This study systematically investigated how the Stone–Wales (SW) defect, the single vacancy (SV), and the double vacancy (DV) affect the mechanical properties of graphene/aluminum composites. The interfacial bonding energies containing the SW and DV defects were about twice that of the pristine graphene. Surprisingly, the interfacial bonding energy of the composites with single vacancy was almost four times that of without defect in graphene. These results indicate that point defects enhance the interfacial bonding strength significantly and thus improve the mechanical properties of graphene/aluminum composites, especially the SV defect. The differential charge density elucidates that the formation of strong Al–C covalent bonds at the defects is the most fundamental reason for improving the mechanical properties of graphene/aluminum composites. The theoretical research results show the defective graphene as the reinforcing phase is more promising to be used in the metal matrix composites, which will provide a novel design guideline for graphene reinforced metal matrix composites. Furthermore, the sp Metal matrix composites (MMCs), which comprise of the metal matrix and the reinforcing phase, have been in the spotlight since the early 1990s ,3, metal2-hybridized carbon atoms 2-hybridion atoms , Ag 28]2-hybridion atoms , W 30],,2-hybridon atoms , Al alloon atoms ,33, Mg aon atoms , Sn alloon atoms , Ni–Al aon atoms , Ti–Al aon atoms , and W–Con atoms . Neverthon atoms . Moreoveon atoms . The finon atoms . Thus, hon atoms , semi-poon atoms , moleculon atoms , electroon atoms , in situon atoms , and othon atoms ,48,49,50Al is the most widely studied metal matrix in GMMCs, owing to its physical and mechanical properties, such as light weight, high corrosion resistance, as well as excellent electrical and thermal conductivity. Nevertheless, the interfacial bonding strength between graphene and the Al matrix is an important factor that affects the mechanical properties of composites. Due to the poor wettability of graphene with the metal matrix, graphene is difficult to form a strong bond with the metal matrix, thus resulting in the poor performance of composite materials . A few rIn this work, the way in which the three typical point defects , single vacancy (SV), and double vacancy (DV)) affect the mechanical properties of graphene/Al composites was studied systematically through the first-principles calculations. Different from the pristine graphene, more sophisticated adsorption sites need to be considered due to the existence of the defects in graphene, indicating that we need to construct a series of different interfacial configurations. Consequently, the interfacial bonding energy, the equilibrium interlayer distance, the minimum interatomic distance, and the buckling of these different interfacial configurations were investigated systematically through the first-principles calculations to better understand how the point defects affect the mechanical properties of graphene/Al composites. Furthermore, the differential charge density was also calculated to give an intuitive illustration of the interfacial electronic structure and charge transfer to understand the interfacial strengthening mechanism between graphene and the Al matrix.2-bonded low-dimensional carbon structures, as shown in Pristine graphene is composed of hexagonal rings, which serve as the building blocks for the spThe excessive lattice constant mismatch of graphene adsorbed on metal surfaces will lead to the initial stress in the interior of graphene sheet. As a result, it is impossible to determine whether the buckling of graphene after adsorption on metal surfaces is due to the interactions between graphene and the metal surface, or due to the release of the initial stress in the interior of graphene sheet. Therefore, the lattice constant mismatch is a priority factor in constructing the interfacial configuration. A metal surface was constructed using a slab that was composed of a finite number of layers of metal and a vacuum region in the direction perpendicular to the layers. We constructed the graphene/metal interface supercell from a slab consisting of six layers of metal atoms, with monolayer graphene adsorbed from the top of the slab. The vacuum region was 15 Å. It should be noted that the upper layer film is usually thinner than the metal substrate and the film tends to match the lattice constant of the metal substrate in the experiment ,60, and −5 eV.Density function theory (DFT) calculations were carried out by the Vienna ab initio simulation package (VASP) . We seleThe detailed parameters of the pristine graphene/Al (111) and the defective graphene/Al (111) interfaces are listed in For the pristine graphene, For the SW defect graphene, we considered more sophisticated adsorption sites due to the existence of two pentagons and two heptagons, as shown in Different from the SW defect graphene, the SV graphene has local magnetic moments, owing to the existence of dangling bonds according to the literature . The spiThe DV is formed in the graphene sheet when two carbon atoms are missed. Different from the SV, the dangling bonds can be healed for the connectivity of carbon atoms. Therefore, no dangling bonds remain in the DV graphene, indicating that the chemical activity of the DV graphene is not good as that of the SV graphene. It can be seen from According to the different adsorption sites shown in For the pristine graphene/Al (111) interface in When it comes to the SW defect graphene/Al (111) interface, there are two kinds of equilibrium configurations found, as shown in For the SV graphene/Al (111) interface, two non-equivalent equilibrium configurations could be observed, as shown in terature . By contIn the case of the DV graphene/Al (111) interface, there were five possible equilibrium configurations found, as shown in In summary, we found that the existence of defects in graphene did enhance the interfacial bonding strength significantly and thus improved the mechanical properties of graphene/Al composites, especially the SV graphene. The above results, such as To understand the interfacial strengthening mechanism between graphene and the Al matrix, we also calculated the differential charge density in order to observe the interfacial electronic structure and charge transfer more intuitively. The differential charge density in this work was calculated as follows:No charge redistribution around the atoms and no charge accumulation at the center of the Al atoms and the C atoms could be observed according to the differential charge density results at the pristine graphene/Al (111) interface. In other words, the interfacial bonding between the pristine graphene and Al (111) surface was only Van der Waals force. Therefore, we did not give the differential charge density plots induced by the adsorption of the pristine graphene on the Al (111) surface here. Unlike the pristine graphene, obvious charge transfer was observed between the defective graphene and the Al (111) surface and occurred mainly between the carbon atoms and the Al atoms at the defect. In order to observe the charge redistribution and the charge accumulation more intuitively, we only gave the local differential charge density plots of the non-equivalent equilibrium configurations between the carbon atoms and the Al atoms at the defect, as shown in In the case of the SW defect graphene/Al (111) interface, almost no charge redistribution and charge accumulation was observed from the second configuration, while clear charge redistribution and large amounts of charge accumulation was observed from the first configuration, as shown in 2 3p1) has only one unpaired electron, and thus it is impossible for the Al atom to form three covalent bonds with three carbon atoms directly. Therefore, we infer that the s orbital and two p orbitals of the Al atom hybridized with each other in order to form three identical sp2-hybridized orbitals, which is similar to the phenomenon that three carbon atoms can form three covalent bonds with one sd2-hybridized Ti atom reported in the previous work of our group [2-hybridized Al atom was not a plane equilateral triangle, which was different from the well-known sp2 hybridization. We think the reason for this phenomenon was that too large Al atomic radius (than carbon atom) and too small vacancy space made the Al atom unable to move exactly to the carbon vacancy. As for the three carbon atoms that formed covalent bonds with the Al atom, they formed two covalent bonds with two carbon atoms before the structural optimization. In addition to the charge accumulation at the center of the three carbons and the Al atom after the structural optimization, the charge density in the other directions of the three carbon atoms was also clearly found from the side view, indicating the formation of the C dangling bonds. These results indicate the hybridization state of the three carbon atoms changed from the original sp2 hybridization to the current sp3 hybridization. The existence of the sp3-hybridized C dangling bonds was able to increase the chemical activity of the SV graphene, thus making it possible for the SV graphene/Al composite to be used in the catalysis field. As for the second configuration, we found that only a carbon atom formed a covalent bond with an Al atom, showing that the interfacial bonding strength obtained from the second configuration was weaker than that from the first one. These results also agree with our calculation results in When it comes to the SV graphene/Al (111) interface, we give the local differential charge density plots of the two non-equivalent equilibrium configurations as shown in ur group . The optFor the DV graphene/Al (111) interface, the local differential charge density plots of the equilibrium configurations can be divided into the following cases, as shown in 2 hybridization of the Al atom, and the sp3 hybridization of the carbon atom at the defect. The calculated results prove the rationality of our differential charge density analysis. Here, we do not give the projected density of states plots because the detailed analysis processes have been reported in the previous work of our group [Subsequently, we calculated the projected density of states to further verify the formation of covalent bonds, the spur group . Therefo2-hybridized Al atom were observed at the SV graphene/Al (111) interface, and the hybridization state of the three carbon atoms changed from the original sp2 hybridization to the current sp3 hybridization. These extensive calculations demonstrate that the defective graphene as the reinforcing phase is more promising in terms of use in the metal matrix, which offers a new insight to guide the design of graphene/metal composites.We systematically investigated how the SW defect, the SV, and the DV as three typical point defects affect the mechanical properties of graphene/Al composites through the first-principles calculations. The interfacial bonding energy, the equilibrium interlayer distance, the minimum interatomic distance, and the buckling were calculated in order to further understand the relationship between the point defects and the mechanical properties. The results demonstrate that the point defects significantly increased the interfacial bonding energy and the buckling while decreasing the equilibrium interlayer distance and the minimum interatomic distance, especially the SV. The interfacial bonding energy of graphene containing the SV was approximately four times that of the pristine graphene. In other words, the point defects enhanced the interfacial bonding strength significantly and thus improved the mechanical properties of graphene/Al composites. To further understand the interfacial strengthening mechanism, we also performed the electronic structure analyses. The differential charge density shows that there was numerous charge accumulation found at the center of the carbon atoms and the Al atoms at the defects, confirming that the carbon atoms formed the strong covalent bonds with the Al atoms, which is the most fundamental reason for enhancing the mechanical properties of graphene/Al composites. It is worth noting that three covalent bonds formed between three carbon atoms and one sp


