previously described , samples were flushed with dinitrogen and supplemented with methane (25%), but not air, to create hypoxic conditions. Samples for RNA extraction were taken after 1, 25, and 49 h. Total RNA was extracted as previously described (Cocultures (50 ml) were assembled by mixing four or 14 strains at equal optical densities. These were grown in NMS medium with 25% methane/75% air (vol/vol) in the headspace, in duplicate. When cultures reached an ODThe transcript sequences have been archived with the IMG Genome Portal IMG genome identifiers (IDs) 1125205, 1125208, 1125211, 1125214, 1125217, 1125220, 1125223, 1125226, 1125229, 1125232, 1125235, and 1125238.


People high in negative affect (anxiety or depression) show cognitive distortions, specific thinking errors which contribute to the maintenance of their condition. It is thought that weak attentional control is a risk factor for negative affect and emotional disorders, because weak attentional control exaggerates the expression of attentional bias, another cognitive feature of emotional disorders. We wondered whether weak attentional control might similarly exaggerate the expression of cognitive distortions. In two samples of students from Turkey and the UK, we found that weak attentional control was indeed related to cognitive distortions, but this relationship was explained by both variables’ relationships with negative affect. This suggests that weak attentional control, while related to negative affect, does not necessarily exaggerate all of its cognitive features. There seems to be a limit on the affective consequences of poor attentional control, which may limit its clinical usefulness as a risk factor for emotional disorders. Disorders related to negative affect, i.e. anxiety and depression, are associated with certain cognitive distortions. In this article, we present evidence that the expression of such cognitive distortions is unrelated to attentional control.We studied the link between cognitive distortions and attentional control because attentional control has been implicated in the expression of another cognitive feature of emotional disorders and negative affect: attentional bias to threat (e.g.). StudieWe wanted to know whether the relationship between attentional control and attentional bias would generalise: whether attentional control would influence the expression of other cognitive features of negative affect or emotional disorders. There are two possibilities here: it is implied in the literature (e.g. ) that wecognitive distortions. These are habitual errors in thinking, not necessarily explicitly negative, but characteristic of emotional disorders [In the present studies, we tested whether attentional control was related to the expression of isorders –29. Muchisorders ), but thisorders , 32. Likisorders –37. StudStudy 1 was conducted with Turkish students studying at a small private university in Istanbul. We assessed their attentional control using the self-report scale commonly used in the attentional bias literature, their anxiety and depression, and their tendency to exhibit cognitive distortions.The study was approved by the Sabancı University Research Ethics Committee (FASS-2018-35), and participants gave their written consent.Mage = 19.47, SDage = 0.87). Data were collected in person by the last eight authors, as a class project.Eighty-two native Turkish speaking students from Sabancı University participated voluntarily. Six were excluded from analyses as they reported a current psychiatric diagnosis other than an anxiety or affective disorder, and another four were excluded for skipping too many questions. The final sample for analysis consisted of 72 students , but this correlation was somewhat smaller than anxiety’s and depression’s positive correlations with cognitive distortions (rs = .51 and .59).Descriptive statistics and correlations between variables are presented in R2 = .38, F  = 21.48, p < .001, and the standardised coefficient was significant for both anxiety, β = .25, p = .04, and depression, β = .43, p < .001. However, adding attentional control did not significantly increase model fit, R2 change = .02, Fchange  = 2.81, p = .10. Based on the literature summarised in the Introduction, the lack of a relationship between attentional control and cognitive distortions is surprising, and theoretically important. We studied this further by repeating our analysis as a Bayesian linear regression, using JASP [01, which indicates how much more likely the observed data are if the tested effect is absent in the population, relative to if the tested effect is present in the population. The BF01 for adding attentional control to our model was 1.24, representing weak evidence that attentional control did not have a unique relationship with distortions. The same was true when cognitive distortions in the achievement context replaced total distortions as the outcome, R2 change = .01, Fchange  = 1.62, p = .21, BF01 = 2.25; when distortions in the social context as the outcome, attentional control marginally increased model fit, R2 change = .03, Fchange  = 3.51, p = .07, BF01 = 0.81, but upon closer inspection this result was due to a strong correlation between attentional control and one particular distortion, the use of should statements  = 2.35, p = .13, BF01 = 1.38. This suggests that, with the exception of should statements in the social context, the relationship between attentional control and cognitive distortions is accounted for by anxiety and depression.Given that both attentional control and cognitive distortions correlated with both anxiety and depression, we wanted to check whether attentional control still predicted cognitive distortions independently from the effects of these variables. Total cognitive distortions were regressed on anxiety and depression, with attentional control entered in a second block. The first block model significantly predicted distortions, ents see . When thR2 change = .0002, Fchange  = 0.02, p = .88, BF01 = 3.91. We then checked whether attentional control moderated depression’s relationship with distortions; it did not, R2 change = .001, Fchange  = 0.15, p = .70, BF01 = 4.11. Finally, we repeated both moderation analyses with both social and achievement distortions as outcomes, no significant interactions emerged . These results suggest that attentional control does not moderate the relationship between negative affect (anxiety or depression) and cognitive distortions.We next checked to see whether attentional control moderated anxiety or depression’s relationships with cognitive distortions: as described in the Introduction, attentional control tends to moderate negative affect’s relationship with attentional bias. Total distortions were regressed on mean-centred attentional control and anxiety, with their interaction term entered in a second block. This interaction term did not improve model fit, Attentional control was related to the expression of cognitive distortions, but this relationship became nonsignificant when anxiety and depression were controlled, suggesting the relationship was explained by negative affect. Attentional control also did not moderate anxiety or depression’s relationships with cognitive distortions. These findings suggest that weak attentional control, while related to negative affect, does not necessarily exaggerate the expression of all negative affect’s cognitive features. There seems to be some limit on the psychopathological sequelae of poor attentional control, which may limit its clinical usefulness as a risk factor for emotional disorders.The Bayes factors for the moderation analyses above indicate quite convincing evidence that attentional control does not moderate negative affect’s relationship with cognitive distortions. However, the Bayes factors for the relationship between attentional control and distortions when negative affect is controlled do not indicate convincing evidence. In other words, Study 1 cannot completely rule out the possibility that there may be a unique relationship between attentional control and cognitive distortions. This is probably an issue of sample size; in Study 2 we were able to collect a much larger sample, and found more convincing evidence that no such unique relationship exists.r = -.40), which is rather stronger than the correlation between this distortion and depression (r = .28). This finding was not replicated in Study 2’s UK sample, reported below; so it is unclear whether this finding reflects a simple Type I error, or whether Turkish students are more likely to show such a relationship than are UK students. Özdel et al. [One complicating issue is that attentional control seemed to show a unique relationship with one particular distortion, the use of should statements in the social context. This is due to an unusually strong correlation between attentional control and this distortion . AlthouThe University of Kent’s Psychology Ethics Committee approved the study (201815180106374922), and participants gave their consent electronically before beginning the study.Mage = 19.59, SDage = 3.02). Participants were told the study was about “thinking styles, and which personal characteristics affect habits of thinking.” They completed the scales online.Two hundred and nine undergraduates from the University of Kent participated for course credit. Ten participants were excluded from analyses as they reported a current psychiatric diagnosis other than an anxiety or affective disorder , or chose not to disclose their diagnoses. The final sample for analysis therefore included 199 students . This pAgain, we first checked for a relationship between attentional control and cognitive distortions, and then whether attentional control moderated anxiety’s or depressions’ relationship with cognitive distortions.Correlations are presented in R = .54, R2 = .30, F  = 41.27, p < .001; the standardised coefficient was marginal for BAIT  and significant for BDI . However, adding attentional control did not improve model fit, R2 change = .003, Fchange  = 0.92, p = .34, indicating that the relationship between attentional control and cognitive distortions is accounted for by anxiety and depression: there is no unique relationship between these variables. This analysis was repeated as a Bayesian regression as before, comparing the full model to a null model including only BAIT and BDI. The BF01 for adding attentional control was 4.14, indicating good evidence that there truly is no unique relationship between attentional