Clinical practice needs a common parameter that can provide an early, reliable estimation of the outcome of sublingual immunotherapy (SLIT) in an upcoming pollen season. We investigated whether the conjunctival provocation test (CPT) can predict the beneficial outcome of SLIT in patients with allergic rhinoconjunctivitis after 4 weeks of treatment.We conducted two separate prospective, randomized, double-blind, placebo-controlled, multicenter trials. Adults 18–75 years of age received placebo or SLIT tablets containing tree or grass pollen allergoids and underwent CPTs. Participants receiving SLIT were divided into two groups  according to their CPT reactions after 4 weeks of treatment. These two groups were compared with regard to clinical outcome parameters  assessed during the pollen season for the 14-day (tree) or 30-day (tree/grass) peaks and for the entire 60-day seasons. Participants’ global evaluations of therapy after completing treatment were also compared.P = 0.0001; grass: P = 0.003), the total combined score for the 14-day (P = 0.017) and 30-day peaks (P = 0.042) as well as the rhinoconjunctivitis total symptom score assessed for the 14-day peak (P = 0.024) were significantly lower in the nonreactive group of the tree pollen trial. In the grass pollen trial, the nonreactive group rated their SLIT treatment significantly better (P = 0.019).The tree pollen trial randomized 188 participants; 182 participants were evaluable, 76 of whom received SLIT and were suitable for this post hoc analysis. The grass pollen trial included 90 participants; 82 participants were evaluable, 44 of whom underwent SLIT. Comparing SLIT participants who reacted to the CPT after 4 weeks  with those who ceased to show a reaction   and 2013-002129-43 ) and the German Federal Ministry of Health (Paul-Ehrlich-Institut). The efficacy and safety of sublingual immunotherapy (SLIT) in patients with allergic rhinoconjunctivitis have been assessed up to now in several trials –5, incluAnother important tool used to assess the efficacy of SLIT is the conjunctival provocation test (CPT). The CPT was developed decades ago  as a meaStudies attempting to determine whether a correlation exists between preseasonal CPT findings and symptoms occurring during the following pollen season have reported heterogeneous results , 20. KruBased on the findings by Kruse et al., it would be desirable if clinical practice had a generally accepted parameter that is able to predict symptom severity even before a first course of preseasonal immunotherapy has been completed. Therefore, we performed a post hoc analysis of two prospective, randomized, double-blind, placebo-controlled trials to investigate whether the CPT can accomplish this task as early as 4 weeks after initiating SLIT.This post hoc analysis is based on two prospective, randomized, double-blind, placebo-controlled, multicenter, two-armed, phase III trials in participants with tree or grass pollen–related allergic rhinoconjunctivitis. The tree pollen trial included 22 sites, 18 of which enrolled participants in the study; the grass pollen trial included 23 sites, 15 of which randomized participants. Both trials were carried out in Germany with the approval of the Medical Ethics Committee of the North Rhine Medical Council  and 2013-002129-43 ) and the German Federal Ministry of Health (Paul-Ehrlich-Institut) [Adults aged 18–75 years with a history of at least 2 years of tree or grass pollen–induced allergic rhinitis and/or allergic rhinoconjunctivitis with or without seasonal, controlled allergic asthma were enrolled in the studies after having given written informed consent. Other inclusion criteria were: specific-IgE reactivity to tree or grass pollen (CAP-RAST ≥ class 2 (0.70 kU/L)), a positive screening skin prick test (SPT) , and a positive response to conjunctival provocation testing at both the screening and the inclusion visits. Participants with cosensitizations could be included if they did not suffer from typical symptoms caused by coseasonally prevalent allergens. Furthermore, specific IgE and SPT results for coseasonally prevalent allergens had to be lower than those for tree or grass pollen, respectively. The main exclusion criteria were: previous immunotherapy within 5 years prior to screening or ongoing immunotherapy for any allergen, predominant perennial allergic rhinitis, partly controlled or uncontrolled asthma, significant medical conditions, pregnancy, breastfeeding, lack of adequate contraception, and intake of contraindicated concomitant medication.® birch–alder monomeric allergoid tablets at a daily dosage of 1000 units of allergen (UA)  or placebo tablets according to the randomization schedule.In all, 188 participants were randomized in the tree pollen study. During the treatment phase of at least 84 ± 14 days, participants received either LAIS® grass tablets at a daily dosage of 1000 UA  or placebo tablets for 20 weeks. Participants were instructed to place 1 sublingual tablet per day under the tongue and to let it dissolve for 2 min before swallowing. All participants were supplied with a blister package of an oral antihistamine  as rescue medication for potentially appearing local side effects of the study medication.A total of 90 participants were randomized in the grass pollen study. They received either LAISComputer-generated randomization lists had a random block size of 4 in the tree pollen study and 8 in the grass pollen study. Participants were allocated to the next random treatment number in consecutive, ascending order. Blinding of the participants and the investigators was ensured by the identical shape, size, weight, color, taste, and smell of the study medication and its packaging. Labeled and sealed envelopes, one for each randomization code, contained the corresponding treatment allocation and were stored in a secure place at the respective study center. The envelopes were only to be opened in the case of a participant-related event requiring unblinding.A CPT was performed at the screening visit (Visit 0), the inclusion visit before the first intake of study medication Visit 1), after 4 weeks of treatment (Visit 3), and after 12 weeks of treatment in the grass pollen study (Visit 4) or at the end-of-study visit  in the tree pollen study (Visit 5), respectively. ALK-lyophilized SQ provocation testing solutions  in increasing concentrations containing 100, 1000, and 10,000 SQ/ml were applied to one of the participant’s eyes. Simultaneously, control solution was administered to the other eye to rule out false-positive reactions. This control procedure was carried out each time a new allergen concentration was tested. The investigator documented the participant’s conjunctival reaction 10 min after applying each concentration as described by Dogan et al. . Reactio, after 4Diaries were handed out to the participants after 4 weeks of treatment in the tree pollen study (Visit 3) and after 12–20 weeks of treatment in the grass pollen study (Visit 4 or Visit 5)  was calculated taking into account the use of oral antihistamines, levocabastine eye drops, and nasal corticosteroids according to the following escalation scheme: Step 1 = antihistamine  1 × 10 mg, maximal daily score of 6; Step 2 = Step 1 plus additional levocabastine (eyedrops) 2 × 1 drop per eye, maximal daily score of 9; Step 3 = Step 2 plus additional beclomethasone  2 × 0.05 mg/nostril, maximal daily score of 18; only for asthmatic participants: long-acting b2-agonists 2 × 1–2 inhalations, 9 points in addition to the daily rescue medication score (Steps 1–3).For each day of the assessment period, RTSS and TRMS were added together, yielding a daily total combined score (TCS) . All indThe primary efficacy endpoint of the tree pollen study was the TCS for the 14-day peak of the birch pollen season (TCS 14), whereas the primary efficacy endpoint for the grass pollen study was defined as the 30-day peak of the grass pollen season (TCS 30). Secondary assessments for both studies were the remaining TCS, RTSS, and TRMS for the time periods mentioned above.