RPL7 and NOG2 have conserved positions; both intron-containing genes encoding snoRNAs. Furthermore, both the proteins and snoRNAs are involved in ribosome biogenesis, suggesting that the expression of the protein-coding genes and noncoding snoRNAs may be functionally coordinated. Indeed, these introns are also conserved in three-quarters of fungi species. Our study shows that fungal introns have a complex evolutionary history and underappreciated roles in gene expression.Previous evolutionary reconstructions have concluded that early eukaryotic ancestors including both the last common ancestor of eukaryotes and of all fungi had intron-rich genomes. By contrast, some extant eukaryotes have few introns, underscoring the complex histories of intron–exon structures, and raising the question as to why these few introns are retained. Here, we have used recently available fungal genomes to address a variety of questions related to intron evolution. Evolutionary reconstruction of intron presence and absence using 263 diverse fungal species supports the idea that massive intron reduction through intron loss has occurred in multiple clades. The intron densities estimated in various fungal ancestors differ from zero to 7.6 introns per 1 kb of protein-coding sequence. Massive intron loss has occurred not only in microsporidian parasites and saccharomycetous yeasts, but also in diverse smuts and allies. To investigate the roles of the remaining introns in highly-reduced species, we have searched for their special characteristics in eight intron-poor fungi. Notably, the introns of ribosome-associated genes  Spliceosomal introns are ubiquitous in eukaryotes. They are removed from all regions of transcripts including the untranslated regions (UTRs) as well as coding sequences (CDS) . Early sSaccharomyces cerevisiae to 7.8 introns/kb in Trichoplax adhaerens  had a density of introns of about 4 introns/kb (the number of introns per 1 kb of CDS on average) . Notablydhaerens . Counter animals . The lar animals  and may, animals . Several animals  and reco animals .S. cerevisiae genes have introns. In contrast, some other fungi, for example, the facultative pathogen Cryptococcus neoformans, have a relatively high intron density of 4 introns/kb , allowing for improved resolution of the history of these organisms. In both lineages, our reconstructions reveal a massive intron loss event leading to the ancestor of a large clade of intron-poor organisms. However, whereas in Microsporidia this loss event occurred in the ancestor of the group after divergence from Cryptomycota, for saccharomycetous yeast, this massive loss event occured within the group, after divergence of L. starkeyi from the ancestor of all other saccharomycetous yeasts represented in the data set.These results also illuminate the history of massive intron loss in two lineages. Many studies have found that the obligate intracellular microsporidian parasites have zero or few introns  and thatA bias toward intron loss over intron gain is seen across the fungal tree . This isNosema bombycis and Nosema apis) and four saccharomycetous yeasts . Although preliminary analysis suggests the reality of some of these gains, it is worthy of note that, given the small absolute number of gains involved (leading to <1 intron/kb), further detailed analysis will be necessary to confirm these episodes.Although ongoing intron loss is characteristic of most lineages, our results indicate several substantial episodes of intron gain. Within Basidiomycotina, we estimated a 25% increase in intron density leading to the ancestor of Ustilaginomycetes and a 14% increase in the ancestor of Pucciniomycotina. The most substantial intron gains occurred, unexpectedly, within the famously intron-poor lineages Microsporidia and Saccharomycotina. We inferred substantial, secondary independent intron gain in two extant microsporidians, (Large variations of intron density in fungi raise a question of whether intron density (or number) correlates with genome size. Given that genome size has been argued to relate to the number of introns, organismal complexity, population size, and generation time , we exam = 0.26, P = 0.0060). Analyses of ascomycetes and basidiomycetes separately also showed similar results , suggesting that this is a common feature of fungi.Interestingly, we found only a weak relationship between genome size and intron density , this relationship is significantly stronger in basidiomycetes . Similarly, total intron length strongly correlates with genome size in basidiomycetes . Taken together, these results highlight the distinct characteristics of introns between intron-poor ascomycetes and intron-rich basidiomycetes.In contrast, although the relationship between genome size and intron number is also generally weak across the entire data set .To explore how gene structures evolve, we computed the lengths of exons that have been reshaped by intron gain or loss events from the analysis of intron/splice site histories . We estimated a false positive rate of 3% for recently gained introns using a posterior probability cutoff of 0.99 . We identified four major classes of extant exons that have been reshaped, where one exon has been divided into multiple pieces, that is, 1) “1-into-3” or 2) “1-into-2”, which has arisen from two or one intron gains; and where multiple exons have been fused into one piece, that is, 3) “2-into-1” or 4) “3-into-1”, due to one or two intron losses  online. 2 tests for the first three intron positions [as indicated by square brackets], P < 10−8). In contrast, introns are preferentially lost at the 3′ ends of genes , which supports the idea of reverse transcriptase-mediated intron loss . We were able to identify 15 recently gained introns in 12 fungi genomes that harbor repeat elements  Kolobok-H, 2) P Instability Factor (PIF)/Harbinger, and 3) TcMar-Fot1; and retrotransposons: 4) Copia and 5) Gypsy long terminal repeats (LTRs)  online. S. cerevisiae and Candida dubliniensis in Saccharomycotina, Cyphellophora europaea and Sporothrix schenckii in Pezizomycotina, and Ustilago maydis, Pseudozyma hubeiensis, Meira miltonrushii, and Malassezia sympodialis in Ustilaginomycotina , identified orthologous genes, and analyzed the evolution of intron-containing and intronless genes. These species included We identified 1,302 complete sets of orthologous genes from these intron-poor species. Comparison of intron-containing genes with intronless genes revealed a number of differences. First, we found that intron-containing genes are less likely to have undergone recent positive selection on their protein-coding sequences than are intronless genes A. We proS. cerevisiae and U. maydis, but not in other species (P < 0.05). In S. cerevisiae, this finding could largely be explained by the overrepresentation in highly-expressed ribosomal protein-coding genes. Alternatively, it could largely be explained if intron-mediated cross-regulation among paralogous genes decreases dosage problems associated with gene duplicates  and NOG2 introns in other fungi species. We found that 82% and 72% of fungi species retain the RPL7 and NOG2 intron positions  and a box H/ACA snR191, respectively. These noncoding RNAs are involved in rRNA and ribosome biogenesis.We further explored the conservation of ositions  online. NOG2 paralogs . A functional divergence may occasionally occur between the RPL7 paralogs by allowing intron/snoRNA loss in one paralog.As noted above, none of the fungi species have intron/snoRNA-loss paralogs  online. Our results suggest that these introns with conserved positions have some functions . Interestingly, this conservation may not date to LECA, as snoRNA genes have been shown to move within the genome . Such dyWe observed that introns are closer to initiation codons than the null distributions  online, RPL7) of intron-poor protozoa and saccharomycetous yeasts , 60% of the intron density of Homo sapiens. A great diversity of exon–intron structures is also observed within Dikarya. In general, ascomycetes have roughly half as many introns as do basidiomycetes, however any such generality is belied by substantial diversity within each group. This diversity includes both groups of organisms that are well known to be intron-sparse   , but alsfe cycle .Anncaliia algerae and Nematocida parisii). At the other, the budding yeast Candida glabrata and the smut fungi Pseudozyma hubeiensis that harbor long introns . Notably, these patterns do not show a simple relationship to intron density. For instance, S. cerevisiae has very few introns (n = 282), but its introns are relatively long (mean = 397 bp); conversely, the yeast Cryptococcus neoformans has many introns  but they are relatively short (mean = 63 bp). Interestingly, though fungi do span most of the known eukaryotic intron density of intron density (see above), they do not reach the extremes of intron length observed elsewhere, for example, mean intron lengths >1 kb in vertebrates.Fungi also show great diversities of intron lengths. At one extreme, the microsporidian parasites that are devoid of introns . In the absence of introner activity, intron gain appears to occur mostly by highly idiosyncratic events ranging from partial gene duplication to insertion of mitochondrial DNA segments . PerhapsNotably, these results depend on the accuracy of our reconstruction methods. Debate over reconstruction methods for intron loss and gain are nearly as old as the discovery of introns themselves. Concerns have been raised both of overcounting of intron losses and of intron gains . AlthougAlthough the methods used here remain imperfect, previous theoretical and empirical results suggest that the major conclusion, that intron losses tend to outnumber intron gains, is likely to be robust to these concerns. First, previous results have shown that improved taxonomic density tends to increase the estimated ratio of intron losses to intron gains  as sister to a clade of Chytridiomycota and fungi. In contrast, we placed Chytridiomycota sister to a clade of Blastocladiomycota and the rest of the fungi. Secondly, the final analysis supported the monophyly of Zoopagomycota (represented here by Conidiobolus coronatus) and Mucoromycota. However, the smaller tree we used in our analysis places Zoopagomycota sister to a clade consisting of Mucaromycota and Dikarya. These ancient radiations remain contentious. Both of the alternative groupings used in this analysis were recovered in other studies   , whereast of LSU . The cont of LSU .Sc. pombe genome   . Indeed,t stress .Notably, most of the first introns are located near translation initiation codons  online. S. cerevisiae  from the Ensembl Fungi release 34 . In addiMycoCosm . Detailehttp://ftp://ftp.ncbi.nlm.nih.gov/genomes/ASSEMBLY_REPORTS/assembly_summary_genbank.txt, last accessed December 9, 2016)  . CompletSaccharomyces cerevisiae was retrieved from YeastMine on April 1, 2017  and retained 263 diverse fungi from 389 quality filtered genomes.  