control and cognitive distortions. The hierarchical regression analysis was repeated for cognitive distortions in both the social and achievement contexts, and then all three analyses were repeated with STAI trait anxiety in the model in place of the BAIT. Attentional control did not improve model fit in any of these analyses .Again, we investigated the relationship between attentional control and cognitive distortions using hierarchical regression. Total cognitive distortions were regressed on BAIT and BDI scores in a first block, and attentional control was added in a second block. The first block model significantly predicted cognitive distortions, R2 change = .006, Fchange  = 1.34, p = .25. Bayesian linear regression showed that the evidence for the interaction’s being absent was somewhat stronger than the evidence for it being present, BF01 = 2.81. These analyses were then repeated with STAI trait anxiety in the model instead of the BAIT; again, adding the interaction term did not improve model fit, R2 change = .004, Fchange  = 1.12, p = .29, and the evidence for the interaction’s absence was stronger than the evidence for its presence, BF01 = 3.97. There was a significant interaction between depression and attentional control on total distortions, R2 change = .02, Fchange  = 6.76, p = .01, but this did not resemble the interaction of negative affect and attentional control on attentional bias reported in the literature: simple slopes calculated for low (mean– 1 SD), medium (mean), and high (mean + 1 SD) levels of depression revealed that attentional control only predicted distortions when depression was low , and not when depression was medium  or high , where distortions were more uniformly high. Essentially, attentional control’s relationship with cognitive distortions was only visible when depression was low; this simply shows that attentional control’s relationship with distortions was rather weaker than depression’s relationship with distortions. It was not the case that distortions were strongest when depression was high and control was low, as found in the attentional bias studies reviewed in the Introduction.Again, moderated regression was used to check for any interaction between trait anxiety and attentional control on total distortions. The interaction term did not significantly improve model fit, Again, attentional control showed a significant relationship with cognitive distortions; however, this relationship seems to be reliably and wholly accounted for by negative affect. The correlations between attentional control and STAI trait anxiety, BAIT trait anxiety, and depression were all very similar, suggesting that it is negative affect in general, not anxiety or depression specifically, which accounts for the relationship between attentional control and cognitive distortions.Unlike Study 1, this study found evidence for an interaction between depression and attentional control on cognitive distortions. However, this interaction was very unlike the interaction reported in the literature between attentional control and negative affect on attentional bias to threat. Instead, we found that attentional control’s relationship with distortions was only visible when depression was low; this is not consistent with the idea that low attentional control increases the expression of affect-related cognitive distortions. Rather, both these studies suggest that weak attentional control does not necessarily exaggerate all cognitive features of negative affect.In two studies, attentional control significantly predicted cognitive distortions, a key symptom–and presumably, a key cause–of negative affect and emotional disorders. However, more careful analyses showed that this relationship was essentially an artefact, resulting from the fact that both control and distortions correlate with negative affect .This could happen for two reasons. One possibility is that both attentional control and cognitive distortions are both independently related to negative affect, but not to each other. The other possibility is that people high in negative affect might have a response bias, so that they tend to respond in a negative way on any scale concerning their own performance or abilities, including both the ACS and the cognitive distortions scale; this could be considered a kind of common method bias , whsee also . Howeversee also . These rsee also , 14, it f2 = .03 Click here for additional data file.


Adsorption of Li and Na on pristine and defective graphene and graphene oxide (GO) is studied using density functional theory (DFT) structural and electronic calculations, quantum theory of atoms in molecules (QTAIM), and electron localization function (ELF) analyses. DFT calculations show that Li and Na adsorptions on pristine graphene are not stable at all metal coverages examined here. However, the presence of defects on graphene support stabilizes both Li and Na adsorptions. Increased Li and Na coverages cause metal nucleation and weaken adsorption. Defective graphene is associated with the presence of band gaps and, thus, Li and Na adsorptions can be used to tune these gaps. Electronic calculations show that Li– and Na–graphene interactions are Coulombic: as Li and Na coverages increase, the metal valences partially hybridize with the graphene bands and weaken metal–graphene support interactions. However, for Li adsorption on single vacancy graphene, QTAIM, ELF, and overlap populations calculations show that the Li-C bond has some covalent character. The Li and Na adsorptions on GO are significantly stronger than on graphene and strengthen upon increased coverages. This is due to Li and Na forming bonds with both carbon and oxygen GO atoms. QTAIM and ELF are used to analyze the metal–C and metal–metal bonds . The Li and Na clusters may contain both covalent and metallic intra metal–metal bonds: This effect is related to the adsorption support selection. ELF bifurcation diagrams show individual metal–C and metal–metal interactions, as Li and Na are adsorbed on graphene and GO, at the metal coverages examined here. Trations) . Currentrations) . The userations) ,9, C60, ectively . Graphenectively ,12,13. Gectively .Increased LIB graphene capacities relative to graphite seem indicative of increased Li atoms being adsorbed on graphene relative to graphite. However, the contrary has been observed via in situ Raman spectroscopy . Althoug6 clusters collapse during adsorption on 24 vacancies defective graphene [The main disadvantage of LIBs is Li cost, which has been rising steadily in recent years, with the exception of a price drop observed in the last quarter of 2018 . Li is agraphene .64 formation [+ size relative to Li+ (100 pm vs. 76 pm) and Na high ionization potential [Sodium ion batteries could serve as alternative batteries due to the abundance of Na. However, the NIB capacity of graphite is only 35 mAh/g, substantially lower relative to LIB . This isormation ,35 due totential . NIB capotential . DFT calotential . Specifiotential .adsE and cE, respectively) per metal, the metal–Carbon plane distances , and the electronic charge transfers from the metals to graphene and GO supports. The adsE and the n = 1, 2, …) on graphene and GO are defined asadsE is the minimum energy required to remove a single Li or Na atom during adsorption on graphene and GO, whereas cE measures the cluster-support interaction. For n = 1, adsE = cE. Single metals and their clusters form a bond with the support if cE < 0. The metal-support interaction is considered stable if the |adsE| is larger than the metal binding energy of its bulk. The metal-support interactions are also examined via electronic structure analyses.In this work, we examine Li and Na adsorptions on pristine and defective graphene, as well as on graphene oxide (GO), at various metal coverages, using periodic DFT, with and without long-range interactions. The metal-support interactions are elucidated the via changes in the adsorption and cohesive energies  and the electron localization function (ELF). QTAIM was developed by Bader and co-workers, who note that orbitals, much less wave functions, are unphysical in nature; thus, chemical bonds are not quantum observables ,39. Howe systems . ELF [41 systems ,43.n and Nan  adsorbed on pristine graphene, the Li and Na cE values, the heights h for the closest metal to the graphene plane, and the smaller of the metal–metal distances per configuration. The Lin/graphene and Nan/graphene  unit cells are built as follows: for n = 1, single metal atoms  are initially placed distant from graphene, followed by geometry optimizations; for n = 3, two metal atoms are placed distant from the previously optimized Li/graphene and Na/graphene structures, followed by geometry optimizations. A similar approach is followed for the Li5/graphene and Na5/graphene cases. n and Nan  adsorbed on single and double vacancy defective graphene, as well as the cE and geometrical parameters, in a similar fashion as in n and Nan  adsorbed on graphene oxide , with cE and geometrical parameters. Here, GO supports contain epoxides and hydroxyls at 25% and 18.75%, respectively (percentages relative to C). Our GO structure is in agreement with the proposed non-hydrated GO structure from Zhou and Bongiorno [Monolayer pristine graphene is modeled as a two-dimensional 4 ongiorno  . A large integration grid is used: this is a pruned grid with 75 radial and 974 angular points. The Crystal Orbital Overlap Populations (COOP) [For geometry optimizations, Brillouin zone integrations (Monkhorst-Pack grid) , the Ferc mixing , coupleds (COOP) , for sels (COOP)  and the s (COOP) , the lats (COOP) ,39. Hirss (COOP)  and smals (COOP) . The abss (COOP) , and thuadsE and cE calculations suffer from basis set superposition errors (BSSE) [adsE and cE values. Calculated adsE and cE values using the counterpoise correction are less negative in energy than corresponding uncorrected values [The stabilities of the final geometry conformations are secured via post-geometry optimizations. When Gaussian basis sets are used, s (BSSE) . The BSSd values ,49,67.The  program , which i density . Specifiectively ,71. Gattectively . Therefo2) serves as an additional criterion to classify non-polar covalent and metallic bonds [Ionic, covalent, and metallic bonds are classified via changes in nts r→cp . Ionic bp values . For metic bonds ,74. For nt bonds .The electron localization function (ELF) is an alternative to QTAIM for analyzing chemical bonding ,75,76,77 0≤ηr→≤1 . The ηr→vy atoms . Kohout