“Well days” were also determined, being defined as those days in the entire birch/grass pollen season having a maximum symptom score of 2 and no rescue medication use according to Durham et al. , 13.Global evaluations of the therapies were carried out at the end of the studies. Participants were asked to evaluate the efficacy of their respective treatment, their satisfaction with the treatment, and whether they would recommend it to others based on a 4-point scale from 0 to 3, with 3 being the best rating.U test was performed to identify differences between the two groups with the level of significance set at 5%. The analysis of the data sets was performed using SPSS by IBM for Windows .The primary outcomes of these studies have been reported elsewhere , 22. In A total of 235 participants were screened for inclusion and exclusion criteria in the tree pollen study; 188 eligible participants were randomized .Demographic characteristics were similar between the two treatment groups within each study. Comparing participants who were nonreactive or reactive to CPT after 4 weeks of SLIT, demographic characteristics were homogeneous for both groups Tables , 4. ConcAccording to the DWD, the peak of the 2014 birch pollen season with a constant high pollen concentration of stage 3 on a scale from 0 to 3  started on 28 March 2014 .The peak of the 2014 grass pollen season with a constant pollen concentration of stage 2  began on 21 or 22 May 2014 in all regions of Germany.n = 76) in the tree pollen study were divided into nonreactive and reactive groups according to their CPT results after 4 weeks of treatment. Of these SLIT participants, 17 (22.4%) did not show a reaction to CPT after 4 weeks (P = 0.0001), whereas 59 participants (77.6%) did.For the post hoc analysis, SLIT participants (P = 0.017). Likewise, the TCS for the 30-day peak (TCS 30) was significantly lower in the nonreactive group than in the reactive group (P = 0.042). The TCS for 60 days was also lower in the nonreactive group than in the reactive group but did not reach statistical significance (P = 0.104) (Table SLIT participants who did not show a reaction had a significantly lower combined score for the peak of the birch pollen season (TCS 14) 8.50) than those who still showed a reaction to CPT (0 than thP = 0.024). Differences between the nonreactive and the reactive groups of SLIT participants could also be observed for the RTSS 30 (P = 0.053) and for the RTSS 60 (P = 0.065). However, these differences were not statistically significant , as well as in the TRMS 30 for the group without reactions (3.44) in comparison to the group with reactions (5.29) (P = 0.103) , RTSS 30 (P = 0.081), and RTSS 60 (P = 0.067) values for nonreactive SLIT participants (Table P = 0.019).Also, when SLIT participants who ceased to show a reaction to the CPT after 4 weeks of treatment were compared with placebo-treated participants, there was a strong trend towards lower RTSS 14 (ts Table . For then = 44) in the grass pollen study were divided into nonreactive and reactive groups based on CPT results after 4 weeks of treatment. Of these SLIT participants, 9 (20.5%) did not react to the CPT after 4 weeks (P = 0.003) whereas 35 (79.5%) still did.Similar to the tree pollen study, SLIT participants (P = 0.065), and in the RTSS 60, with a score of 2.84 in participants without reactions and 4.09 in those with reactions (P = 0.146). These results demonstrate a further trend towards fewer symptoms in the nonreactive group  could be observed with respect to the self-reported efficacy of the treatment between participants with reactions (2.00) and those without reactions (2.67), once again in favor of the nonreactive group  could be observed as well as a trend towards a lower RTSS 30 (P = 0.086) in the nonreactive, actively treated group  .In both trials of this post hoc analysis, nonreactivity in the CPT was observed in 22.4% (tree) and 20.5% (grass) of actively treated participants after 4 weeks of SLIT intake. Nonreactive SLIT participants in both trials showed trends towards lower symptom and rescue medication scores during the peak of the respective pollen season, more well days, as well as a better evaluation of efficacy, greater satisfaction, and more recommendations of SLIT treatment than did participants still reacting to conjunctival provocation. Similar trends could be observed when comparing nonreactive SLIT participants to placebo-treated participants.Heterogeneous results have been reported with respect to a possible correlation between preseasonal CPT and rhinoconjunctivitis symptoms during the pollen season , 20. OurOn the contrary, Radcliffe et al.  were notFor SLIT, a treatment duration of approximately 3 years is usually recommended in order to achieve a therapeutic effect in patients with allergic rhinoconjunctivitis , 28. OurP = 0.0042), which could be maintained at 2 (P = 0.0203) and 4 months (P = 0.0007) with decreasing average RTSS (ARTSS) results at each challenge in the SLIT group. In contrast to Horak et al., we performed a CPT instead of provocation in an allergen challenge chamber, but even so, we found a similar early onset of nonreactivity in the CPT after only 4 weeks of SLIT intake. In the subsequent pollen season, the anticipated beneficial outcome was confirmed.Horak et al.  aimed toEarly onset of an immunotherapy effect was also found in another trial conducted by Ott et al. . In a raThere were also placebo-treated patients who did not react to conjunctival challenge after 4 weeks of treatment. This effect was already described by Gloistein et al. . They foOne strength of the analysis presented here is that two prospective, double-blind, randomized, placebo-controlled, multicenter, phase III studies yielded similar results demonstrating a similar trend—independently of each other. Furthermore, in contrast to Kruse et al., who performed a posttrial observation, our analysis was carried out based on data gathered according to the study protocol, and no further data had to be collected to complete the analysis.An important limitation, on the other hand, is the fact that we conducted a post hoc analysis. By doing so, more value may be ascribed to the outcome of an unplanned analysis than is warranted . MoreoveOur data suggest that the cease in SLIT patients’ reactions to preseasonal CPT reflects their individual outcomes during the subsequent pollen season. Carrying out the CPT after only 4 weeks of SLIT can reveal early responders to therapy. Thus, a prediction concerning treatment efficacy and possible symptoms during an upcoming pollen season can already be made at an early stage of treatment with SLIT. This estimation could help increase compliance in patients receiving preseasonal SLIT and help physicians improve their patients’ individual therapeutic strategies for the upcoming pollen season by knowing whether to prescribe rescue medication as needed or on a daily basis.