v1.16.6 .http://timetree.org/, last accessed September 19, 2020) using a list of the 263 fungal species and the outgroups  and the 1,000 Fungal Genomes Project  . Specifically, this bimodal distribution was modeled using a finite mixture model whose parameters were estimated by an expectation–maximization algorithm , and the threshold was computed by Monte Carlo simulations (using the cutoff function with default settings). Only the orthologs that captured at least 80% (211/265) of the species were used in the subsequent analyses .For the inference of ancestral introns, orthologous genes were identified using HMMER v3.1b2 . A HMM d2, 2017) . HomologS. cerevisiae and Candida dubliniensis in Saccharomycotina, Cyphellophora europaea and Sporothrix schenckii in Pezizomycotina, and U. maydis, Pseudozyma hubeiensis, Meira miltonrushii, and Malassezia sympodialis in Ustilaginomycotina. The orthologs of these intron-poor species were identified using proteinortho5 (using parameter -synteny)  . A totalhttps://github.com/LongTianPy/SkewGD_v1, last accessed April 24, 2017). This pipeline includes sequence clustering and “age” estimation using Ks (the number of synonymous substitutions per synonymous site) (Duplicated genes were identified using SkewGD v1 (us site) .For the inference of ancestral introns, protein sequences were aligned using Clustal Omega v1.2.4 (using parameter –hmm-in) . AnnotatThe orthologs of the intron-poor species were aligned using MUSCLE v3.8.31 . The proWe inferred ancestral introns from 1,444 sets of orthologs of 263 fungal genomes using Malin . Firstly = 0.001). For the constant rate model, each intron site has only a branch-specific gain and loss rate. In contrast, for the rate-variation model, intron sites additionally belong to one of two discrete rate loss categories.Failure to account for variation in intron loss rate across sites can lead to an underestimation in intron density of eukaryotic ancestors , and pre2 distribution with one degree of freedom. In this comparison, we obtained a P value lower than machine precision. Therefore, we rejected the constant rate results and chose the more complex rate-variation model. In addition, we inferred ancestral densities by using Dollo parsimony  were filtered by searching against the NCBI Nucleotide database, fungi (taxid: 4751), using BLASTN with default settings . To estimate the false positive rate of recently gained introns, full-length matches to both the query species/genera and the early-branching groups (in the NCBI taxonomy) were considered as false positives and subsequently discarded (n = 101).Intron site histories were estimated using the rate variation model. Only the intron site histories with a posterior probability ≥0.99 were retained. The candidates of recently gained introns . The repeat families detected were used to build species-specific profile HMM databases using hmmbuild and hmmpress ( = 10)  .The overlapping regions between repeat elements and introns, exons, and intergenic regions were obtained using BEDTools v2.27.1 . The proThe distributions of intron density (introns/kb), genome size, and the number of CDS and introns, and the total lengths of CDS and introns were examined for normality using different transformation functions in the R package bestNormalize v1.6.1. To avoid infinities, a pseudocount of 0.001 was used for intron density, whereas a pseudocount of 1 for intron number and total intron length. Box-cox transformation was chosen and data were transformed using the boxcox function.Phylogenetic independent contrasts analysis was carried out using the R package caper v1.0.1. Specifically, input variables were mapped to the tips of the timetree phylogeny using the comparative.data function. Independent contrasts  were calThe orthologous protein sequences were aligned using PRANK v.150803 . The alihttp://www.bioinf.uni-leipzig.de/publications/supplements.html   . These f5, 2018) . These m5, 2018) .S. cerevisiae genes was performed using the Bioconductor packages clusterProfiler v3.0.5 and org. Sc.sgd.db v3.4.0  mapped reads.For ribosome profiling, translation efficiency (TE) was calculated as:S. cerevisiae orthologs in other species using proteinortho5 (using parameter -synteny)  . We foun2 test, Welch two-sample t-test, Kolmogorov–Smirnov test and Spearman’s rank correlation were calculated using the base R system, SciPy v1.4.1, and Pingouin v0.3.8 (Statistical analysis and plotting were performed using R v4.0.3 and Python v3.7.7. Fisher’s exact test, χn v0.3.8 . Computan v0.3.8 , unless Molecular Biology and Evolution online.msab094_Supplementary_DataClick here for additional data file.


This circumstance deprives the solids’ in vitro spectroscopy of revealing the individual character of basic structural elements, and in silico spectrometry fills this shortcoming. The obtained virtual vibrational spectra allow for drawing first conclusions about the specific features of the vibrational dynamics of the necklaced graphene molecules, caused by spatial structure and packing of their graphene domains as well as by chemical composition of the relevant necklaces. As shown, IR absorption spectra of the molecules are strongly necklace dependent, once becoming a distinct spectral signature of the amorphous body origin. Otherwise, Raman spectra are a spectral mark of the graphene domain’s size and packing, thus disclosing the mystery of their universal D-G-band standard related to graphene-containing materials of various origins.The article presents results of an extended virtual experiment on graphene molecules performed using the virtual vibrational spectrometer HF Spectrodyn that exploits semiempirical Hartree–Fock approximation. The molecules are composed of flat graphene domains surrounded with heteroatom necklaces. Not existing individually, these molecules are met in practice as basic structure units of complex multilevel structure of all  The concept of virtual spectrometry has arisen recently, but it has turned out to be attractive and productive for placing things in order in the field of computational spectroscopy. A virtual spectrometer, as any device, is usually designed to carry out a large number of measurements. Certainly, these measurements’ accuracy is limited by the device’s technical characteristics. Whatever these characteristics are, there will always be reasons for their inconsistency, with some specific requirement of a real experiment. Underlying computational software determines the technical characteristics of a virtual spectrometer. In the case of vibrational spectrometry, the programs must allow for solving the dynamical problem of an object using the methods of quantum theory. Thus, in the first realized virtual multi-frequency spectrometer (VMS) , the cal6H6, had not lived their own lives, but have been considered as an integral accessory when describing real molecules once identified with the latter. Moreover, even the presence of a set of possible structures, such as Kekule ones, did not violate the general perception of the benzene molecule in the form of a hexagonal structure formed by C-H units, taken as a real picture. However, evidently, C6H6 hexagon is a model of a real substance called benzene.The first goal of the virtual vibrational spectrometry of complex molecular objects ,4,5 was digital twins (DTs), which are defined as virtual representations of physical assets enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making  and Ak-rGO (XVII) [17.23], are shown in the last row of To do this, first it is necessary to construct DTs of the amorphic under study. Diverse experimental studies, recently performed for a selected set of ns (ACs) ,17,18,19e carbon . The sec−1 and narrow band at 2100 cm−1, is much in common despite a considerable difference in the chemical compositions of the DTs necklaces. Evidently, the first region is well consistent with a composition of CH3 units, presenting C-CH3 stretchings and bendings as it was discussed relating to the VIII spectrum in XVI only, while this role in the case of DT XVII is given to methylene ones. Apparently, a general coincidence of the GFs for both units  well correlates with similar three-band virtual one, reliably supporting atomic configuration of the DT XVI. Naturally, it is impossible to speak about a complete adequacy of the proposed model to the real BSU structure, but the fact of reproduction of the main components of the latter is obvious. In the current case, the evidence, provided with the INS , vibrational spectra of the bodies are determined by the pool of covalent bonds, among which sp2C=C bonds do not possess a static dipole moment due to their IR activity arising from a dynamic mechanism in which the charge is transferred from extended to compressed bonds [sp2C=C bonds mainly due to which Raman signatures of bare graphene domains and the relevant necklaced bodies are similar.IR and Raman spectra of the bodies are provided with different components of the bonds pool. Covalent homopolar ed bonds . AccordiIR spectra depend on the size of the body graphene domain rather weakly. In contrast, Raman spectra show a drastic dependence on the linear size and packing of both bare and necklaced graphene domains. When the domain dimension approaches ~15 nm. which is the free path of the high frequency optical phonons in graphene crystal , the RamThe necklaced graphene molecules are prone to stacking and agglomeration. The former examined in the current study showed a practical tolerance of IR spectra, but a drastic change of the Raman ones. The latter, once multiband for individual bodies, take on a dual-band form similar to the standard empirical D-G doublets when stacking.sp2C=C bonds in layers that are extremely flexible. However, more important seems to be another thing—a strong dynamical coupling between the layers. Actually, the interlayer distance in stacks of graphene materials is of ~3.4 Å, once equalizing the van der Waals diameter of carbon atoms. Evidently, the out-of-plane vibrations, which are typical for multilayered graphene [….C distance approaching it to the maximum length of the sp3 C bond of ~2.5 Å [sp3C-C bonds between the layers are not formed, while a dynamic imitation of long C-C bonds between the layers may occur. The frequencies of corresponding sp3C-C stretching expectedly fill the interval that coincides with the D band region. Since the intensity of the out-of-plane phonon D bands in multilayered graphenes significantly grows when the nanostructuring of the latter effectively proceeds [This finding removes the veil from the mystery of the universal standard of Raman spectra of graphene-containing materials of various origins. A huge number of spears were broken when trying to explain this standard form of Raman spectrum, regardless of the wide variability of the structure and chemical composition of the studied materials. The above analysis of the Raman spectra of necklaced graphene molecules opens up a new possibility of linking this fact with the stacked nature of the investigated solids. The transformation of a multiband spectrum of an individual molecule into a D-G doublet can be qualitatively described by the amplification of the D band with respect to the G one. Obviously, the interaction between molecules in adjacent layers is the reason. This interaction expectedly affects the dynamics of the graphene ,50,51,52f ~2.5 Å . Obviousproceeds . Undoubt