Li to Sr . Savin eLi to Sr  interpreg basins . In QTAIins [ρr→ . Basins ins [ρr→ ). Only cdiagrams , with thg basins . The ELFg basins .3, Li5, Na3, and Na5, cE values is small. However, disagreements between the DFT calculations with and without D2 corrections are observed in the h values and the metal–metal closest distances  coverage, Li and Na are adsorbed on the hollow site, in agreement with past reports ,17,18,22ulations b,c. ThesadsE for Li and Na adsorptions on graphene and GO. At the same metal coverages, Li adsorption on pristine graphene is significantly stronger relative to Na, as shown by adsE, cE, and h values. This statement is extended for adsorption on defective graphene and GO, with the exception of Na3/GO calculations without D2 corrections (vide infra). Our Li and Na |adsE| values are smaller than the binding energies of bulk Li  and Na 63 eV/Li ): These 63 eV/Li  and Lian63 eV/Li . Althougguration , where ig energy . For adsexpected ,37. ThisadsE| values at all coverages are larger than the binding energies of their metallic bulk, in agreement with past reports [cE| from 3.44 eV to 2.14 eV. At 3/32 ML coverage, the presence of a single C vacancy causes the Li clusters to collapse, whereas in all other cases Li and Na form clusters, in a similar fashion as during adsorption on pristine graphene.Li and Na adsorptions on defective graphene are stable . Here, L reports ,37. The The strong adsorption of Na on defective graphene  is indicative of its possible use in NIB. Although, Na adsorption on graphene is not as strong as Li, its abundancy is a significant advantage over Li.3/GO geometry calculated without D2 corrections. 3/GO with and without D2 corrections. Therefore, for Na adsorption on GO, at coverages > 1/32 ML, it is important to include long-range interactions due to Na atoms’ large polarizations [3/GO, Na clustering is observed, in contrast to the Li3 case.Li and Na adsorptions on GO are also stable . Moreoveizations . For Na3cE| and, thus, the weakening of the metal-support interaction.Dobrota et al. showed that Na adsorption on graphene-OH compounds leads to Na being neutral . This is3 group [The metal–C bond strengths can be estimated via changes in their COOP values. The majority of the metal–C COOP values in 3 group . Our COO3 group . TherefoDE, DOS(DE) = 0) below the Fermi energy  and conduction bands (π*) meet in a linear dispersion fashion [DE downshift, due to Li and Na adsorptions on pristine graphene, is indicative of n-doped graphene support via charge transfer from Li and Na. At low Li and Na coverages, the DE is intact  and thus, partially occupied. The partial occupancy of these states decreases the Coulombic interaction between the metals and pristine graphene, as the metal coverage increases. This statement also applies for adsorption on defective graphene.At higher Li coverages, the s broken e,f, whercoverage f. HoweveD region f, turninDE above the Fermi energy and create band gaps. For example, the double vacancy graphene is a semiconductor [DE upshift is indicative of a p-doped graphene support [3 adsorbed on double vacancy graphene, the DE appears above the Fermi energy  . This vaED . Li and ED . The GO ion-VSCC ) and  point . In all cases here, with the exception of Li adsorbed on single vacancy graphene, QTAIM shows that metal–C bonds are ionic. Recall that for ionic bonding a et al. . Here n . The For Li/graphene, the bifurcation diagram reveals the Li and C core basins (C(Li) and C(C)) and the polysynaptic valence basin V a. For Nao et al. , who stugraphene . At highgraphene b and polgraphene c.At low metal coverages and for adsorption on single vacancy graphene, the first bifurcation point occurs at higher d region a. COOP, 3 clusters collapse during adsorption on single vacancy graphene and GO. The adsE calculations show that Li and Na adsorption on pristine graphene is unstable at all the metal coverages examined here, whereas the presence of defects in the graphene support stabilizes Li and Na adsorptions. Increased metal coverage weakens metal-graphene and strengthen metal-GO interactions. Adsorption on GO is strong due to simultaneous metal bonding with C and O GO atoms.Li and Na adsorptions on pristine and defective graphene and GO has been studied using periodic DFT structural and electronic calculations paired with COOP, QTAIM, and ELF analyzes. DFT optimized geometries show that increased metal coverage clusters Li and Na during adsorption on graphene, which lowers the efficiency of LIB and NIB. However, LiDE is pushed below the Fermi energy due to Li and Na adsorptions on pristine graphene and may be associated with small band gaps at higher metal coverages. The presence of defects on graphene is associated with DE shifting and bang gap openings.DOS calculations show that, at low metal coverages, Li and Na adsorptions on pristine does not affect the DOS spectra below the Fermi energy. This effect is indicative of Coulombic interactions between the adsorbates and the support. These interactions weaken as metal coverages increase, due to the downshift of the metal valances below the Fermi energy. The band calculations show that the The metal–C bonds are analyzed using COOP, QTAIM, and ELF. The COOP metal–C COOP values at the order expected for ionic bonding, for all cases except for adsorption on single vacancy graphene, at low metal coverages. QTAIM shows that only the Li-C bond contains covalent character for adsorption on single vacancy graphene, whereas in all other cases the metal–C bond is ionic. ELF partially agrees with QTAIM and shows that both Li-C and Na-C bonds are covalent for adsorption on single vacancy graphene. QTAIM and EFT are also used to study Li-Li and Na-Na bonds: Li and Na clusters may contain metallic and covalent metal–metal bonds depending on adsorption support .


Tribolium castaneum, while it is absent in honeybees, carpenter ants, and vinegar flies. In our study, we focus on cell proliferation in the adult mushroom bodies of T. castaneum. We reliably labelled the progenies of the adult persisting mushroom body neuroblasts and determined the proliferation rate under several olfactory conditions within the first week after adult eclosion. We found at least two phases of Kenyon cell proliferation in the early adult beetle. Our results suggest that the generation of Kenyon cells during the first three days after adult eclosion is mainly genetically predetermined and a continuation of the developmental processes (nature), whereas from day four on proliferation seems to be mainly dependent on the odour environment (nurture). Considering that the mushroom bodies are linked to learning and memory, neurogenesis in the mushroom bodies is part of the remodelling of neuronal circuits leading to the adaption to the environment and optimization of behaviour.Several studies showed adult persisting neurogenesis in insects, including the red flour beetle  Following development, when the nervous system first encounters environmental sensory input, it is crucial for the survival of an animal to adapt to the actual conditions and cues. Therefore, the genetically encoded wiring scheme of the nervous system must be altered. This plasticity typically occurs in special time windows of elevated susceptibility to sensory input 10 and song learning in birds13. However, several studies demonstrated the existence of critical periods also in insects19, with many focusing on the plasticity of the olfactory system in the vinegar fly Drosophila melanogaster22.The research on critical periods has mainly focused on vertebrates, where critical periods were among others found during the postnatal development of the visual system26. From there the information is relayed into the primary olfactory processing centres29 and further projected to the mushroom bodies (MB) and the lateral horn30, with the former being linked to olfactory learning and memory45.Many insects rely on their olfactory systems to accomplish various tasks, such as the localization of food sources, mating partners, and suitable habitats. The adult olfactory system of holometabolous insects is first confronted with environmental cues upon adult eclosion. These olfactory cues are mainly detected by the olfactory sensory neurons (OSN) housed in the chemosensory sensilla of the antennae and palpsApis mellifera and D. melanogaster, described synaptic plasticity, but no neurogenesis, in the mushroom bodies55. Since adult neurogenesis in the mushroom bodies was first described in the hemimetabolous house cricket Acheta domestica57, it has also been found in holometabolous insects, such as the noctuid moth Agrotis ipsilon58, as well as several beetle species59 including the red flour beetle Tribolium castaneum60.Studies in adult holometabolous insects, including T. castaneum is a member of the largest insect order (Coleoptera)62 and a major pest of stored cereal products64. With its annotated genome sequence67, an expanding genetic toolbox70 including strong systemic RNA interference (RNAi)73, and its relative longevity of up to two years74, T. castaneum represents an eligible beetle model for studying adult plasticity.T. castaneum26, to answer whether the adult persisting neurogenesis is genetically predetermined and continuation of developmental processes or if it is activity-dependent.We concentrated on olfaction which is supposed to be a main input into the mushroom bodies of 76. With this method, we reliably and exclusively labelled the mushroom body neuroblasts and their progeny, while neurogenesis is not present in other areas of the cerebral ganglion of the red flour beetle  methodtle Fig. . The neutle Fig. .Figure 1T. castaneum, adult-born Kenyon cells usually derive from two neuroblasts (NB) per hemisphere and send their axons into the core of the mushroom body peduncle (PED) and lobes as shown by Zhao and colleagues60  dependent behavioural response81.For enrichment of the olfactory environment we chose the beetles’ aggregation pheromone 4,8-dimethyldecanal (DMD)The mushroom body neuroblasts of beetles reared in mixed-sex groups of 20 individuals generate new Kenyon cells within the first four days after adult eclosion. During the first three days of the proliferation phase, the median daily proliferation rate of each neuroblast is 10 to 15 cells, with a reduction on day four Figs. . At day Isolating beetles as pupae lead to a proliferation phase lasting three days after adult eclosion. During this proliferation phase, each neuroblast gives rise to about 10 to 20 cells per