Plasmodium falciparum prevalence is used to understand the past and chart a future for malaria control in Kenya by confidently highlighting areas within important policy relevant thresholds to allow either the revision of malaria strategies to those that support pre-elimination or those that require additional control efforts.Spatial and temporal malaria risk maps are essential tools to monitor the impact of control, evaluate priority areas to reorient intervention approaches and investments in malaria endemic countries. Here, the analysis of 36 years data on Plasmodium falciparum parasite prevalence (PfPR) surveys undertaken in Kenya between 1980 and 2015 were assembled. A spatio-temporal geostatistical model was fitted to predict annual malaria risk for children aged 2–10 years (PfPR2–10) at 1 × 1 km spatial resolution from 1990 to 2015. Changing PfPR2–10 was compared against plausible explanatory variables. The fitted model was used to categorize areas with varying degrees of prediction probability for two important policy thresholds PfPR2–10 < 1% (non-exceedance probability) or ≥ 30% (exceedance probability).PfPR2–10 from 21.2% (ICR: 13.8–32.1%) in 1990 to 2.6% (ICR: 1.8–3.9%) in 2015. The most significant decline began in 2003. Declining prevalence was not equal across the country and did not directly coincide with scaled vector control coverage or changing therapeutics. Over the period 2013–2015, of Kenya’s 47 counties, 23 had an average PfPR2–10 of < 1%; four counties remained ≥ 30%. Using a metric of 80% probability, 8.5% of Kenya’s 2015 population live in areas with PfPR2–10 ≥ 30%; while 61% live in areas where PfPR2–10 is < 1%.5020 surveys at 3701 communities were assembled. Nationally, there was an 88% reduction in the mean modelled Kenya has made substantial progress in reducing the prevalence of malaria over the last 26 years. Areas today confidently and consistently with < 1% prevalence require a revised approach to control and a possible consideration of strategies that support pre-elimination. Conversely, there remains several intractable areas where current levels and approaches to control might be inadequate. The modelling approaches presented here allow the Ministry of Health opportunities to consider data-driven model certainty in defining their future spatial targeting of resources.The online version of this article (10.1186/s12936-018-2489-9) contains supplementary material, which is available to authorized users. Variations in the intensity of malaria transmission in countries requires tailoring of interventions appropriate to the corresponding level of transmission. The World Health Organization Global technical strategy for malaria 2016–2030 [Plasmodium falciparum survey data were used to provide an updated cartography [Malaria risk mapping in Kenya is not new. Maps of malaria risk were developed as early as the 1950s based on the length of the presumed malaria season . In the tography . The firtography . This matography , future tography  from 201However, harnessing the full value of information on malaria prevalence in time and space to provide an understanding of the fine temporal and spatial resolution changes in malaria risk at national or sub-national scales and provision of probability metrics for important programmatic policy relevant thresholds has not been attempted. Such approaches are often limited by a paucity of input data over time; Kenya however, is a country with a rich history of malaria surveys and provides a unique opportunity to explore patterns of malaria endemicity since 1990. Spatio-temporal methods were applied to understand the changing landscape of malaria transmission in Kenya since 1990 and used the statistical certainty in these models to provide insights into the future investments in control during an era of maximizing value for money.For the first time in Kenya, a MBG framework was used to provide statistical certainty to identify areas that represent policy relevant thresholds, allowing the government to make informed choices on a more efficient future control strategy.2 and lies on the equator across the great East African Rift Valley, extending from Lake Victoria to Lake Turkana and further south-east to the Indian Ocean ttlement . This dettlement .Fig. 1Kemalaria” and “Kenya”; searches of national ministry of health archives in Nairobi and other major centres; reviews of post-graduate theses at three major universities; school-based surveys undertaken to support the NMCP 2009–2011 [A detailed description of the assembly of a database of malaria surveys carried between January 1980 and December 2015 in Kenya is presented elsewhere , 15. The009–2011 ; nationaP. falciparum infection, methods used to detect the infection and every location detail provided in the original source including the name, administrative unit, and coordinates, where available. Data were classified as points if they were individual villages, communities, schools or a collection of communities and covered an area of at most 5 km2. Areas covering > 5 km2 were classified as wide-areas. Global positioning systems (GPS) cluster coordinates collected during sample household surveys were used to re-aggregate household survey data, to increase the sampling precision by combining clusters of small sample sizes, while maintaining the 5 km2 criteria.For each survey, details were extracted on the start and end of survey dates (month and year), age ranges (lowest and highest), sample size, numbers reported positive for To provide a precise longitude and latitude where coordinates were not available, a variety of methods were used including reported GPS coordinates, other national digital gazetteers of populated places , schools and health facilities –19. All P. falciparum prevalence across Kenya between 1990 and 2015. More specifically, let S denote the random effects used to account for unmeasured spatio-temporal risk factors for malaria and let Z be unstructured random effects accounting for the unexplained variation within communities. Conditionally on S and Z, the counts of positive tests for P. falciparum were assumed to follow mutually independent binomial distributions with number of trials N, corresponding to number of sampled individuals, and probability of a positive outcome p at location x (3701) and year t (1990–2015) given bymAand MA are the minimum and maximum age among the sampled individuals at a location x. In carrying the spatio-temporal predictions, mAand MA were set to 2 and 10 to standardize to a single age range of 2–10 years (PfPR2–10) conventionally used for malaria risk mapping [A geostatistical modelling framework –22 was u mapping , 24.S were modelled as a stationary and isotropic Gaussian process with spatio-temporal correlation function given byϕ and ψ are scale parameters which regulate the rate of decay of the spatial and temporal correlation for increasing distance and time separation, respectively; ||x − x′|| is the distance in space between the locations of two communities, one at x and the other at x′; finally, |t − t′| is the time separation in years between two surveys.The spatio-temporal random effects PfPR2–10 were obtained from the fitted model over a 1 by 1 km regular grid covering the whole of Kenya, for every year between 1990 and 2015, exported and mapped using ArcMap 10.5 . Predictions to each of the 312 months since January 1990 have not been attempted as there was insufficient monthly-gridded data to allow for such analysis.The model parameters were estimated using Monte Carlo maximum likelihood implemented in the PrevMap package  in the RS= 0 for all x and t); (Step 3) compute the 95% confidence interval using the resulting 1000 variograms at a predefined set of spatial distances and time separations; (Step 4) compute the variogram using the residuals from a non-spatial logit-linear model as done in step 2 but using the original data and if this falls within 95% envelope from (Step 3), then, the adopted spatio-temporal correlation was compatible with the community parasite survey data.The fitted spatio-temporal correlation function was validated using the following variogram-based algorithm using R software environment (version 