Respiratory monitoring is a fundamental method to understand the physiological and psychological relationships between respiration and the human body. In this review, we overview recent developments on ultrafast humidity sensors with functional nanomaterials for monitoring human respiration. Key advances in design and materials have resulted in humidity sensors with response and recovery times reaching 8 ms. In addition, these sensors are particularly beneficial for respiratory monitoring by being portable and noninvasive. We systematically classify the reported sensors according to four types of output signals: impedance, light, frequency, and voltage. Design strategies for preparing ultrafast humidity sensors using nanomaterials are discussed with regard to physical parameters such as the nanomaterial film thickness, porosity, and hydrophilicity. We also summarize other applications that require ultrafast humidity sensors for physiological studies. This review provides key guidelines and directions for preparing and applying such sensors in practical applications. AdjustiE ratio) . Some reE ratio) ,24. YuanE ratio) . The paupatterns . If the patterns . For eva2 into and excreting CO2 from the body [2 and CO2 in ambient air  are PiO2 = 159 mmHg and PiCO2 = 0.3 mmHg, and those in expiratory air are PeO2 = 120 mmHg and PeCO2 = 27 mmHg, respectively [2 and CO2 can be used for evaluating metabolisms in the body. Specific gas components are used to find diseases in clinical breath analysis. The nitric oxide (NO) concentration on a ppb scale increases due to airway inflammation from asthma [Analysis of volume and gas components in breath air is another tool for understanding the health of subjects. The volume of breath air can be evaluated by several factors: tidal volume, inspiratory/expiratory reserve volume, and residual volume. The depth of breathing corresponds to the volume. These factors are measured by a spirometer and used to evaluate the functionality of the lung. The function of breathing is to exchange gas components with the blood through pulmonary alveoli: taking Othe body . In geneectively . The conm asthma . The ureExpiratory air has nearly saturated moisture (47 mmHg at 37 °C). By using humidity sensors in front of the human mouth/nose, it is possible to monitor the change in humidity between exhaled and ambient air. For example, Using nanomaterials for gas sensors is advantageous for obtaining high sensitivity to adsorbates because of their large surface-to-volume ratio and beneficial structure–property relationships  . Humiditt90, which is the time required for the signal to reach 90% of the total change. A similar indicator is t63, where the signal must reach 63% of the total change (corresponding to a time constant (1-1/e): e is the Napier’s constant). It is obvious that t90 is longer than t63, and, therefore, using t90 corresponds to a stricter evaluation of the response. When we apply the humidity sensors to respiratory monitoring, it is preferable that the response time τres and recovery time τrec are shorter than 60/2fR, where fR is the respiratory rate in bpm. For example, when fR is 10 bpm, we find τres, τrec < 3 s. As the human respiratory rate goes to 60 bpm in hyperpnea, a minimum τres, τrec of less than 0.5 s is desirable to monitor human respiration over the full bpm range. Thus, the development of humidity sensors with rapid response and recovery has been intensively studied. The number of publications relating to humidity sensors for respiratory monitoring has been increasing from 2017, as shown in The response and recovery times provide a standard to evaluate the quickness of the sensor response. 2 concentration by ≈2 s. In the data outputted by the humidity sensor, rising/plateau and falling phases corresponded to expiration and inspiration of the subject, respectively. It should be noted that the recovery time of these humidity sensors was not short enough, and therefore, there was no plateau region in the inspiration phase of the humidity signals. A pause in inspiration was not clearly resolved in these experiments. We first overview early works of monitoring respiratory signals using humidity sensors. In 1994, Muto et al. proposed an optical humidity sensor for monitoring breath condition . They usIn the field of sensor electronics, the microfabrication of humidity sensors is an effective strategy to improve their response and recovery times. Laville et al. proposed a capacitive humidity sensor using a polyimide layer as a pulmonary-function diagnostic microsystem in 2001 . In thisIn this review, we have classified the sensors by the types of output signals. Reading the impedance of a sensor is a classical method to detect humidity in air ,35,36. IZ is expressed as the impedance of the nanomaterial film Zn and the surface resistance of the adsorbed water layers Rw in parallel. The film impedance is composed of resistive and capacitive components (Rn and Cn). Therefore, we can use this structure as a humidity sensor if one of the three components  is dependent on humidity. It should be noted that an AC voltage is required to monitor the humidity dependence of the capacitive component as the capacitive impedance is inversely proportional to the applied frequency . The humidity dependence of the resistive components can be monitored with a DC voltage, which is simple to implement with external devices such as through the input/output pins of microcontrollers.An equivalent electrical circuit of the impedance-type sensor is shown in Rw is attributed to proton conduction through the water molecular layer on the surface. Proton hopping over the hydrogen bonds between water molecules due to an applied electric field is called the Grotthuss mechanism [Rw becomes smaller in higher humidity. This process is described in The humidity dependence of the impedance-type sensors can be described from the following physical origins. The humidity dependence of echanism ,38. The echanism ,40. As wRn and Cn. Charge carriers doped from water molecules to the nanomaterial surface or an induced electric field by the water dipole are able to modulate the conductance  and oxide [2 . ϵw = 80). By filling pores in the nanomaterial film (ϵ0 = 1) with water molecules, as shown in On the other hand, the capacitance of the nanomaterial is affected by the large dielectric permittivity of water  nanoparticle-based sensor chip and a microcontroller with a Bluetooth connection [To realize a monitoring system of respiratory rates based on humidity, fast-response humidity sensors with an insulating nanoparticle film were investigated . This sennection . The devnnection . Respirannection . From thOptical fiber humidity sensors measure the humidity level by detecting changes in light propagation through a waveguide. Unlike their electronic counterparts, these sensors are unaffected by electromagnetic interference because the sensing components do not include electronic circuits ,76. ThusAdvances in optical waveguide design and humidity-sensitive material components have resulted in improved sensitivities, response times, target humidity ranges, accuracy, repeatability, durability, and cost-effectiveness. These have been comprehensively covered by previous reviews ,83, inclRecently, the use of a humidity-sensitive coating on a photonic crystal fiber (PCF) to modify the propagation of light in the mid-section of an optical fiber was proposed by Lopez-Torres et al. . A 240 n2 nanosheets of 50 to 180 nm thickness on the polished part of an optical fiber. Both the average response and recovery times of the sensor were 85 ms. Similarly, Du et al. [2 nanosheets. The variation in transmitted power with the MoS2-coated ESMF was found to be 14-fold higher than that of a bare ESMF. The authors also reported that the sensor achieved fast response and recovery times of 66 ms and 2.4 s, respectively, over the range of 20–80%RH. Although the exact mechanism behind the humidity responsivity from MoS2 needs further investigation, it is believed that the adsorbed water molecules change the charge carrier concentration of the nanosheets, modifying the polarizability and thus the effective complex refractive index of the nanosheets.Changes in the transmitted power through an optical fiber decorated with 2D nanosheets have also been used to monitor the humidity. 