day, decreasing on day three and significantly dropping at day four before from day five onwards no neurogenesis is detectable.Enriching the environment of group-reared beetles with DMD leads to a prolonged proliferation phase of five days. During the first three days of the proliferation phase, the median daily proliferation rate of each neuroblast equals to about 15 to 20 cells, with slight decrease on days four and five. At day 6 the proliferation rate drops significantly and afterwards no neurogenesis is detectable.orco injection) group-reared beetles with DMD leads to a proliferation phase lasting four days after adult eclosion. During the first four days each neuroblast’s median daily proliferation rate is 10 to 15 cells, with a high variation on day four  group-reared beetles with cis-3-hexenol leads to a proliferation phase lasting four days after adult eclosion. During the first three days of the proliferation phase, the daily proliferation rate per is 10 to about 15 cells, with a significant reduction on day four  differ in their response to the tested odours, compared to beetles injected with sham (DsRed) dsRNA .Electroantennographical recordings (EAG) were used to demonstrate that beetles with an RNAi-mediated systemic knockdown of Orco (dsRNA−8 − 10−6) of the beetle’s aggregation pheromone DMD leads to no obvious reaction of the antennae  of the food volatile cis-3-hexenol leads to no obvious responses. Stimulating with a dilution of 10−3 causes a slight response in the control group only. Whereas, at the highest tested concentration (10−2) both groups respond  as published for the red flour beetle by Zhao and colleaguessts Fig.  we studins Figs.  to answe79 prolongs the proliferation phase by one day, while stimulation with the food odour cis-3-hexenol leads to a prolongation of the proliferation phase for at least four days. This prolonging effect is mostly inhibited in beetles with a significantly reduced perception of the tested odorants , generated by RNAi mediated knockdown of orco.Rearing beetles in groups of 20 animals of both sexes leads to the generation of new Kenyon cells during the first four days after adult eclosion. This phase is shortened in isolated beetles by one day and prolonged by additional olfactory stimulation. Enriching the olfactory environment of group-reared beetles with the beetles’ aggregation pheromone DMD82. A second study based on these results asked what proportion of neurogenesis is caused by visual and olfactory stimuli83 and demonstrated that olfactory and visual deprivation is sufficient to decrease neurogenesis in adult crickets, regardless whether reared in groups or isolation. This clearly showed a link between sensory input and adult neurogenesis.A study in crickets already demonstrated that sensory deprivation by isolation results in significantly less neurogenesis among the Kenyon cells of young females, compared to group-reared femalesIn our study, we used a day-to-day analysis of adult neurogenesis together with the specific manipulation of the Orco dependent olfactory sensory neurons. This allowed us to temporally dissect adult neurogenesis into distinct phases and link them to olfactory activity.82, our data suggest that isolation reduces adult neurogenesis. We were able to demonstrate that this reduction is based on a shortened proliferation phase rather than an altered proliferation rate.Similarly to the results from cricketsIt seems that the generation of Kenyon cells during the first three days after adult eclosion is mainly genetically predetermined and a continuation of developmental processes rather than depending on sensory activity.orco knock-down in odour stimulated beetles is still longer than in isolated beetles. Since the beetles were kept under constant darkness, visual stimulation can be excluded, making tactile and gustatory cues the most likely triggers. This is partially in accordance with findings in crickets, were unilateral removal of the antennae, causing the loss of chemosensory as well as mechanosensory antennal input in the ipsilateral hemisphere, lead to less adult neurogenesis in the ipsilateral hemisphere83.The shorter proliferation phase in isolated beetles compared to group-reared beetles suggest an influence of social interactions. This is supported by the fact that the proliferation phase after The significant differences in the proliferation rate within the first three days might indicate an influence of odour stimulation. However, as the results for DMD and cis-3-hexenol are contradicting, we conclude that these differences are very likely artificial.orco injected beetles. Since after a RNAi mediated knockdown of orco the EAG response towards DMD is significantly reduced 26.This longer proliferation phase is massively shortened in dsRNAion Fig. , which s95. This could mean that continued exposure to high DMD concentration leads to a desensitisation of the OR/Orco complex, causing the shorter proliferation phase. Whereas, the response to cis-3-hexenol seemingly also perceived by IRs persists longer. Furthermore, there is the possibility of different pheromone vs. normal odour processing networks in the antennal lobes that provide the input to the MB as shown among others in Manduca sexta96, D. melanogaster97, and A. mellifera98. However, so far, such separate olfactory processing networks were not described in the red flour beetle and it remains unclear whether they exist.The different effects of DMD and cis-3-hexenol on the duration of the proliferation phase (five vs. eight days) might be explained already on the receptor level, as IRs, unlike ORs, do not exhibit adaptation to longer stimulationsorco injection, corresponding roughly to A4. Besides, we performed immunohistochemical staining against Orco in freshly eclosed (A0) and seven days old beetles (A7) using the cross-reactive Moth-R2 antiserum26. This resulted in no immunoreactivity after pupal injection of dsRNAorco  of the wild-type strains “San Bernadino”107 and “black”108, as well as the neuron labelling77 transgenic line EF1-B-DsRed  were used. The beetles were bred under constant darkness at about 28 °C and 40–50% relative humidity on organic whole grain wheat flour supplemented with 5% dried yeast powder and 0.05% Fumagilin-B  to prevent sporozoan infections109. For age determination, freshly eclosed beetles (A0) were collected and kept in mixed-sex groups of 20 in 68 ml Drosophila vials on about 20 g substrate.Red flour beetles . Injection was performed with glass micropipettes made from thin-walled glass capillaries  attached to a pressure ejection system . After injection, the beetles were transferred into a para-film sealed petri dish and incubated at 28 °C for 24 hours.Cold anaesthetized beetles were mounted ventral-up on microscope slides using double-faced adhesive tape and were subsequently injected with about 1 µl 5-ethynyl-2′-desoxyuridine (EdU) solution  and fixed in 0.01 M PBS containing 4% paraformaldehyde for 1–2 hours at room temperature or at 4 °C overnight. Fixation was stopped by rinsing 4 × 10 min in PBS supplemented with 0.3% Triton X-100 (PBS-TrX). Afterwards, the brains were transferred into a blocking solution  in PBS-TrX) and incubated for 3–4 hours at room temperature or overnight at 4 °C. The brains were then incubated with primary antibody solution  or 1 µl of the beetles aggregation pheromone 4,8-dimethyldecanal79 in a dilution of 1:1000 in silicone oil , on filter paper (1 cm²).For enhanced olfactory stimulation we reared 20 freshly eclosed adults in mixed-sex groups in para-film sealed 68 ml Orco dsRNA (dsRNAorco) and DsRed dsRNA (dsRNAsham) used as a control were synthesized as previously published26. Both dsRNAs were injected into pupae at about 30%–40% of total metamorphosis. The Orco knockdown was verified by immunohistochemistry against Orco  on cryo-sections of antennae as previously published26  and a confocal laser scanning microscope . Confocal image stacks were analysed with Amira 6 graphics software .For further processing, snapshots of single sections and projections generated in Amira were processed  in Adobe Photoshop CC . Final figure arrangements were performed in Adobe Illustrator CC.80. The tests were performed using five female beetles aged seven days after dsRNA injection, corresponding to about A4, with three repeated measurements per animal. The antennal response was recorded at 25 Hz via a custom-build amplifier attached to a data acquisition controller  using EAG 2000 software (Syntech). During the EAG recordings, the antennae were exposed to a constant flow (~3 l/min) of filtered and humidified air. The green leaf volatile cis-3-hexenol and the beetle’s aggregation pheromone DMD111 were used as test odours. They were presented as 1 s pulse via a stimulus controller  that looped in the odour diluted in silicone oil from a filter paper impregnated with 20 µl odour sample. Measurements were performed in minute-long intervals with increasing concentrations (10−8–10−2). Each repeat was preceded by the measurement of the reaction to DMD (positive control) and silicone oil, which was used as solvent)For electronantennographical recordings, we followed a previously published protocolwww.python.org) based custom scripts. Those scripts utilize the SciPy ecosystems (www.scipy.org) modules SciPy (version 1.2.1)112, Numpy (version 1.16.2)113, Matplotlib (version 3.0.3)114, and Pandas (version 0.24.2)115, as well as the additional modules scikit-posthocs 116, Seaborn (version 0.9.0)117, and localreg .Analysis of cell numbers and EAG responses including statistical comparison and plotting was performed using Python , Kruskal-Wallis test from SciPy and posthoc analysis by Dunn’s multiple comparison from scikit-posthocs with p-value correction using the Holm method119 were used.To test for significant differences between experimental groups, Scheirer-Ray-HareRaw EAG voltage trains exported from EAG 2000 were initially smoothed using robust LOESS  method from localreg to account for local spikes and subsequently normalized by subtracting the corresponding responses to silicone oil which was used as the solvent to dilute the test odours.Figures were plotted as vector graphics and final figure arrangements were performed in Adobe Illustrator CC.All experiments involving animals were performed in compliance with the guidelines of the European Union (Directive 2010/63/EU). As all experiments were on insects an approval of the study by an ethics committee was unnecessary.Supplementary information.Dataset 1.Dataset 2.