3.4.1): (Step 1) simulate 1000 data-sets under the fitted model; (Step 2) for each simulated data-set, compute a variogram using the residuals from a non-spatial logit-linear model  representing the mean difference between the observed and predicted values, and the mean absolute error (MAE) representing the average magnitude of the errors of the absolute differences between the predictions and actual the observations [Cross-validation was also undertaken by holding out a 10% random sample of the survey data points selected between 1990 and 2015 to assess the predictive performance of the model. The following were computed: the correlation between observed and predicted rvations .PfPR2–10 between 1990 and 2015.Malaria prevention and disease management milestones since 1990 in Kenya were defined by the literature, previous reviews –30 and mPfPR2–10 were calculated by averaging the 1 × 1 km predictions among populated areas per county for the three most recent years of survey data, 2013–2015. Areas that were represented as temperature unsuitable for P. falciparum transmission were assigned values of 0% PfPR2–10 [Given the importance of county level government resource allocation for malaria, mean annual county level estimates of PfPR2–10 .PfPR2–10 may lead to policy decisions that do not allow for the certainty of the PfPR2–10 predictions [PfPR2–10 below or above certain policy relevant thresholds. The choice of these thresholds should be guided by reduction targets set by the global community, malaria epidemiology, and local goals for the country of interest.Certainty of model predictions forms an important metric for NMCPs by justifying decisions on sustained, or changing control intervention policy. MBG allows for the quantification of uncertainty, which might arise from inadequate survey input data (suggesting further sampling needs) and inherent variability in small area prediction. Classifying areas into different endemic levels purely based on predicted dictions . Future There are no formal international guidelines to countries on how thresholds of malaria risk might inform a stratified intervention response. Here, two policy relevance thresholds have been selected that might serve as valuable criteria within the Kenyan context. Areas with sustained low malaria prevalence where prevalence lies below 1% (non-exceedance probability-NEP) as an indication of pre-elimination , that isPfPR2–10 < 1% (NEP), and probability that an area has a PfPR2–10 ≥ 30% (EP) across the study period and summarized for the three most recent consecutive years (2013–2015), formally expressed asl is the prevalence threshold. A NEP close to 100% indicates that PfPR2–10 is highly likely to be below the threshold l; if close to 0%, PfPR2–10, is highly likely to be above the threshold l; if close to 50%, PfPR2–10, is equally likely to be above or below the threshold l, hence corresponding to a high level of uncertainty. Areas likely to have a prevalence of ≥ 30% were defined by setting l at 30% in the preceding equation and calculating EP asThe fitted spatio-temporal model was used to compute the probability that an area has 2 at 3701 unique locations covering malaria parasite examinations of over 578,281 blood samples, between 1980 and 2015  in Butula, Siaya county and Kinango, Kwale county; and the lowest values outside of areas unable to support transmission located in Tarbaja, Wajir county in 2011 (0.01%) : 13.8–32.1%) in 1990 to 2.6% (ICR 1.8–3.9%) in 2015 , declining slightly between 1998 and 1999, then continued at this level until 2003. The largest decline (81%) in the national mean PfPR2–10 occurred between 2003  and 2007  and remained generally low thereafter. PfPR2–10 slightly rose slowly from 2011 to 2014, following which it declined again in 2015 reaching the lowest national mean PfPR2–10 of 2.6% (ICR 1.8–3.9%) recorded during the 26-year period of observation  coincided with poor population coverage of vector control [PfPR2–10 occurred during a period of continued use of SP, relatively poor population coverage of insecticide treated bed nets delivered on a subsidized cost-recovery basis [PfPR2–10 during 2014, but does not alone explain the subsequent decline in 2015 and the slight rise in PfPR2–10 prior to IRS suspension PfPR2–10 since 2003 was not equal everywhere , in the semi-arid North Eastern and central regions, have declined dramatically, to very low levels (< 1%) after 2006  and North Eastern  regions wholly and partially in Eastern , Rift Valley  and Coastal (Lamu) region encompassing 44.3% (20.1 million) of Kenya’s 2015 population . Over the 26 years, reductions in prevalence were observed in these areas and by 2013–2015 no counties were classified as hyper-holoendemic. However, declining PfPR2–10 was less marked over the 26 years of observation in these counties compared to countries, which started at lower transmission intensity. Four counties  had an averaged mean PfPR2–10 of ≥ 30% between 2013 and 2015  and ≥ 30% (EP)  Fig. . The maxEP) Fig. . Areas iEP) Fig. .Fig. 5CoPfPR2–10  descriptions of risk nationwide. A large swathe of the country is occupied by areas predicted to have a PfPR2–10 less than 1% with a probability of at least 80%, covering approximately 68%  of the populated areas and 61% (27.8 million people) of Kenya’s 2015 population. At a higher probability (≥ 90%) at least half (51%) of Kenya’s populated areas, occupied by 53% of Kenya’s population has a prevalence of less than 1%. In such populations where the infection prevalence over the period 2013–2015 is < 1%, should be an indication for possible migration to a pre-elimination phase by the NMCP [The heterogeneous nature of the NMCP . In thesthe NMCP .PfPR2–10 observed in Nairobi (1.1%), might be due to a combination of locally acquired and imported malaria [PfPR2–10 2013–2015 , compared to low transmission, these areas encompass 3.9 million people, 8.5% of Kenya’s 2015 population. The counties affected by this elevated level of PfPR2–10 transmission are Kilifi, Kwale, Migori, Homa Bay, Kisumu, Siaya, Kakamega, Vihiga, and Busia (Figs. There continues to be areas of Kenya, which over the last 26 years appear to be intractable to current coverage levels, and approaches to vector control. Areas that on average continue to support ia Figs. , howeverSpatio-temporal geostatistical models of sparse malaria input data have used multiple, dynamic  or long-The novelty of non-exceedance probabilities will allow the NMCP in Kenya, and other malaria endemic countries, to implement control measures that are congruent to malaria risk. This may involve re-orientation of resources allowing optimal utilization of funds in a time of competing health agendas and limited resources. The global momentum is to stratify national malaria control because a blanket cover of intervention is no longer appropriate in increasingly heterogenous settings . The worP. falciparum infection prevalence over time. The declines in transmission intensity were heterogeneous in nature over the 26 years. However, the reductions were witnessed before the implementation of optimized treatment and vector control. Areas confidently classified to have prevalence < 1% calls for a possible migration to control strategies suited for a pre-elimination phase. Conversely, in the areas which over the last 26 years seem to be intractable to current levels of vector control coverage will require expansion of vector control and use of other innovative approaches to control both the parasite and vector.Kenya has made substantial progress in reducing Additional file 1. Summary of malaria parasite surveys data used in the analysis.Additional file 2. The temporal and frequency distribution of communities surveyed for malaria infection between 1980 and 2015 in Kenya.Additional file 3. The locations of communities surveyed for malaria infection in Kenya between 1980 and 2015.Additional file 4. Spatio-temporal structure validation.Additional file 5. Spatio-temporal variation of the standard errors.Additional file 6. Model predictive performance assessment through cross validation.Additional file 7. The Monte Carlo maximum likelihood parameters from the fitted spatio-temporal geostatistical model.