2D nanosheets offer high surface-to-volume ratios that help the rapid adsorption of water molecules. When placed on polished or etched optical fibers, the 2D nanosheets couple with the evanescent field from the optical core, modifying the transmitted power through the fiber. Li et al.  preparedu et al.  reportedGraphene oxide is another class of 2D nanomaterials that exhibits strong humidity-responsivity. Jiang et al.  reportedWe note that most of the sensors above detect the transmitted signal through a fiber whose sidewall is functionalized by hygroscopic nanomaterials. Alternatively, one could also monitor the reflected signal from a fiber tip functionalized by humidity-responsive material. This approach was recently reported by Du et al.  where a mλ = 2dneff sinθ), where m is an integer, λ is the light wavelength, d is the characteristic dimension, neff is the effective refractive index of the constituent material, and θ is the angle between the surface and incident light. It is apparent from the equation that a change in either the refractive index, structure, or both will shift the wavelength. As the incident angle also affects the colors, it is generally fixed to accurately assess changes in the refractive index or structure. Colorimetric humidity sensors are those that indicate the humidity level in color. The key benefit is that their response can be easily discerned visually or measured quantitatively using a photosensitive device. Although it is possible for the sensor to electrically measure the humidity and optically display color separately through humidity-sensitive material and optoelectronic platforms, less energy-intensive approaches that output signals directly from ambient light are available via structural colors. Structural colors are determined by the coherent or incoherent interference of light by judiciously arranged structured units, and can be modified via changes in refractive index contrast between units and the surroundings, structural parameters, or both. To this end, nanostructures play an important role as their sizes are commensurate with the wavelength of visible light, providing conditions amenable for light interference. If the material is humidity-responsive, that is, susceptible to changes in the optical constant or structure through interaction with water molecules, one can realize color variations that form the basis for colorimetric humidity sensing. Some of the most popular methods to create such structural colors from the coherent interference of light involve the ordered assembly of nanostructures in 1D (lamellar gratings), 2D (stacked films), and 3D  or isolated nanostructures such as a single film . AssuminHydrogels are often used to produce a change in the structure as they swell or shrink in response to the input and output of moisture, respectively. However, the swelling and shrinking is temporally limited by the diffusion time of water molecules permeating throughout the gel and the deformation time of shifting chemically crosslinked polymer chains constituting the gel. Three-dimensional photonic crystals (PCs) made from opal hydrogels  and invemλ = 2(hdhn + ldln)sinθ, where hn, hd, ln, and ld are the refractive indices and thicknesses of the high- and low-index layers, respectively. Reports of such Bragg stacks composed of organic/inorganic hybrid materials [N,N′-methylene bis(acrylamide)) (P(AM-MBA)) nanogels and TiO2 nanoparticles forming textual mesopores. By optimizing the layer density, thickness, and stack number, the sensor exhibited a response time of ~0.5 s in response to humidity from respiration, marking an improvement over those from 3DPCs. Shifts in bright colors corresponding to wavelength differences of up to 242 nm were also found between 47 and 89%RH.Bragg stacks consisting of repeating units of high- and low-refractive-index layers, one of which is a hydrogel, swell and contract in one direction, that is, along the stack axis, decreasing the humidity-induced swelling and shrinking times considerably. Here, reflected light of each interface in the stack interferes coherently under conditions satisfying Bragg’s law for the stack: aterials ,94, inoraterials ,96,97, aaterials  have dematerials  recentlyπ. Practically, a single film is easier to prepare than multilayers comprising Bragg stacks.Further improvements in the response rates are possible by scaling down the multilayer stacks to a single layer because of smaller diffusion volumes and shorter adsorption-desorption paths for the water molecules. Structural colors from a single layer arise when the phase difference of light reflecting off the top and bottom interface of the film is an integer multiple of 2block-methylbutylene) (PSS-b-PMB) block copolymer film [Kim et al. used a 240 nm-thick partially sulfonated poly resonator is another method to give a frequency output. Here, an acoustic wave propagates along the surface between emitting/receiving interdigitated electrodes. The transmission wave is scattered by the adsorbates of the surface due to a change in mass, viscosity, and electrical permittivity. Therefore, monitoring the transmission loss and the velocity of the wave can provide the amount of humidity in air.The resonant frequency of mechanical oscillators is affected by adsorbates on the surface.  crystal . Cf is df per %RH), attributed to the mass loads of adsorbed water molecules. Chen et al. recently reported a QCM-based humidity sensor for respiratory monitoring under the nose [f was proportional to the RH in ambient. The response and recovery times of the fabricated sensor were 5.2 and 4.5 s, respectively. Rimeika et al. proposed the possibility of a fast-response SAW humidity sensor with a hematoporphyrin film in 2009 [QCMs have been classically used as a sensitive detector of surface adsorbates. For the rapid detection of humidity, Yao et al. evaluated the response of a QCM humidity sensor using a graphene oxide film with a thickness of 46 nm in 2011 . The resthe nose . They us in 2009 . The res in 2009 . PolyaniWith the exception of structural color-based sensors, the above-mentioned sensors need a voltage supply to operate. Supplying electrical power to sensors is one of the largest problems for IoT applications in various situations. Recently, self-powered systems have been increasingly attracting attention for the realization of sustainable information societies. In the case of respiratory monitoring, a battery-less system is promising for realizing wearables with enhanced flexibility and comfort for users.2 nanowire networks [2 nanowire networks/indium tin oxide coated on a flexible poly substrate. When the device is exposed to humid air, a gradient in the water concentration forms in the TiO2 networks. At the top side, the adsorbed water concentration is higher than that at the bottom side. This induces diffusion of ionic species related to water molecules (proton and hydronium ion) that subsequently generates a potential difference, as shown in 2 at 85%RH with which they realized battery-less respiratory monitoring. In this observation, a 1.2 × 1.2 cm-sized device was located 0.8 cm under the nose of the subject and generated a 20 mV output from a single breath. A respiratory rate up to 38 bpm was experimentally observed in the report. Li et al. reported respiratory monitoring by using a self-powered humidity sensor with a polypyrrole and melamine aerogel [One solution is to harvest an electrical voltage from the humidity in breath air. Power generation from ambient humidity has been recently proposed ,118,119.networks . The dev aerogel . The sen2 and reduced graphene oxide nanohybrid film. They used TENG to power the humidity sensor and monitored a respiratory rate up to 45 bpm.Another solution is to drive a fast-response humidity sensor by another self-powered system. Zhang et al. reported a flexible humidity sensor driven by a triboelectric nanogenerator (TENG) . TENG haAs a summary of this section, we describe the benefits (advantages) of each type of device based on its output and humidity sensing mechanisms . For impDespite the many advantages, there are also disadvantages to each type of device. For impedance and light-based outputs, one must precisely calibrate the device for volumetric measurements. For light-based reflective devices, it is obvious that information is inaccessible in dark environments. Frequency-type devices require large sizes commensurate with the resonant frequency (MHz to GHz), which hinder the portability of the device. The response of voltage-type outputs is relatively slow and further improvement in the conversion efficiency from humidity changes to electric power is required.τres) and recovery time (τrec). The reported values are dispersed from 100 to 0.01 s. Ultrafast response and recovery times shorter than 0.1 s are achieved by resistive-type [ive-type ,64,67, cive-type ,130, andive-type ,110,111.ive-type . This loThe red dashed lines represent the breathing periods of human respiration at the normal (12 bpm) and fastest (60 bpm) states. In order to track respiratory signals with sufficient temporal resolution, including breathing pose and depth, sensors having shorter response/recovery times than the threshold time are preferable. When a humidity sensor has a relatively longer response/recovery time than respiratory breathing periods, it is possible to only monitor respiratory rates but not respiratory rhythm and volume. To acquire only respiratory rates, we can use sensors with longer response/recovery times, as many reports have demonstrated. Güder et al. used digital filtering algorithms to extract respiratory signals from an impedance output of a paper-based humidity sensor . An impoTo shorten the response and recovery of the sensor, both adsorption and desorption processes need to be accelerated. In the field of gas sensors, a typical technique to enhance the desorption rate of adsorbates is to use a heater attached to the sensor. Recently, the room-temperature operation of gas sensors using nanomaterials was discussed for reducing the total power consumption of the sensor system . In a si(A) ThicknessHumidity-sensitive nanomaterials are usually prepared by depositing the colloidal solution on a sensor substrate. The thickness of the materials can be controlled within a few tens of nanometers by well-designed coating processes, such as dip-coating, spin-coating, and spray-coating . In agre(B) Porosityp/p0: ratio of a partial vapor pressure p and a saturated vapor pressure p0) and pore curvature r is Vm is the molar volume of water, R is the gas constant, and T is the environmental temperature. A reduction in the critical pressure of condensation becomes obvious when the size of the nanopore is less than 100 nm. This phenomenon can make the response irreversible. Therefore, small nanopores of a few nanometers are not favorable for achieving fast response and recovery.The porosity of a nanomaterial film is also critical for determining the response and recovery of humidity sensors. According to the Kelvin law, a nanometer-scale pore generally captures adsorbates in the pore. The critical pressure of water vapor condensation strongly depends on the curvature of a liquid–vapor interface ,141,142.(C) HydrophilicityWater molecules interact strongly with a hydrophilic surface. As this can increase the output sensitivity to humidity change, most of the reported nanomaterials in fast-response humidity sensors are hydrophilic such as oxide nanomaterials  causes the marked dilation of cerebral arteries and arterioles and increased blood flow, and hypocapnia  causes constriction and decreased blood flow. Thus, a capnometer is also an important tool for physiological studies on human behavior and brain activities. A spirometer uses an airflow sensor to measure the amount of breath air. The airflow sensor is typically a pressure sensor that calculates a volumetric flow rate from the pressure in the tube. By breathing air into the tube, the volumetric respiratory function of the lung  can be evaluated from the airflow signal. These methods are used for measuring the respiration of the subject at rest such as lying on a bed and sitting on a chair. A capnometer can also be used to measure respiration during exercise. In this section, we discuss the benefit to use fast-response humidity sensors for monitoring human respiration. 