Supramolecular hydrogels, obtained from small organic molecules, may be advantageous over polymeric ones for several applications, because these materials have some peculiar properties that differentiate them from the traditional polymeric hydrogels, such as elasticity, thixotropy, self-healing propensity, and biocompatibility. We report here the preparation of strong supramolecular pseudopeptide-based hydrogels that owe their strength to the introduction of graphene in the gelling mixture. These materials proved to be strong, stable, thermoreversible and elastic. The concentration of the gelator, the degree of graphene doping, and the nature of the trigger are crucial to get hydrogels with the desired properties, where a high storage modulus coexists with a good thixotropic behavior. Finally, NIH-3T3 cells were used to evaluate the cell response to the presence of the most promising hydrogels. The hydrogels biocompatibility remains good, if a small degree of graphene doping is introduced. Hydrogels have been extensively explored as promising biomaterials for biosensors, tissue engineering, and drug delivery systems ,2,3,4, bPeptide-based hydrogels ,14,15,16A strategy that has emerged for improving the functionality of supramolecular hydrogels is the incorporation of nanofillers ,21,22,23Recently, incorporation of graphene-based nanofillers in hydrogels has been used to tailor their properties ,39,40,41d-Oxd or d-pGlu moiety  in their skeleton [We recently reported several pseudopeptides that freeze water in low concentration and form hydrogels that possess high mechanical strength and transparency ,48,49. Askeleton ,51 that skeleton ,53 that skeleton ,55,56,57skeleton ,59, pH vskeleton ,61, enzyskeleton ,63, dissskeleton , and ultskeleton ,66. d-Oxd-l-Phe-CO(CH2)7CO-l-Phe-d-Oxd-OH A possesses two l-Phe-d-Oxd  dipeptide units coupled with an azelaic acid unit , and the hydrogels mechanical properties were tested and compared. The hydrogels biocompatibility was also tested.In this work, the pseudopeptide A (in 1% or in 2% w/w concentration) and an increasing amount of graphene in MilliQ water  and loss moduli (G″). All the analyzed hydrogels are characterized by a “solid-like” behavior, i.e., the storage modulus is approximately an order of magnitude higher than the loss component  that is compatible with cellular environment. Thus, we further analyzed all the hydrogels, with a particular interest in the properties of hydrogels 13–16.Considering the mechanical properties and the final pH of the 16 samples, we can gather that the most promising hydrogels for applications as biocompatible materials are hydrogels 1–16, multiple cycles composed of three steps were applied to the gels. During the first step, the sample was subjected to a strain value within the LVE region and was characterized by G’ values greater than G’’. When the applied strain was increased above the crossover point, the sample behavior switched from gel-like to sol-like, with G’’ values greater than G’. Finally, the sample was leaved at fixed strain within the LVE range to check the recovery of the gel-like behavior.Self-healing properties ,71,72,7313–16 are characterized by a great capability to recover the gel-like behavior and confirm at the molecular level their thixotropic properties, as they all fully recover the “solid-like” behavior when the strain level goes back within the LVE region , a significant toxic effect on fibroblasts is observed. However, after 48 h the cells continue to proliferate both in hydrogel 13 (without graphene) and in hydrogel 14–16, insensitive to the graphene doping concentration. This mean that the scaffold support cell growth. Supramolecular hydrogel, doped with graphene, may represent a promising building block for the development of smart materials for biomolecules and tissue engineering purposes. The degradation of the gel does not modify the graphene toxicity. We measured the cell viability after the destruction of the matrix, and the results, in Cell adhesion is strongly influenced by graphene doping. Hydrogel All chemicals and solvents were purchased from Sigma-Aldrich , VWR , or Iris Biotech GMBH  and used as received. Acetonitrile was distilled under inert atmosphere before use. MilliQ water  was used throughout. Solvent were dried by distillation before use. All reactions were carried out in dried glassware.d-Thr, l-Phe, and azelaic acid following a multistep procedure in solution, reported in [The compound was synthesized from orted in ,662 according to [Three dispersions of MilliQ water (20 mL each) containing edge-functionalized graphene,  were prepared and vigorously stirred for ~20 min with a magnetic stirrer. The graphene sample, produced by 24 h ball-milling in the presence of COrding to , was kinrding to .A  was placed in a test tube (diameter: 8 mm), then MilliQ water (0.5 mL) or a graphene solution  was placed in a test tube (diameter: 8 mm), then arginine  was added. 0.5 mL of MilliQ water or a graphene solution (see A portion of gelator gel was determined by heating some test tubes (diameter: 8 mm) containing the gel and a glass ball  on the top of it. When the gel is formed, the ball is suspended atop. The Tgel is the temperatures in which the ball starts to penetrate inside the gel. Some hydrogel samples melt, producing a clear solution, whereas in other cases, the gelator shrinks and water is ejected, as syneresis occurs.T1–16 were freeze-dried using a BENCHTOP Freeze Dry System LABCONCO 7740030  with the following procedure; the hydrogel was prepared into an Eppendorf test tube at room temperature. After 16 h, the samples were deepened in liquid nitrogen for 10 min, then freeze-dried for 24 h in vacuo (0.2 mBar) at −50 °C.Samples of hydrogels Scanning electron micrographs of the samples were recorded using a Hitachi 6400 field emission gun scanning electron microscope operating at 15 kV .−1. Once the LVE of each hydrogel was established, frequency sweep tests were performed (ω: 0.1–100 rad s−1) at a constant strain within the LVE region of each sample (γ = 0.04%). Step strain experiments were performed on hydrogels to analyze the thixotropic behavior of the material. The sample was subjected to consecutive deformation and recovery steps. The recovery step was performed by keeping the sample at a constant strain γ = 0.04%, i.e., within the LVE region, for a period of 400 s. The deformation step was performed by applying to the gel a constant strain γ = 100%, i.e., above the LVE region of the sample for a period of 300 s. The cycles were performed 3 times at a fixed frequency ω = 10 rad s−1.Rheology experiments were carried out on an Anton Paar Rheometer MCR 102  using a parallel plate configuration (25 mm diameter). Experiments were performed at constant temperature of 23 °C controlled by the integrated Peltier system and a Julabo AWC100 cooling system. Oscillatory amplitude sweep experiments (γ: 0.01–100%) were carried out to determine the linear viscoelastic (LVE) range at a fixed frequency of 1 rad sHigh quality infrared spectra (64 scans) were obtained with an ATR-FT-IR Bruker Alpha System spectrometer (64 scans) .−1 glucose), supplemented with 10% (v/v) Fetal Bovine Serum, 2 mM l-glutamine, 0.1 mM MEM Nonessential Amino Acids (NEAA), 100 U mL−1 penicillin, and 100 U mL−1 streptomycin in a humidified incubator set at 37 °C with 5% CO2. For cell culture, the samples were sterilized with ultraviolet (UV) radiation for 20 min before use. The gels were incubated for 30 min at 37 °C with 5% CO2 with complete medium. Cells were seeded on gel in 24-well plates at a density of 105 cells per mL.Mouse embryonic fibroblast (NIH-3T3) cells were cultured under standard conditions in Dulbecco’s modified Eagle’s medium (DMEM) . We included a negative control of only medium without cells to determine the background signal and a positive control of 100% reduced resazurin reagent without cells.Cell viability was determined by resazurin reduction assay; the reagent is an oxidized form of the redox indicator that is blue in color and nonfluorescent. When incubated with viable cells, the reagent is reduced and it changes its color from blue to red becoming fluorescent. Briefly, cells were seeded on gels with complete medium. After incubation times, the resazurin reagent was added directly to the culture medium with 10% volume of medium contained in each sample and incubated for 4 h at 37 °C with 5% CO1–8  proved to be very strong but quite rigid, as testified by the analysis of the values of storage moduli and loss moduli variation during step strain experiments. In contrast, hydrogels 13–16  are very strong. SEM analysis of the corresponding aerogels demonstrates that the intimate structure of these hydrogels is formed by fibrous networks lacking in the other aerogels, which are characterized by locally oriented long strips that cross on the large scale. We also tested the biocompatibility of the most promising hydrogels 13–16 by assessing the bioavailability of mouse embryonic fibroblast (NIH-3T3) cells in the hydrogels, where pure water was replaced with complete medium serum. Cell adhesion on the hydrogel is strongly influenced by graphene doping. The hydrogel scaffold support cell growth at all the concentration of graphene doping.We report here the preparation of strong supramolecular hydrogels that owe their peculiar properties to the introduction of graphene in the gelling mixture. These materials are strong, stable, thermoreversible, and elastic. The concentration of the gelator, the degree of graphene doping and the nature of the trigger are crucial to get hydrogels with the desired properties, where a high storage modulus coexists with good self-healing properties. Indeed, the hydrogels In conclusion, in this paper, we demonstrated that the rheological properties and the biocompatibility of these graphene-hybrid hydrogels may be easily tuned selecting (i) the degree of the graphene doping and (ii) the nature of the gelator in the gelling mixture.