Ultrafast heat transport in nanoscale metal multilayers is of great interest in the context of optically induced demagnetization, remagnetization and switching. If the penetration depth of light exceeds the bilayer thickness, layer-specific information is unavailable from optical probes. Femtosecond diffraction experiments provide unique experimental access to heat transport over single digit nanometer distances. Here, we investigate the structural response and the energy flow in the ultrathin double-layer system: gold on ferromagnetic nickel. Even though the excitation pulse is incident from the Au side, we observe a very rapid heating of the Ni lattice, whereas the Au lattice initially remains cold. The subsequent heat transfer from Ni to the Au lattice is found to be two orders of magnitude slower than predicted by the conventional heat equation and much slower than electron–phonon coupling times in Au. We present a simplified model calculation highlighting the relevant thermophysical quantities. Heat transport in ultrathin metal layers is important for potential applications in optical‐magnetic switching, but difficult to access experimentally. Here, the authors use ultrafast X‐ray diffraction to directly probe and explain unexpected time‐dependent transport behavior in Au–Ni nanolayers. The energy flow in metal multilayers following optical excitation attracted particular attention in the context of heat-assisted magnetic recording15 and all-optical magnetic switching18. The role of temperature in optically induced femtosecond demagnetization is intensely discussed, particularly with regard to multipulse switching scenarios19. Two- or three-temperature models (TTMs) are often used to fit the experimental observations20. The microscopic three-temperature model (M3TM)20, which uses Elliot–Yafet spin-flip scattering as the main mechanism for ultrafast demagnetization is often contrasted against superdiffusive spin transport21. Such electron transport is closely related to ultrafast spin-Seebeck effects23, which require a description with independent majority and minority spin temperatures. The heat flow involving electrons, phonons, and spins has been found to play a profound role in ultrafast magnetization dynamics25. The description of the observed dynamics in TTMs or the M3TM are challenged by ab initio theory which explicitly holds the nonequilibrium distribution responsible for the very fast photoinduced demagnetization27. The presence of multiple subsystems , e.g., in ferromagnetic metals28, poses a formidable challenge for experimental studies of their coupling and thermal transport on ultrafast time scales when these subsystems are generally not in equilibrium with each other29. Temperature dynamics in metal films are typically monitored using optical probe pulses via time-domain thermoreflectance (TDTR)12. This technique has been a workhorse of nanoscale thermal transport studies, but experiences significant limitations when applied to ultrathin multilayers with individual layer thicknesses falling below the optical skin depth, which are in the focus of ultrafast magnetism research32. Optical probes are generally sensitive to electronic and lattice temperatures, although in some cases the lattice temperature13 or the spin temperature24 may be deduced. In order to understand the thermal energy flow, it is highly desirable to directly access the temperature of the lattice, which provides the largest contribution to the specific heat. Ultrafast X-ray diffraction is selectively sensitive to the crystal lattice, and material-specific Bragg angles enable measurements of multiple layers even when they are thinner than the optical skin depth and/or buried below opaque capping layers35. The lattice constant variations of each layer can be measured with high absolute accuracy, making it possible to determine the amount of deposited heat in metal bilayers that was debated recently32. The great promise of ultrafast X-ray diffraction (UXRD) for nanoscale thermal transport measurements and ultrafast lattice dynamics has already been demonstrated in experiments with synchrotron-based sources36. However, limited temporal resolution of these experiments (~100 ps) only allowed to study heat transport on a relatively slow (nanosecond) time scale and over distances >100 nm. Ultrafast nanoscale thermal transport research will greatly benefit from femtosecond X-ray sources. While free electron laser facilities are in very high demand, an alternative is offered by laser-based plasma sources of femtosecond X-rays38, which lack the coherence and high flux of a free electron laser but are fully adequate for UXRD measurements40. As an example, a recent experiment on 6 nm thick Au nanotriangles39 confirmed the 42. For similar fluences ultrafast electron diffraction reported TC43.Ultrafast heating and cooling of thin metal films has been studied extensively to elucidate the fundamentals of electron–phonon interactionsdAu = 5.6 nm and dNi = 12.4 nm grown on MgO. By monitoring the dynamics of the lattice constants of Au and Ni, we find that the Ni lattice fully expands within about 2 ps, while the Au lattice initially remains cold even if a significant fraction of the excitation light is absorbed by the electronic subsystem in Au. The Au lattice then heats up slowly, reaching the maximum temperature about 80 ps after the optical excitation. The observed thermal relaxation of the bilayer structure is two orders of magnitude slower than 1 ps predicted by the heat equation and also much slower than the usual electron–phonon equilibration time 42. We explain this surprising result in a model  on the characteristic timescaleNote that the two temperatures in this model are the temperature of the Au lattice, Due to the small film thickness and the rapid electronic heat diffusion, we do not assume any gradient in the temperatures of each film. At about 1 ps after excitation we define the initial conditions as TAu and the exponential decay of TNi converge around 80 ps. Deviations at longer times originate mainly from heat transport into the MgO substrate, which is not included in the model (dashed lines).This very simple model ))5)) capt15 and helicity-dependent49 switching in ferrimagnets and two different timescales observed in the demagnetization of transition metals44 or rare earths51, precise calibration of the lattice temperature is crucial. We are convinced that the direct access to the lattice, the layer-specific information for layers thinner than the optical skin depth, the conceptual simplicity of the arguments and the experimental geometry make the paper particularly useful for comparisons to previous44 and future work on optical manipulation of spins.We have demonstrated the power of UXRD in probing nanoscale heat transport in an ultrathin metallic bilayer system which is relevant to current magnetic recording developments such as heat-assisted magnetic recording. To understand the all-optical-−10 mbar. We measured the layer thicknesses dAu = 5.6 nm and dNi = 12.4 nm of the investigated sample by X-ray reflectivity. The 24 lattice planes of Au yield a symmetric (111) Bragg reflection  Ni peak at 25.92° originating from 70 lattice planes. The lattice strains εNi,Au(t) = −cot(ϑ(t))Δϑ(t) perpendicular to the sample surface are directly retrieved from the time-resolved Bragg-peak positions ϑ(t) . Since the angle between the pump pulse and the Bragg-reflecting X-ray probe pulse is fixed in the setup, we take into accout the modified angle of incidence of the optical pulse of 44° (51°) with respect to the surface normal for the Ni (Au) reflection to calculate the incident fluence of 9 (8) mJ/cm2 and an absorbed fluence of 3 (2.9) mJ/cm2 for our bilayer system using a matrix formalism, which also yields the absorption profiles at 400 and 800 nm excitation shown in Fig. 1c52. The above values are for 800 nm excitation, and the 400 nm data in Fig. Ni/Au stacks with different Ni and Au thicknesses were grown by molecular beam epitaxy onto a MgO(001) substrate at 100 °C. The MgO(001) substrates were degassed at 350 °C for 10 min. The pressure during growth never exceeded 6ion Fig.  at ϑ = 1(t) Fig. 5839,40. 53. In cubic materials with (100) surface orientation the ratio of the observed ultrafast (uf) strain and the strain εeq = αeq(T)ΔT along the (100) direction calculated from equilibrium value (eq) is ε/εeq = αuf(T)/αeq(T) = 1 + 2C12/C11 = 2.2 for Ni and would be 2.6 for Au. For the Au (111) cubic crystal surface, the above equation is still valid if the elastic constants are calculated in the rotated coordinate system, in which the x-axis is [111]. We find that the newly obtained C11 and C12 coincidentally yield the same correction factor of 2.2 for Au (111) as for Ni (100).The effective expansion coefficient TNi,Au(t) from our TTM as input parameters for a full thermo-elastic simulation using the udkm1Dsim toolbox, which are represented as solid lines in Fig. 4a59. For convenience, Fig. The pronounced compression and expansion of the Au layer see Fig.  clearly σ, which drives the observed strain waves. A very useful concept uses the macroscopic Grüneisen coefficient Γe and Γph, which relate the energy densities ρQ to the stress σ = Γρ. While in Au the electronic Grüneisen constant 7. For our analysis the distinction of the origin of pressure in Ni is not very relevant, since the redistribution of energy from electrons to phonons only increases the stress by 15%. In Au the electron pressure is negligible in our bilayer system, since due to the large electronic specific heat of Ni and the subpicosecond equilibration among the electrons, all the energy is accumulated in Ni. The ab initio modeling discussed in connection to the recent UXRD study on Fe points out that both electron–phonon coupling parameters and phonon Grüneisen coefficients depend on the phonon mode29. While in that study the scattering of X-rays from individual phonon modes selected by the scattering geometry may require a mode-specific analysis, we believe that measuring the lattice expansion via a Bragg-peak shift looks at an average response of the lattice to all phonon modes, and hence a mode-averaged analysis is reasonable if there is no selective excitation of modes with extraordinarily different Grüneisen coefficients.Several recent ultrafast X-ray diffraction and electron diffraction experiments on thin metal films have highlighted two contributions of electrons and phonons to the transient stress The data that support the findings of this study are available from the corresponding authors on reasonable request.