2, chest wall movements, and modulated cardiac activity. Currently, the most popular method is to measure chest wall movements with wearable sensor systems. The chest and abdomen synchronously move with inspiratory and expiratory phases during normal tidal breathing [In recent studies on respiratory monitoring during physical activity, contact-based methods have been popular. As Massaroni et al. summarized in 2019 , there areathing ,152,153.reathing ,155,156,reathing ,159,160,reathing  sensors Respiration monitoring using breath air does not require contact of the sensor to the torso. As the sensor signal is based on the physical properties of breath air: temperature, gas component, and humidity, the sensor is free from motion artefacts in principle. Thus, respiration during physical activity can be monitored accurately. In order not to interfere with the natural breathing of the subject, a comfortable and unobtrusive sensor system is recommended. Thermistors attached near the nose or the mouth have also been used to measure the temperature of breathing air.We note three reasons that hinder the practical use of respiratory monitoring with humidity sensors: an effect of the environment, a lack of volume measurements, and missing validation on a large number of human subjects. As the sensor signal is determined by the difference between humidity in breath air and ambient air, respiratory monitoring under saturated humidity (~100%RH) conditions is challenging. Ambient humidity and wind affect the respiratory monitoring by humidity sensors. Designing a device structure to suppress this environmental influence is required in the future. Currently, the volume of breath air is not quantitatively measured with a humidity sensor. Volumetric measurement is the next step for humidity-based respiratory observations. As Liu et al. commented , furtherIn addition to monitoring human respiration, fast-response humidity sensors can be used to monitor human physiological behaviors, as shown in this section . Duan etAnother important application for fast-response humidity sensors is in monitoring water vapor from the skin. Transepidermal vapor transport is an important process describing water loss in human physiology ,167. As 2/h for normal skin of young persons and 5.046 ± 0.882 mg/cm2/h for lesional skin of atopic patients. Li et al. reported the feasibility of studying skin moisture with a fast-response humidity sensor using a polymer gel electrolyte [TEWL is largely dependent on the skin condition. Rough skin accelerates the vaporization of water from the body surface. Yamamura et al. reported the relationship between skin disease and the amount of TEWL measured by a monochromatic refractometer . The valctrolyte . They coIn this review, we have overviewed recent developments on ultrafast humidity sensors made with functional nanomaterials for monitoring human respiration. Respiration is an important gauge of overall health, physical/mental activity, and emotions of human subjects. There are four main indicators: breath rate, rhythm, volume, and gas components. These indicators are informative for preparing precision healthcare solutions. As such, the development of advanced sensing systems is becoming an increasingly attractive field in materials science. We classify ultrafast humidity sensors according to four types of outputs: impedance, light, frequency, and voltage. For each type, we introduce the physical origin of humidity sensing and associated works marking pioneering and recent developments. The benefits using ultrafast humidity sensors are discussed for the application of respiratory monitoring. The response and recovery times of reported humidity sensors are compared, where the shortest times have reached 8 ms. Based on these reports, we discuss general design considerations for achieving fast response speeds, one of which is the tradeoff between response/recovery time and sensitivity. One of the key benefits of using humidity sensors in respiratory applications is that the sensors can be porfigureand noninvasive. Currently, ultrafast humidity sensors are used for observing respiratory rate and rhythm. Finally, we describe other areas of use besides respiration monitoring: voice recognition, noncontact user interface, and skin evaluation. So far, research in these areas using ultrafast humidity sensors has been limited to feasibility studies. It is important to carry out trials on a large number of human subjects to bring ultrafast humidity sensors closer to practical real-world applications.


Large-scale variation in ecological parameters across Madagascar is hypothesized to drive varying spatial patterns of malaria infection. However, to date, few studies of parasite prevalence with resolution at finer, sub-regional spatial scales are available. As a result, there is a poor understanding of how Madagascar’s diverse local ecologies link with variation in the distribution of infections at the community and household level. Efforts to preserve Madagascar’s ecological diversity often focus on improving livelihoods in rural communities near remaining forested areas but are limited by a lack of data on their infectious disease burden.To investigate spatial variation in malaria prevalence at the sub-regional scale in Madagascar, we sampled 1476 households  from 31 rural communities divided among five ecologically distinct regions. The sampled regions range from tropical rainforest to semi-arid, spiny forest and include communities near protected areas including the Masoala, Makira, and Mikea forests. Malaria prevalence was estimated by rapid diagnostic test (RDT) cross-sectional surveys performed during malaria transmission seasons over 2013–2017.Indicative of localized hotspots, malaria prevalence varied more than 10-fold between nearby (< 50 km) communities in some cases. Prevalence was highest on average in the west coast region , situated near protected dry deciduous forest habitat. At the household level, communities in southeast Madagascar (Mananjary district) were observed with over 50% of households containing multiple infected individuals at the time of sampling. From simulations accounting for variation in household size and prevalence at the community level, we observed a significant excess of households with multiple infections in rural communities in southwest and southeast Madagascar, suggesting variation in risk within communities.Our data suggest that the malaria infection burden experienced by rural communities in Madagascar varies greatly at smaller spatial scales  and that the southeast and west coast ecological regions warrant further attention from disease control efforts. Conservation and development efforts in these regions may benefit from consideration of the high, and variable, malaria prevalences among communities in these areas.The online version contains supplementary material available at 10.1186/s12889-021-11090-3. Malaria remains a major public health concern in Madagascar. There are an estimated more than 2 million cases per year  and malaIn Madagascar, existing malaria data are available from periodic national-level surveys, routine reporting from government clinics, and a small number of field studies. For national surveys, data are usually sparse at the local level and then aggregated into large epidemiological zones (‘faciès épidémiologiques’) that coarsely correspond to areas of the country with different rainfall patterns and elevation , 5. A weGiven that national surveys are under-powered at the local, sub-regional level, and no recent surveys are available for many areas, there is a need for new empirical data on the spatial distribution of parasite prevalence across Madagascar. We analyze a newly generated data set from sampling 1476 households divided among five ecologically distinct regions of the country to explore variation in prevalence (by RDT) within and between rural communities.A secondary motivation is that high rates of deforestation, largely driven by expansion in the land used for subsistence agriculture by impoverished rural populations –9, threaAdditionally, rural communities in these forest-periphery areas may face elevated risk as they often have poorer access to health infrastructure and increased exposure to malaria mosquito vectors , 15.In this study, we aim to explore fine-scale spatial variation in malaria prevalence in human communities in Madagascar, and we highlight a subset of the studied communities with relevance for conservation programming. In practice, the observation of spatial variation is relevant for control efforts as it allows prioritizing areas of greatest burden where a small portion of the population contributes a disproportionately large share of infections . These gthe high rainfall northeast (NE) containing humid rainforest, secondary forest and flooded lowland rice paddy agriculture (Maroantsetra district),the slightly lower rainfall and more heavily deforested southeast (SE) (Mananjary district),the more arid south and southwest (SW) containing dry spiny forest-thicket and secondary grasslands (Toliara II district),the seasonally dry west coast (WC) containing dry deciduous forest and secondary grasslands (Morombe district),the high elevation central plateau (CP) containing wooded grassland-bushland mosaic and terraced agriculture (Amoron’i Mania region).Sampling was performed at 6–7 sites each for five ecological regions in Madagascar. Madagascar features extreme ecological diversity as a result of hypervariability in bioclimatic variables such as rainfall, elevation, and plant communities , 19. TheSee Fig. The regions sampled represent a majority of the ecological regions that have been described in Madagascar , 22, butOf the five regions sampled, communities in three of the regions reside near (1-15 km) or within buffer zones for protected terrestrial and marine natural areas that are globally acknowledged biodiversity hotspots . NE siteWithin a region, we hypothesize that (i) communities nearer urban centers have greater access to care and prevention resources, thus motivating the inclusion of some study communities that are more distant, and (ii) that coastal communities may have different vector ecologies due to, for example, sandier soils or winds, and that more mobile fishing communities may have different patterns of exposure. As a result,rural communities were stratified by two variables in order to attempt to capture the ecological and epidemiological variation present in an area: proximity to the coast and proximity to urban infrastructure (e.g. the nearest town with road access). We defined roads as paths regularly (i.e. daily) passable by public or private vehicles. Lacking census data or maps for these remote areas, communities were defined as sites with clusters of approximately > 30 households visible by satellite imagery. Due to the settlement pattern of rural communities in these areas, household clusters, their peripheral agricultural (mainly rice) fields, and surrounding cleared or