With the application of a recently developed deposition method called initiated chemical vapor deposition (iCVD), responsive hydrogel thin films in the order of a few hundred nanometers were created. When in contact with humid air, the hydrogel layer increases its thickness considerably. The measurement of the thickness change was realized interferometrically with a laser and a broadband light source in two different implementations. The relative change in thickness with respect to humidity can be described with the Flory–Huggins theory. The required Flory–Huggins interaction parameter was determined for the actual hydrogel composition. The setup was designed without electric components in the vicinity of the active sensor layer and is therefore applicable in harsh environments such as explosive or corrosive ones. The implemented sensor prototype delivered reproducible relative humidity ( Water is involved in many processes in life and technology and plays an important role in the climate system. The term moisture refers to the amount of liquid water in a gas in the form of small droplets, whereas by humidity the amount of gaseous water is meant ,2. HumidDevices for measuring humidity are called hygrometers, which quantify the amount of water vapor in the air. There are several principles ranging from ancient methods based on the elongation of a hygroscopic material , measuring the temperature difference between a dry and wet bulb (psychrometer) to optical detection of the condensation on a chilled surface (dew point hygrometer) ,6. A verof water . Since tNew materials and measurement techniques are under investigation for the development of novel humidity sensors. A promising class of materials are hydrophilic polymer networks that can absorb water in their meshes, the so-called hydrogels. Electronic properties of these hydrogels are related to the humidity of the surrounding atmosphere , but theThe correlation of the relative thickness change and the relative humidity was used ,15. The Developing a new sensor principle involves contributions from different fields of science. Certainly an important part is the hydrogel itself and its deposition, which is well-explored in the field of polymer, surface and thin-film physics ,18,19,20Variation of Equation  12]. from a hot wire suspended above the substrate. The created radicals react with the vinyl bonds of the monomers, which are vaporized and fed into the vacuum chamber. The substrate is kept at temperatures between 10 and 60 ications ,17.In the present study, the chosen deposition conditions were the following. HEMA was heated up to 75 r et al. . With thr et al. . The refl. [1.44 , a transThe optical detection was realized in two implementations, which are both based on interference phenomena. The first implementation consists of a laser and a photo detector and is able to measure relative thickness changes with a time resolution of The optical path and the principal detection setup are sketched in Equation .(2)Ir=|Equation  using thIf the wavelength Equation  leads toThe experimental setup was implemented with the substrate mounted on the sidewall of a measurement chamber shown in In another implementation of the optical setup, the hydrogel was deposited onto the tip of a fiber probe, which was illuminated and detected with the same components used for the tabletop solution connected with a fiber beam splitter. This setup was more robust in terms of optical misalignment and therefore easy applicable for future field measurements. The optical path can be described with the same model a and an The basic outcomes of the performed measurements were different intensities and raw spectra, which vary with the hydrogel thickness depending on the humidity in the chamber. To evaluate the model shown in The total increase of the hydrogel thickness can be observed in Equation ), which The Flory–Huggins theory was evaluated over a humidity range from 3% RH to 97% RH. Thus, the data from the spectral reflectance measurements were used for this evaluation. The initial thickness Equation ) was derEquation  for the  solvent . The sma process . Such cr% to 90% ,24. In p% to 90% . Since tBy applying the derived Equation , the corWith the approval of the Flory–Huggins model function and the determination of the corresponding interaction parameter surement a as wellsurement b. The laThe response time is commonly defined as the  sensors .The response times of the hydrogel sensor are much shorter than expected from previous experiments . It was In the presented work, hydrogel thin films produced by initiated chemical vapor deposition were characterized and their capability as humidity sensor was investigated. The theory underlying the sensor by Flory–Huggins was evaluated and the corresponding interaction parameter


The aim of the study was to present the possibility of the sensitivity improvement of the electronic nose (e-nose) and to summarize the detection mechanisms of trace gas concentrations. Our main area of interest is graphene, however, for the better understanding of the sensing mechanisms, it is crucial to review other sensors of similar functions. On the basis of our previous research, we explained the detection mechanism which may stay behind the graphene sensor’s sensitivity improvement. We proposed a qualitative interpretation of detection mechanisms in graphene based on the theory regarding the influence of metals and substituents on the electronic systems of carbon rings and heterocyclic aromatic ligands. The analysis of detection mechanisms suggests that an increase of the electronic density in graphene by attaching a substituent and stabilization of electronic charge distribution leads to the increase of graphene sensor conductivity. The complexation of porphyrins with selected metals stabilizes the electronic system and increases the sensitivity and selectivity of porphyrin-based sensors. Our research summary and proposed conclusions allow us to better understand the mechanisms of a radical change of graphene conductivity in the presence of trace amounts of various gases. Molecular mechanisms of detection of trace amounts of gases have been studied for many years. Moreover, different analyses and data interpretation methods have been developed over the past decades. These achievements allowed us to get more detailed insights into the concept of electronic nose (e-nose) which represents a method that is complementary to the commonly used GC-MS technique (gas chromatography combined with mass spectrometry). In contrast to GC-MS, the use of the e-nose does not allow for direct chemical identification of the particular substance, but it can show the final specific response of the sensors to the analyzed substance present in the sample and assign it to a specific group of compounds. Moreover, the e-nose method may allow for the production of small, inexpensive and user-friendly devices that can be used in different areas where gas identification mechanisms play a significant role . An electronic nose is a model of the mammalian olfactory system. According to the Axel and Buck theory , the perception process begins in the olfactory epithelium, where approximately fifty million receptor neurons initially identify and classify volatile molecules . Each ofCurrently, research is focused on applying the e-nose in medical fields, especially in the early diagnosis and prevention of respiratory diseases  using, among others, quartz microbubbles as well as detecting the presence of bacteria in the urine and the eyeball using polymer sensors . The e-nThe development of artificial olfaction methods would not be so dynamic without the implementation of different methods that allow for a detailed analysis of data sets of sensors arrays. Here, we would like to highlight the two most popular and useful methods—principal component analysis (PCA) and cluster analysis (CA). PCA is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information from the large set. By applying dimensionality reduction, we trade a little accuracy for simplicity. The reason for this is that smaller data sets are easier to explore and visualize which makes analyzing data much easier and faster for machine learning algorithms without extraneous variables to process. Thanks to PCA analysis, sensors are assayed for their responsibility for the vapors classification. Sensors with loadings ~ 0, for a particular principal component, have a minor contribution to the total response of the array, whereas, high values show discriminating sensors. According to this theory, sensors that have an inconsiderable responsibility for the distribution pattern in the PCA plot are usually