In the monsoon months of July-August, δD reduces by 53.0 ± 6.5‰ albeit the specific humidity increases by 3.4 ± 0.2 g kg−1. Using joint observations of specific humidity and isotope ratio in lower troposphere, we discern the moisture sources over NE India in pre-monsoon and monsoon seasons and posit the role of transpiration in continental recycling during pre-monsoon season.Variations in isotopic composition of water vapor in the atmosphere is an important indicator of the processes within the hydrological cycle. Isotopic signature of water vapor and precipitation can be helpful in partitioning evaporation and transpiration fluxes. It is well known that transpiration from forested regions supplies a significant amount of vapor to the atmosphere in monsoon and post-monsoon seasons. Here, we utilize observations from Tropospheric Emission Spectrometer (TES), Atmospheric Infra-Red Sounder (AIRS) and simulation models to ascertain that transpiration is dominant in the forests of Northeast India (NE) during pre-monsoon season. Our results show an increase in δD of 78.0 ± 7.1‰ and in specific humidity of 3.1 ± 0.2 g kg Northeast (NE) India receives highest rainfall during the Indian Summer Monsoon as compared to other parts of Indian subcontinent2. Forests of NE India cover 64% geographical area of the region3 and are regarded as the northern most limit of tropical rainforests in the world4. This region is a major biodiversity hotspot of India and is home to a wide variety of plant species5. Previous studies have considered NE India as a distinct macro-region within the Indian landmass6. This region is dominated by dynamic weather that is influenced by water vapor in the atmosphere arising from oceanic as well as terrestrial sources like vegetation, open water bodies etc. Orography of the region also plays a major role in causing precipitation2. Evapotranspiration (ET) is an important component of hydrological cycle that plays a vital role in surface hydrology, vegetation dynamics and energy balance at the terrestrial surface. Continental recycling of ET in NE India is well understood for the post monsoon period7 but not for the pre-monsoon and transition periods. Goroshi et al.8 found a strong positive correlation between ET and insolation in NE India implying that insolation and ambient air temperature are major stress factors affecting ET in this region. Correct quantification of evaporation and transpiration in any hydrological model is necessary to reduce uncertainties in the hydrological budget. In this regard, studying the isotopic signature of water (H216O and HDO) can provide evidence about the dominant prevailing process amongst evaporation and transpiration10.Indian subcontinent receives maximum rainfall from June to September11. Wright et al.12 reported that Amazon rainforests play an important role in destabilizing the atmosphere resulting in early onset of wet season. Forests of NE India are vulnerable to changing climate conditions5. Monsoon precipitation over the region has been on a continuous decline since 1950s13. Breitenbach et al.14 studied the stable isotopes in precipitation during 2007–08 in Meghalaya, NE India and proposed that there is a shift from northwestern continental moisture source (during winter) to southern marine source (during Indian summer monsoon). Western Disturbance (WD) influences precipitation during winter months with moisture originating from Mediterranean Sea and mid-west Atlantic Ocean15. This phenomenon is more pronounced in Western and Central Himalayas as compared to Eastern Himalayan region16. During summer months, when most of India is dry, NE India receives a significant amount of precipitation. Nor’westers typically occur in summer months and are characterized by thunderstorms that develop due to intensive atmospheric vortices and convective processes17. Intensive localized evaporation from open water bodies like rivers and lakes provides moisture for generation of these thunderstorms, which typically cover distances of less than 50 km18. However, large scale moistening of the lower troposphere during pre-monsoon months driven by transpiration from forests has not been well studied. Precipitation based studies conducted earlier18 only provide Nor’westers as possible explanation of pre-monsoon precipitation.Rainforests form a crucial component of global carbon and hydrological cycles19. However, lack of well-distributed measurements and difficulty of modeling the latent heat fluxes usually result in poor constraints on evaporation and transpiration components at catchment scale. Measurement of stable isotope ratios of oxygen and hydrogen is used to decouple evaporation and transpiration20. Evaporation causes the resulting vapor to be depleted in heavier isotopes whereas transpiration at steady state does not cause fractionation21. Analysis of stable water isotopes in rivers and lake water across the globe have been used to partition various fluxes within the hydrological cycle22. Isotopic analysis of precipitation has been widely carried out to understand moisture sources26. Using observations of δD and δ18O in precipitation and comparing them with Global Meteoric Water Line (GMWL) provides information about source and transport of moisture over a region28.Conventional models and techniques for discharge estimation have helped constrain river discharge fluxes to the ocean32. Satellite sensors like SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT-1 and Tropospheric Emission Spectrometer (TES) onboard Aura have provided valuable long-term records of isotopic ratio in atmospheric water vapor34. Satellites can measure concentrations of HDO and H216O in atmosphere, but limitations in sensitivity of current instruments do not allow the retrieval of H218O. As a result, observations of D/H ratio are only available from space-based platforms. However, with joint measurements of isotopic composition of water vapor and specific humidity, it is possible to retrace the history of condensation and mixing processes associated with an air parcel35. Retrieval of HDO from SCIAMACHY uses the shortwave infrared channels and provides total columnar concentration34, whereas TES retrieval uses the thermal infrared channels to provide vertical profile of HDO33. Isotopic composition of water vapor differs with that of precipitation due to different sources and hence, remote sensing gives new information about the hydrological cycle, which is unattainable from surface. We carried our study with two major objectives: (a) to determine the source of moisture in lower troposphere over NE India during pre-monsoon months and (b) to study the role of this atmospheric moistening in convection process resulting in pre-monsoon precipitation.Satellite-based remote sensing of water isotopes in atmosphere is an effective tool to understand certain hydrological processes36 (LULC) for 2009. The size of bounding box was chosen to accommodate atleast two adjoining passes of Aura satellite. Different forest types cover major area of this region, along with the flood plains of Brahmaputra in Assam and Bangladesh. This region receives majority of rainfall during the Indian Summer Monsoon with normal onset date between 1–10 June every year37. The climate of this region is sub-tropical and it receives some of the highest rainfall in the world. Mawsynram in Meghalaya, India is the wettest place on Earth and lies within the study area.Study was carried over NE Indian region with bounding box 22°–28° N and 88°–100° E. Figure 2O profiles38 for the period 2006–2009 during which the data points were most dense. TES measures HDO and H2O concentrations in the water vapor at 17 pressure levels during ascending (~1345 LT at equator) and descending (~0145 LT) passes of Aura satellite with footprint of 5.3 km × 8.4 km33. Individual TES observations have large biases as reported in earlier literature40. Worden et al.33 noted that TES data in its initial phase showed bias of ~5% in estimation of HDO, which accounted for ~50‰ δD bias for values near zero and smaller bias error for values much below zero. However, with subsequent revisions of TES