deforested lands were easily distinguishable in satellite imagery. Within strata, for each region, communities were randomly selected so as to have communities that were coastal (< 3 km from the coast) or inland (> 3 km from the coast), as well as communities that were within 15 km of an urban center or more than 15 km from an urban center. In the central plateau (CP) region, this inland area is far removed from the coast so by definition there were no coastal communities. The urban centers of the regions studied were Maroantsetra (NE), Mananjary (SE), Toliara (SW), Morombe (WC), and Ambositra (CP). As communities were intentionally stratified to capture variation that may be present within regions, the communities selected are likely not a random sample of variation within a region.Details of enrollment, ethical approval, and study design for the larger epidemiological studies from which these malaria prevalence analyses were an extension are explained by Golden et al. 2017 , Golden The central plateau (CP) differs notably from the other regions: Malaria transmission is known to be lower  and tempAt each site, approximately . The percentage of individuals within households sampled is similar to that obtained in other studies of household prevalence in Madagascar [Individuals of all sexes and ages within an enrolled household were recruited. Comparing reported household size to the number of individuals with a malaria RDT result and matching age and sex data showed that 86% of individuals within the recruited households were sampled for a total of 7117 individuals analyzed here  dagascar . Individdagascar .After obtaining informed consent or assent, a blood sample was obtained by veni-puncture or finger prick. Blood sampling procedures were performed by local physicians and phlebotomists – either medical personnel from the local district health office or a trained team member from local partner development organizations with long-standing ties to local communities. Aside from individual consent or assent, efforts to obtain community-wide understanding of the purpose and protocol of blood sampling included public meetings with the community as a whole, with local officials, with traditional community leadership, and with individual family groups  rapid diagnostic test (RDT) which uses the antigens HRP2 and pLDH. These RDTs and markers have been assessed in Madagascar previously for the detection of nfection . For RDTTo explore the distribution of infections among households, we performed simulations to compare the observed frequency of multiple infections in a household to that expected under a scenario where infections were distributed randomly. If infections are distributed randomly in a community, the expected number of households with a given number of infections  depends solely on household size  and each individual’s independent probability of infection . A significant excess or rarity of households with multiple infections in comparison to the random expectation would suggest that some variable acting at the household level significantly affects the probability of infection.P)n where P is the probability an individual is infected and n is the number of individuals in the household. As a first approximation, we simulated a population where n = 4.8  for each household. All else equal, this provides an estimation for the proportion of households expected to have 1+ infections given the regional prevalence.We first compared the observed household prevalence to the household prevalence expected in a simulated population with a prevalence set equal to the observed regional average prevalence. The probability of a household having at least one infection is given by 1 –  using 10,000 bootstrap resamples to assess significance. Simulations were performed for each community, using the observed number of individuals in each household sampled in each community. The observed number of infections in a community were randomly re-assigned to households (with the proportion of households of a given size matching the observed distribution in that community). The null hypothesis is that the observed number of households with a given number of infections will be similar to that generated when randomly reassigning infections in a simulated community with a matching distribution of households of a given size. Random reassignment was repeated 1,000 times to determine the probability that an observed distribution of infections among households  could be expected by chance. Communities where the observed pattern was expected in less than 5% of the permutations were considered to deviate from the null hypothesis.Analyses were performed in R version 4.0.2 . For theDetails are described in Golden et al. 2017 , Golden n = 247 and 151 individuals) to over 30% at seven sites  (Supplementary Table 1). Prevalence was, as expected, lowest in the higher elevation, cooler central plateau (CP region). The highest observed prevalence was in the southeast sites (Mananjary district), where prevalence was 40.8% at site SE.05 near the Pangalana Canal in Mahatsara Atsimo commune and was 46.2% at site SE.06 in Antsenavolo commune. Despite having two sites with prevalence over 40%, the southeast region also contained three sites with prevalence less than 10% . In the west coast region, among rural communities in Morombe district, prevalence was consistently high and was above 30% in five of the six sampled communities. For comparison, estimated regional average prevalence among children under 5 for the east coast as a whole per the 2016 national MIS was 9.0, and 8.8% for the west coast [We observed substantial variation in malaria prevalence by RDT between communities within the studied regions Fig. . For theIn several cases, large variation was observed between pairs of geographically close sites Fig. . For exaAtsimo Andrefana, a large administrative region in western and southwestern Madagascar, is worthy of mention as it contains areas within both the semi-arid spiny forest southwest (SW) ecological region (sampled in the Toliara II district) and the dry, deciduous forest west coast (WC) ecological region (sampled in the Morombe district). The pattern of consistently high prevalence seen among the rural sites in the Morombe district (WC region) near Mikea National Park and the Mangoky-Ihotry Protected Harmonious Landscape differs greatly from that seen in the Toliara II district (SW region) near Ifaty forest Fig.  indicatiFig. S2).As sampling was not performed simultaneously at the study communities within a region, the length of time between sampling dates may contribute to the variation seen between communities. We observed that communities sampled within 1–2 weeks often differed as much as the sites sampled more than 2, 4 weeks apart, indicating time difference is unlikely to explain the difference in prevalence observed between communities (Supplementary Table 1). In communities in the southwest, boys aged 10–15 contributed more infections than any other age-sex class. This differs from the pattern typically seen in endemic settings in sub-Saharan Africa where infections are generally most common in younger children and decrease quickly after age 5 [n = 6) observed. For sex, there were small differences in the percent of infections that were male or female among some age groups in some regions, but no consistent pattern was apparent. Overall, the percentage of sampled individuals that were male (46.3%) was similar but slightly less than those that were female  of e female .7%, where female .5% slighSupplementary Table 1). Alarmingly, a substantial proportion of households in the SE  and the WC  were burdened with 4 or more infections at the time of sampling.In order to explore the distribution of infections among households within a community, we determined the proportions of households found with 0, 1, 2, 3, or 4+ individuals testing positive for malaria by RDT by region  , for exan = 22–85 per site). Illustrating this, even for sites where there was wide variation in the proportion of individuals infected in a household, the household distribution of infections observed could be produced by random chance  with household clustering had lower prevalence than a majority of the other sites sampled in the region such that a small number of households were responsible for a large fraction of infections. For site SE.02 for example, just 8% of the households contributed over 60% of the RDT positive results observed. Policy implications are clear – if the factor(s) responsible for such an uneven distribution of infection can be identified then interventions targeted to vulnerable households can be deployed.Multiple factors may drive the observed variation in prevalence at the household and community level, including variation in the abundance of mosquito vector larval sources, variation in exposure to bites of adult mosquito vectors, and variation in access to prevention and care. However, the observation of communities and households with elevated prevalence, termed hotspots, are potentially confounded by several factors. Foremost, temporal fluctuations in infection rates, while common in malaria, cannot be inferred from cross-sectional sampling . As a reFig. S2). Second, sampling at the regions with the highest observed prevalence occurred after  the typical seasonal peaks in incidence [Supplementary Table 1) and the time periods of sampling were not atypical as inferred by clinical reporting data [However, multiple lines of evidence suggest that the large variation observed between sites within a region is not an artefact of sampling alone. First, nearby sites that were sampled within 1–2 weeks of each other  were observed to vary greatly . RDT positivity among older children was consistently higher than for younger children Fig. , indicatIn comparison to other observations of fine-scale variation in malaria infection, hotspots of stable high transmission down to the single household  level were observed in coastal Kenya , 53 and From sampling 1476 households across multiple regions of Madagascar, we provide new insight into the heterogeneous landscape of malaria infection in rural Madagascar. Large site-site variation in communities in some areas and evidence of a nonrandom distribution of infections among households indicate the presence of local foci of elevated malaria risk. Such local hotspots merit increased control efforts that are likely to intersect with ongoing in situ conservation and development programming priorities.Additional file 1: csv contains the malaria rapid diagnostic test outcome data as a line list. Supplementary Table S1.csv contains sampling dates and malaria prevalence data by site and household. Figure S1. shows the distribution of household size . Figure S2. shows the difference in prevalence as a function of the length of time between sampling dates All files and the R code used to process the data for this study are also publicly available at the GitHub repository: https://github.com/labmetcalf/madagascar_malaria_prevalence_2021.