removed from the sensor array, because of a negative effect on the pattern resolution. Moreover, sensors with equal loading parameters can be presented by just one sensor . In the The cluster analysis (CA) is a class of methods that are applied to classify objects or cases into relative groups named clusters. CA may be used to the obtained data set. The reaction between different vapors and various reduced graphene oxide (RGO) sensors results in data points that are localized close to each other for similar sensors. Therefore, chemically similar sensors should be classified into one cluster . Figure Development of new analysis methods, improvement of sensitivity and selectivity of detectors as well as the better understanding of sensing mechanisms allow to popularize the use of the e-nose in the industry and medicine.2) significantly improves the sensitivity of the detector and secures an excellent response to NO2 [3 [2 [Carbon nanotubes (CNT) found widespread use as gas sensors due to their electronic properties and large surface area. A significant improvement in the sensitivity and selectivity of CNT-based sensors has been achieved by the formation of various types of hybrids. It was shown that the formation of a CNT hybrid with a metal oxide  signifo NO2 [3 , and H2 O2 [3 [2 . SensorsO2 [3 [2 . An impoO2 [3 [2 .Carboxylated single-wall carbon nanotubes (SWNT-COOH) found application in the detection of amines, ammonia, trimethylamine, and dimethylamine. An increase of the sensor sensitivity based on SWNT-COOH was achieved by forming an SWNT-COOH composite with polymers, such as polyvinyl chloride, polyvinylpyrrolidone, and cumene ending with polystyrene-co-maleic anhydrite ,19. The 0 was defined as ΔR/Ro% = (R − Ro)/Ro * 100, where R stands for the resistance of the device exposed to the analyte and Ro stands for the initial resistance before analyte exposure. It can be noticed that the conductance of bare CNTs decreased significantly upon functionalization with ruthenium complexes with porphyrin. Moreover, the ruthenium complexes with porphyrin-coated CNTs had a more negative threshold gate voltage and lower transconductance when compared to the bare CNTs. These changes are attributed to the n-doping by the electron donor porphyrin of the p-type semiconductor CNT which results in lower carrier (hole) concentration and carrier mobility [Despite the particular physicochemical properties of carbon nanotubes, the low sensitivity to volatile organic compounds is a serious limitation. The increase in the sensitivity of sensors based on carbon nanotubes was achieved by creating a CNT hybrid with porphyrins and metalloporphyrins. The formation of the hybrid is based on the reaction of the porphyrin core with the sidewall of the nanotube ,22,23. Tmobility .A comparison of the sensitivity of CNT-porphyrins hybrids towards acetone, methanol, ethyl acetate, and tetrahydrofurane is presented in A chemiresistor built from a CNT network spanning two metallic electrodes has a current flowing through it when a voltage is applied. In the presence of an analyte, the current flow can be inhibited according to the following effects : modulatThe analysis carried out by Calbi  proved tMechanism of gas detection can rely on changing the material conductivity which is a result of the reaction of the gas molecule with the CNT surface. During the detection analysis of volatile compounds by e-noses, a conductivity measurement between two electrodes is performed. Due to the fact that CNTs are formed almost entirely from surface atoms, a small change in the chemical environment of CNT leads to a measurable change in conductivity. One of the reasons for the change in the sensor’s material conductivity is the change of Schottky’s potential barrier which occurs at the interface of metal and semiconductor at room temperature, i.e., on the electrode connector with CNT. At a higher temperature, e.g., 150 °C, gas molecules are adsorbed on the CNT wall and the charge transfer from the adsorbed gas to the CNTs contributes to the rise in the signal due to a change in conductivity. As a result of gas adsorption, electrons are transferred to the CNT conduction band that leads to a reduction in hole concentration and an increase in conductivity.2 and NH3 was observed [3N4 film. After isolating the CNT-Au connector and sensor exposure to NH3 no response from the CNT channels, even at high gas concentrations (above 500 ppm), was noted. However, after isolation of the CNT channels, the CNT-Au exposed to NH3 was highly sensitive at room temperature. The presented experiment suggests that NH3 induces Schottky barrier modulation which may be the predominant mechanism of NH3 detection by carbon nanotubes at room temperature. At temperatures above 150 °C, NH3 may adsorb to the CNT wall and play a role of an electron donor, whereas the Fermi CNT level moves towards the conduction band and the voltage threshold reaches a lower value. It should be also emphasized that NH3 reluctantly reacts with CNT in the pure form due to the significant activation barrier and preferential adsorption in structural defects. Furthermore, the activation barrier of NH3 adsorbed in CNT defects can be lowered by the presence of previously dispersed oxygen atoms. In a paper published by Peng, a change of the charge transfer in CNT exposed to NOobserved . It was 3 on the CNT wall does not induce any effect and the sensing signal arises from the CNT/metal contact. When NH3 is absorbed on the CNT/metal interface then the electrostatic charge balance between CNT and metal is disturbed by NH3 dipoles, which results in an increase of the Schottky barrier for hole injection. The sensitivity is gate voltage-dependent. In GS) bending the energy band of the CNT upward leads to narrowing of the Schottky barrier width, holes can tunnel through the barrier. At a positive gate voltage, the Schottky barrier width is too small for tunneling. Using CNT channels as a sensing element, the charge transfer leads to moving the Fermi level of the exposed CNT channel upwards and the energy band shifts downwards. Thus, a potential barrier is created impeding the current flow (A comparison of the two detection mechanisms related to the modulation of the Schottky barrier (SB) and the charge transfer (CT) shows some differences. For example, at room temperature, the absorption of NHent flow d. Here, 2, an observed signal amplification effect can result from the formation of CNTs embedded in SnO2. The formation of channels on the metal oxide surface may lead to an increase in gas molecules diffusion to the MOX surface, as well as to locally increase the electric field at the CNT-SnO2 interface [The SB detection is characterized by a very high sensitivity at room temperature but low reversibility, whereas the CT mechanism shows a low level of sensitivity at >150 °C and a high degree of recovery. The change of nanomaterials conductivity can be also connected to the reduction of the charge mobility in the CNT by forming areas disturbing the flow of charge. As we have already mentioned a single CNT does not have sufficient length to form conductive channels which are basically formed by combining multiple CNTs. Thus, if the analyzed substance is adsorbed in the connections between the nanotubes, the conductive channels are defective and the CNT conductivity will be modified . In the nterface .2) were proposed. Wei et al. explain the gas-sensing by amplification effect of the PN junction structure between n-SnO2 and p-SWCNT [The various sensing mechanisms of CNT-MOX , X-ray diffraction, computational methods , and aromaticity indices analysis. −1) marked by Versanyi [To assess an influence of metals on the electronic system of different (aromatic) ligands with potential biological activity, we applied different methods such as the Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy (UV/VIS), nuclear magnetic resonance spectroscopy , Hg(II), Ag(I), Pb(II), and alkali metals. In this case, both, ligand’s electronic system and aromatic system are being disturbed. Moreover, bond polarization increases. Absorption bands related to π→π* transitions move to the shorter wavelength (hypochromic shifts). FTIR and FT-Raman bands characteristic for an aromatic moiety shift towards smaller wave numbers and their intensity decreases or vanishes. Here, X-ray data and aromaticity indices indicate differentiation of both, the length of bonds and angles between bonds in aromatic rings. Moreover, the delocalization of the electronic charge is decreased. We observed similar effects while analyzing oxygen adducts of Fe, Ru, and Os complexes with porphyrins by means of Raman spectroscopy and matrix isolation spectroscopy .2 and H2O results in a decrease in resistivity which claims their electron acceptor nature, while absorption of NH3 and CO leads to an increase in resistivity which indicated their electron donor character [Distribution of the electronic charge, as well as HOMO/LUMO , level occupancy determines not only reactivity and stability of molecules but also aromatic ring susceptibility to electrophilic substitution. An increase or decrease of an electronic density in the aromatic ring (including graphene and porphyrins rings) triggered by the influence of different metals, substituents, or even trace amounts of gases may result in