data, these biases have been accounted for and the same have been incorporated in the provided Nadir Lite v6 product used in our present study41. Errors such as smoothing errors (given by averaging kernel), systematic and noise errors etc. are inherent to the algorithm for retrieving trace gases from space-based spectrometric techniques42. TES retrieval algorithm performs joint retrievals of multiple species including HDO and H2O to reduce these errors33 in the measurement and a full quantification of individual errors is provided in the product file. Although individual data points may be biased, averaging data points over a larger grid and increasing the number of independent observations reduces the uncertainty. Point observations by TES have a precision of ~10–15‰ which reduces to 1–2‰ when averaging over larger regions44.We have used TES Level 2 (Nadir-Lite-Version 6) retrievals of HDO and Het al.45 where average cloud optical depth was <0.4  and degree of freedom of signal >1. These criteria allow only cloud-free observations to be considered, as cloud cover reduces the vertical resolution of TES observation39. Providing these control filters on TES dataset affects the number of valid points but does not influence the qualitative nature of results obtained12. Averaging kernel represents the sensitivity of log(R) at a given pressure level to that of ratios at other pressure levels, where R is the ratio of concentration of HDO to H2O. Figure 39. Observations over Bay of Bengal (bounding box 8°–18° N and 85°–92° E) show better sensitivity in the 908–825 hPa range  modelled monthly wind velocity at 850 hPa was used to determine general wind direction during different months47.Atmospheric InfraRed Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU) based TqJoint product of specific humidity (q) and temperature (T) was used in the study48 (EVI) at 0.05 degrees was obtained for the period 2006–2009. Data from Global Network of Isotopes in Precipitation49 (GNIP) provided by International Atomic Energy Agency (IAEA) and isotopic data for rainwater samples collected by Breitenbach et al.14 at Cherrapunji were used to determine the isotopic signature of precipitation in the study area. Rainfall data was obtained from Global Satellite Mapping of Precipitation50 (GSMaP) provided by JAXA at 0.1° × 0.1° resolution and converted to daily rainfall. Daily latent heat flux data was obtained from NCEP/NCAR reanalysis dataset provided by NOAA/ESRL.MODIS 16-day Enhanced Vegetation Index2O is usually defined in parts per thousand (per mil- ‰) relative to a standard  and given by delta (δ- notation) expressed by the formula51,2O)VSMOW is 3.11 × 10−4. Individual TES retrievals with average cloud optical depth <0.4 and degree of freedom of signal >1 were used for analysis. All valid TES retrievals within the study area were binned over 5-day periods to generate pentad-averaged vertical profiles of water vapor volume mixing ratio (VMR) and δD. TES derived δD at different pressure levels were converted to a single pressure-weighted value using Eq.  within the study region was available at monthly time-steps from the GNIP database along with data points collected by Breitenbach et al.14 at Cherrapunji during 2007–08. Monthly data from GNIP with number of rain samples >1 were considered. Similarly, we estimate the isotopic composition of an evaporative source from Bay of Bengal by computing the pressure-weighted average of TES observations from 908 to 825 hPa over the region.To get an idea about the isotopic signature of soil moisture, data from GNIP and isotopic data for rainwater samples collected by Breitenbach 53:35α is the temperature dependent fractionation factor. Similarly, curves for reversible moist adiabatic process (with precipitation efficiency zero) is represented as35αe is the equilibrium fractionation factor. For plotting Rayleigh curves, α is set as αe. The initial point for simulating ideal Rayleigh and moist adiabatic processes have been taken at 80% of the specific humidity of the evaporative source. These curves have been modelled for free troposphere with δD = −250‰ only.When two vapor parcels mix, the resulting parcel’s mixing ratio is the weighted average of their individual mixing ratios. Isotopic composition of the mixed parcel is given byMean monthly specific humidity (AIRS) and δD (TES) for all co-located observations within the study area for period of January-August between 825–619 hPa were used to generate q-δ plots. Seasonal means for January-February, March to May and June to August were also plotted with 1σ variability of each distribution. Individual q-δ data points for these seasons were plotted along with seasonal means and slope of q- δ distribution was computed using a simple linear fit.e) given by Eq. . If θLarge positive value of this temperature difference implies a greater potential for moist convection to occur.MODIS 16-day composite EVI at 0.05-degree resolution was analyzed for mid-March and mid-May and averaged across four years. Area averaged mean of EVI was computed for all four years to show its temporal variation at 16-day intervals. Daily rainfall data from GSMaP was binned over 5-day periods and area average was computed for each pentad. NCEP/NCAR Reanalysis daily latent heat flux data was converted to pentad-averaged ET by dividing it with the latent heat of vaporization of pure water at 20 °C.37 (corresponding to 151–160 Julian Day). However, during the study period, the onset dates were 27–28 May in 200637, 8–10 June in 200755, 31 May-9 June in 200856 and 25 May – 28 June in 200957. It is evident that there is an increase in δD values in pre-monsoon period near the surface and same signal propagates in the mixing layer between 750–619 hPa. Water vapor volume mixing ratio . Shillong data showed the highest uncertainty in δ values which were recorded during 1966–1978. Observations at Cherrapunji coincided with our study period and provided the best available estimates. Using these observations, we can assume transpired vapor to have δD ~ −20‰.Joint measurements of q and δD can help us ascertain whether increase in lower tropospheric humidity during pre-monsoon period happens due to local transpiration or advection from Bay of Bengal or sources from western and central India. Oceanic source of vapor is depleted in heavy isotopes due to fractionation occurring during phase change53. Mixing curves  and δ2  were plotted. Initial specific humidity values of the two end-members were assumed to be q1 = 1.5 g kg−1 and q2 = 20 g kg−1. These are close approximations to conditions in NE India during the pre-monsoon season. When two vapor sources mix, there is a possibility of condensation and subsequent precipitation leading to further fractionation and more depleted vapor. Dashed lines in Fig. TES observations in 908–825 hPa levels showed that vapor arising from Bay of Bengal was relatively depleted δD~−70‰) during Apr-May. Therefore, the two vapor sources (i.e. transpired and ocean-evaporate sources) are isotopically distinct. When vertical mixing of these two sources occurs with the free troposphere, the isotope ratio of final vapor will be different. To understand this, we plot the theoretical mixing curves of these two sources of water vapor with the free troposphere0‰ during−1) and δD (78.0 ± 7.1‰) compared to January-February. Error terms represent the standard error in estimation of mean whereas error bars in Fig. Figure Figure TES and AIRS observations show a large scale moistening of the lower troposphere dominated by transpiration. The role of this increased vapor on atmospheric stability can be understood by studying the equivalent potential temperature at different pressure levels. Negative vertical gradient of equivalent potential temperature in atmosphere results in conditional instability that can trigger convection process. In other words, high positive values of conditional instability parameter increases the chance of moist convection. Figure −1 in Jan-Feb to 6.8 ± 0.2 g kg−1 in Apr-May. q-δ plots of co-located TES-AIRS observations point to a transpiration dominated vapor source mixing with the lower troposphere. Pre-monsoon atmospheric moistening could explain the large-scale atmospheric instability over the region resulting in increased chance of convection. The technique of interpreting q-δ diagrams could be implemented in other parts of India to understand the evolution and transport of water vapor in the atmosphere. Further study is required to determine the role of this enhanced transpiration in atmospheric feedback mechanisms and its response to declining monsoon precipitation in the region.In this study, we used multiple satellite observations of various meteorological and isotopic parameters to determine the source of vapor in free troposphere above NE India and ascertained increase in local transpiration during pre-monsoon months. TES observations indicated sharp increase in δD values during April and May between 825–619 hPa pressure levels. Mean δD values increased from −153.9 ± 6.0‰ in Jan-Feb to −75.8 ± 3.8‰ in Apr-May whereas mean specific humidity increased from 3.7 ± 0.1 g kg