We report a novel Mn-Co-Ni-O (MCN) nanocomposite in which the p-type semiconductivity of Mn-Co-Ni-O can be manipulated by addition of graphene. With an increase of graphene content, the semiconductivity of the nanocomposite can be tuned from p-type through electrically neutral to n-type. The very low effective mass of electrons in graphene facilitates electron tunneling into the MCN, neutralizing holes in the MCN nanoparticles. XPS analysis shows that the multivalent manganese ions in the MCN nanoparticles are chemically reduced by the graphene electrons to lower-valent states. Unlike traditional semiconductor devices, electrons are excited from the filled graphite band into the empty band at the Dirac points from where they move freely in the graphene and tunnel into the MCN. The new composite film demonstrates inherent flexibility, high mobility, short carrier lifetime, and high carrier concentration. This work is useful not only in manufacturing flexible transistors, FETs, and thermosensitive and thermoelectric devices with unique properties but also in providing a new method for future development of 2D-based semiconductors. Modern transistor manufacturing is based on p-n interface phenomena developed in semiconductor science and technology. The migration and separation of electrons/holes in electronic devices are dependent on carrier lifetimes in the devices. Quantum tunneling at the contact interface only involves <2 nm in interface thickness, but the leakage current caused by this tunneling can result in unreliability of the circuit in the manufacture of transistors below 10 nm. Over the past 70 years, metal/semiconductor contacts were divided into two types: Schottky contact and ohmic contact. The formation of a Schottky barrier is based on the mechanism of carrier electron diffusion and thermionic emission, and its properties are determined ideally by the work functions of metal and semiconductor and, in practice, often also by interface defects or reaction , 4. We f−6 S/m and that of graphene is about 106 S/m. We measured the MCN work function (WF) and that of graphene by two methods—Kelvin probe (KP) and UPS—and take the average between the two measurements  and are high recombination contacts. The conductivity of MCN at room temperature is about 10urements . The aveμm thick single-layer films as a function of graphene content . The contact geometry is shown in Figure μm thick) change from p-type through intrinsic type to n-type with an increase in the graphene content from 1% to about 13%. The trend of change of resistance and conductivity type of films with different thicknesses is the same, but the critical content of graphene needed to give different conductivity types varies according to thickness. Although the graphene content is less than MCN, the most conductive component in the composite materials is graphene because of its extremely large surface area as well as its much higher conductivity.Hall effect measurements show that the MCN-graphene composite films . When the graphene content is increased beyond 12.5%, the graphene phase becomes continuous and the properties are like a conductor. The carrier type and concentration of the composite films can be modulated by the graphene MCN ratio and can vary over a wide range. Note that all tests are completed by pressing composite contact particles into thin films at 5-30 MPa with different thicknesses. In addition to the graphene/MCN ratio, the specific conductivity data is also related to the formation pressure and thickness of the thin films. Although there will be fluctuation errors in the test data, the change trend of each sample is the same.The  mobility of the graphene/MCN was measured and found to be 91.4 cm12-1013 times per second, but because of the high electron mobility in graphene, electron scattering is strongly reduced. However, the electron space density of graphene is so large that diffusion is not the main effect ..22].−31 kg and the reduced Planck constant ħ = 1.05457266 × 10−34 j.s. From the work function difference between MCN and graphene, the potential well difference can be assumed to be V0 − E ≈ 0.3 eV approximately. Assuming a potential well a = 1 nm, for general electronic calculation, we getThe formula of quantum tunneling transmittance ismGe = m − m0, mGe is approximately 5.5 × 10−6 m0.The electron velocity is 1/300 of the speed of light, so the effective dynamic mass, a = 1 nm,When the potential well width a = 3 nm, 5 nm, and 10 nm, the velocity of graphene electrons entering MCN will drop very rapidly and the effective mass of electrons will increase, but TRECTe increases by 310, 334, and 1.0 times at the different values of a. Although these assumptions include errors, it can be found that the tunneling transmittance at the contact interface between graphene electrons and MCN can be ~102 times bigger than that of electrons from most other materials .(3)/(2) is 85.6 times. If 3+/Mn4+ ratio increases from 2.8 to 5.2 or more as the graphene concentration is increased.However, the real size of MCN nanoparticles is about 10 nm, the actual action width scale of graphene electron tunneling action is above the MCN 10 nm diameter, and the electrons migrate into the whole volume structure of the MCN nanoparticles. For the various Mn valences  in the MCN structure, electrons fill holes resulting in an overall reduction in Mn oxidation state. As discussed above, our XPS data show that the Mn4+ to Mn3+ , the graphene will donate electrons to the high WF MCN . This results in two main effects: that of electrons donated to the MCN and holes remaining in the graphene. The effect of electrons on the MCN is to reduce MnThe MCN/graphene composite has many unique characteristics , 26. By As an example, 2-103 times, and carrier concentration can be adjusted between 1010 and 1019/cm3.Because the electron movement in the composite involves tunneling and the graphene's electron transition process is extremely rapid , 27–32, In conclusion, we have developed a method to effectively regulate p- and n-type semiconductivity of graphene-MCN nanocomposite, making it possible to easily fabricate graphene-based functional semiconductor devices. Unlike traditional semiconductor devices, transistor charge transport is dominated by highly mobile electron movement at the Dirac points on the 2-dimensional surface of graphene. It is anticipated that availability of both p- and n-type graphene-based materials will spur development to fabricate a new generation of semiconductor devices.2+, Co2+, and Ni2+ salts with aqueous ammonia and the precipitate fired at 900–1000 K in air for several hours. MCN inorganic semiconductor devices can work stably for a long time with high precision, sensitivity and reliability at high temperatures (up to 780°C), high voltage, and mechanical stress. The transition metal oxide Mn3-xCo2-yNiO8-x-y  has a spinel structure. The radii of Mn4+, Mn3+, and Mn2+ ions are close to those of Co3+, Co2+, and Ni3+ ions, respectively, which conforms to the Hume-Rothery rule. The MCN nanocrystal structure is compact and stable. MCN is usually a p-type semiconductor with negative temperature coefficient of resistivity in the range of -2%/K to -6%/K. At room temperature, the carrier concentration of these oxides is about 109/cm3. It has very low carrier mobility, about 10−5 cm2/V·s; its intrinsic conductivity is close to that of insulators [3+ ions become Mn4+. In the normal temperature equilibrium state, MCN nanocrystals actually have fixed ionic chemical compositions of Mn4+/Mn3+/Mn2+ at room temperature (25°C): Mn4+ and Mn3+ ions account for more than 80% and are the main components. The size of the individual MCN nanoparticles is about 10 nm  [2/g. Single-layer graphene has an electron energy band structure containing Dirac cones with a zero bandgap. This band structure leads to an effective mass of the two-dimensional surface electrons in graphene that tends to zero, resulting in their very fast velocity of nearly 1/300 of the speed of light. One of its striking properties is the very high mobility ~ up to 150,000 cm2/V·s (for single-layer graphene at room temperature) and good electrical conductivity. Current research on semiconducting graphene focuses on heterojunction formation and elemental doping. The direct realization of functional devices by utilizing the Dirac cone characteristics is a topic which is of interest to many researchers [Single-layer graphene  was prod3) . The speearchers , 35–42.Graphene and MCN semiconductor powders were weighed according to different mass ratios, (G : MCN between 1 : 5 and 1 : 200 mass ratios). The packing density of graphene, ~0.001 g/ml, is about 1,000 times that of MCN nanopowder (about 1.1 g/ml). MCN powder was added to graphene slowly, and the mixture was ground in a planetary mill for over 36 hours. The average thickness of graphene covering the MCN particles varied from 0.35 to 60 nm depending on the graphene concentration . The com