changes in conductivity (resistivity) of electronic nose sensors. haracter ,41.Corroles), this effect was observed for magnesium whereas for iron it is not evident [The probable cause of an increased sensitivity of metal complexed porphyrins is the stabilization of its electronic system. It has been shown that there is a relationship between an increase of sensitivity and selectivity and the polar nature of volatile compounds. It suggests that the polar groups bind through hydrogen bonds in porphyrin analogs rings in which three hydrogen atoms are present instead of one. In the presence of polar compounds, an introduction of metal ions in the porphyrin analogs is not a sufficient method to improve the detection properties compared to the porphyrins in the basic form. Complexation with iron and magnesium always increases the sensitivity of porphyrins however, in the case of porphyrin analogs , graphene oxides (GO), and reduced graphene oxides (RGO) are excellent materials for the construction of gas sensors due to their electronic, chemical, mechanical, and thermal properties, as well as high sensitivity to surface adsorption of gas molecules. The morphological characterization of the graphene structure was presented in , and CO . The dis, and CO ,42. Diff2 and O2 adsorption convert the system to p-type semiconductor, while the NH3 adsorption leads to n-type behavior. The p- and n-type semiconducting behavior can be detected by applying and modulating gate voltage. Among all gas molecules considered, the absorption of the NH3 molecule can enhance conductance [2 can be an electron acceptor from the material where NO2 has been adsorbed, therefore, a decrease in electron density and an increase in graphene’s resistance is expected. According to Latif, similar conclusions were presented where the increase of graphene sensor resistance in contact with NO2 was shown [2 adsorption can take place in low energy centers of graphene via sp2 carbon atom or in high energy centers  [2 is adsorbed mainly via oxygen functional groups [2 molecule, the positively charged hole is formed in the graphene’s structure [2. The results of the experiment carried out by Nomani showed that the exposure of graphene to NO2 at a concentration of 500 ppb and 18 ppm caused a decrease in conductivity of 2.25% and 10%, respectively [The electronic structure of graphene can be modified by gas molecules in diverse ways. COductance . In the ductance . NO2 canas shown . The NO2defects) . NO2 is l groups . As a retructure . Thus, tectively . It was 2, NH3, H2O, H2, and CO can act as electron donors or acceptors which may cause a change in the sensitivity and conductivity of graphene. Graphene, being a perfect crystalline material with a high surface to volume ratio, can undergo fluctuations in the charge carrier’s concentration by adding even several additional electrons. It is possible to distinguish two basic charge transfer mechanisms which lead to a change in graphene conductivity. Firstly, the charge transfer can depend on the relative position of the HOMO and LUMO orbitals. If the HOMO orbital is above the Fermi level of pure graphene (Dirac point) then the charge transfer takes place towards graphene. However, if the LUMO orbital is located below the Dirac point, then the charge transfer takes place towards the adsorbent particle. Secondly, the charge transfer between the absorbed molecule and graphene can be partially affected by the mixing of HOMO and LUMO orbitals with graphene orbitals (hybridization) [The adsorbed gases such as NO, NOization) .3 group which is an electron donor to the graphene aromatic ring leads to an increase in the electronic density in the ring and activates it by directing the substituent to the ortho- or para-position. Thus, electron donor groups improve the conductivity of graphene. NO2 as an electron acceptor that directs the substituent to the meta-position withdraws electrons from the graphene aromatic ring and thereby, inhibits electrophilic substitutions [2, −CH3, −C2H5, activating the benzene ring) and type II  can be used in the classification of trace amounts of gases. The type I substituents push the electrons into the ring and activate it, while the type II substituents pull out the electrons from the ring and deactivate it.According to the, well known, mechanisms the NHitutions . Electro2 and Cl2. For comparison, RGO-IL shows a strong response to all four gases and a negative type of conductivity (type n). In addition, it has been proven that IL content has a significant effect on the conductivity of the gas-sensitive material, and the sensitivity of RGO increases with the increase in IL concentration. The mechanism of changing the RGO conductivity can be based on the presence of oxygen groups that lead to disturbance of the electronic structure. An improvement of the sensitivity of the sensor by surface modification of reduced graphene oxides using various types of ionic liquids (IL) with a tailored structure was proposed by Zhu et al. . It was Alizadeh et al. suggest the significant differences in the selectivity of the sensor formed from RGO which depends on the conditions of oxide synthesis . The forSome molecules with reducing properties can change the electronic and adsorptive properties of RGO by transferring oxygen, nitrogen or the entire functional group to the graphene structure. In the case of GO exposure to hydrazine, the oxygen functional groups are removed from the oxide, while the hydrazine nitrogen atoms are covalently attached to the GO surface. The nitrogen atom can play a function of the n-type dopant and modify properties of the final graphene product . 4 effectively reduces carbonyl groups, however, it has low efficiency in reducing epoxides and carboxylic acids. Ascorbic acid is a moderate reducing agent compared to the previous two. The advantage of using vitamin C as a reducer is to minimize the risk of introducing heteroatoms into the RGO structure [Sodium borohydride is a better reducing agent than hydrazine but introduces several heteroatoms to RGO. It has been demonstrated that NaBHtructure .0 parameter, where ΔR is the resistance difference between the sensor response after and before the odor exposure, and R0 is the resistance due to the response on the odor exposure. Depending on the reductor used, the difference in the ΔR/R0 parameter was 3% for ethanol and 5% for acetone [Results presented by Alizadeh indicate a significant effect of graphene oxidation and reduction of graphene oxides on the final porosity and surface of the obtained materials . In the  acetone .2 [2S, CO, and NO2 [2 adsorption, while doping graphene with Si significantly improves the sensitivity to NO and NO2. The sensor built of N-Si-G shows sensitivity towards 21 ppm NO2 and the sensor response declines along with the gas concentration to ~1 ppm [Experimental and theoretical studies proved that the sensitivity of graphene-based sensors can be significantly improved by doping with Br, N, P, Ga, Cr, Mg, S, and Si. Doping leads to the formation of new active sites on the graphene’s surface which have an ability to strongly adsorb gas molecules. For example, doping graphene with Mg and Cr results in an increase in sensitivity towards SO2 ,59, whil and NO2 ,61, respo ~1 ppm . In addio ~1 ppm . The sum3 (including the type I substituents) increase the electronic density in the aromatic ring and stabilize electronic systems. Substituents with low ionic potential  destabilize the electronic systems of aromatic ligands [2, CO, SO2, CO2, N2O3, and NO radically reduce the conductivity of graphene, while NH3 increases its conductivity. In addition, metal-complexed porphyrins  shows greater sensitivity and selectivity to trace amounts of NH3 than porphyrins in the basic form.To enhance the interpretation of the sensor detection molecular mechanisms in the e-nose, we used our previous analysis of the influence of metals and substituents on the electronic systems of carbon and heterocyclic rings of selected aromatic ligands . We show ligands ,39,85,863) and delocalization of the electronic charge distribution results in an increase of the graphene sensor conductivity. Both phenomena show a certain analogy to the metal’s electrical conductivity. High electronic density and delocalized charge surrounding the atomic strands of metals are the cause of the excellent conductivity of these substances. The presented idea of the radical change of the graphene conductivity under the influence of type I and II substituents and the change of sensitivity and selectivity of porphyrins complexed with metals is at this stage purely qualitative. The quantitative confirmation of the described relationships requires further research.The comparison of our previous research with the analysis in the literature suggests that the increase of graphene electronic density  and the phenomena of electron transfer can significantly affect the change in graphene conductivity under the influence of trace amounts of various gases.The proposed approach to the gas detection mechanism can open or facilitate the search for further practical applications of graphene, nanotubes, and porphyrin derivatives in various fields of science and industry. For the quantitative confirmation of the presented thesis, further experimental and theoretical studies are necessary.