Photosynthesis occurs through the synergistic effects of the non-ncontinuously distributed components in the chloroplast. Inspired by nature, we mimic chloroplast and develop a generic approach to synthesize non-continuously distributed semiconductors threaded by carbon nanotubes. In the synthesis, carbon nanotubes serve as microwave antennas to produce local super-hot dots on the surface, which might induce and accelerate various organic/inorganic semiconductors assembly. With the unique nanoscale designed bionic architecture, a chloroplast structured photocatalyst with 3−dimentional dual electron transfer pathways facilitate enhanced photocatalytic performance. The as-synthesized carbon nanotubes-titanium oxide achieves a record-breaking efficiency of 86% for nitric oxide treatment under ultraviolet light irradiation. As a general strategy, a wide variety of carbon nanotubes threaded chloroplast structured nanomaterials can be synthesized and these nanomaterials could find applications in energy chemistry, environmental science and human health. 2 nanomaterials threaded by carbon nanotubes enable a high efficiency for nitric oxide treatment.Inspired by the favorable configuration of chloroplast, here the authors develop a microwave aided approach for the synthesis of similarly structured photocatalysts. The non-continuously distributed TiO However, without a favorable structure, they tend to suffer from some disadvantages such as the ease of aggregation and rapid photoelectron-hole recombination8. The photosynthesis allows plants to harvest solar energy by converting it into carbohydrate molecules to fuel all biological life activities on Earth11. Typically, photosynthesis efficiently occurs in the chloroplast  are widely used since they usually display fascinating electrical and optical properties endowed by the unique 1-dimensional properties. Such properties are expected and advantageous for the surface reactions and charge transport processes22.To decorate CNTs with semiconductors, the in-situ solvothermal strategy is reported25. However, it is still hard to selectively control the growth sites of semiconductors on the CNTs and it is also difficult to avoid the free nucleation and growth. As we know, people can hardly realize the intimate contact between CNTs and nanocrystals.Nano-sized semiconductors have been widely used as electrodes and photocatalysts in the application of energy and environmental field26. As a typical example, the concept of solar-energy convertor by constructing nano-rectenna solar cells using aligned CNTs as optical antennas is also introduced27.We consider introducing microwave heating into the in situ fabrication involving CNTs, aiming to obtain the chloroplast structured photocatalysts enabled by microwave synthesis Fig.  right. W2) to study their microwave synthesis mechanism, as well as their photocatalytic performances in nitric oxide oxidation reaction. This unique structure is proved for promoted light harvesting ability, while the 3−dimensional CNTs networks provided electron transfer pathways for accelerating chemical reactions. Thus, the as-synthesized CNTs-TiO2 present a record-breaking efficiency of 86% for ultraviolet light-driven nitric oxide abatement. In addition, as a general strategy, a series of chloroplast structured nanomaterials can be synthesized and should be potentially used in the energy and environment fields.Here, our design is that CNTs would work as microwave antennas in the liquid–solid interficial synthesis. By using CNTs as microwave antennas, we have developed a general strategy for in-situ fabrication of semiconductors non-continuously threaded by the CNTs. As show in Fig. 2 was synthesized by heating the dimethyl sulfoxide (DMSO) solution containing TiCl3 and CNTs with microwave. Transmission electron microscopy (TEM) images .As a typical example, the CNT-TiOges Fig.  demonstrern Fig.  displaye2 was prepared by time-temperature-programmed process  While, the f-CT exhibited the lower conversion of ~77% after equilibrium in 20 min, but gradually dropped to ~58% after 120 min irradiation, which was even much lower than that for sample CT-mx (~72%). Considering that these samples own the similar CNT/TiO2 ratio, surface area and light absorption properties analyzed in Supplementary Fig. 2 by the covalent bonds of Ti–O–C=O or Ti–O–C (located at ~1120 cm−1) formed due to the etherification process between the CNTs surface carboxyl/hydroxyl groups and TiO2, which was evidenced by the Fourier transform infrared (FT-IR) spectra as shown in Supplementary Fig. s), charge transfer impedance (RCT) and a non-ideal capacitor (CPE), which substituted the double layer capacitance of the electrode–electrolyte interface. The capacitor is related to the adsorption of reactant on the surface of electrode and the diffusion process associated with charge transfer reaction across the electrode–electrolyte interface. The charge transfer impedance (RCT) of CT, CT-mx, and f-CT were distinguished and could be fitted to 126.4, 218.9, and 400.2Ω, respectively. For the CT and CT-mx samples, they exhibited lower RCT value in the EIS spectra and relatively higher photocurrents shown in Fig. 2 and CNTs. For the sample of CT, multiple interfaces between TiO2 and CNTs provided relatively efficient electron transfer resulting in a short lifetime. While, the aggregation of TiO2 particles might suppress the interfacial electron transfer from TiO2 to CNT, thus resulting in the prolonged lifetime for the f-CT sample. Furthermore, the photocarrier density calculated from the Mott–Schottky plots  is an intrinsic parameter used to define the ability of a specific material or solvent to convert microwave energy into heat38. The tan δ value can be expressed as tan δ = ε”/ε’, where ε’ refers to the polarizability and ε” is the dielectric loss corresponding to the efficiency to convert the microwave energy into heat40. Different materials exhibit different tan δ at a given microwave frequency and temperature. The CNTs have an excellent high tan δ of 1.45 at a frequency of 2.45 GHz and room temperature41, which is much more positive than that of many solvents 38. Thus, the CNTs could act as microwave antennas to efficiently absorb microwave and subsequently convert microwave energy into thermal energy, leading to the formation of surface “super-hot” dots with much higher temperature than that of bulk solution45. Unfortunately, we could not directly measure the local surface temperature to confirm the “super-hot” dots on CNTs. Under this case, we determined the DMSO solution temperature by heating the system in the presence or absence of CNTs and observed the variation of the solution temperature. As shown in Fig. δ (~0.002 at 2.45 GHz) exhibited nearly the same temperature as the pure DMSO solution. The enhanced heating rate of solution in the presence of CNTs was caused by the heat transfer from CNTs to solution based on the much higher temperature on the CNTs surfaces. In contrast, when the regular oil-bath heating was used instead of microwave, the DMSO solution containing CNTs or PTFE exhibited the similar temperature as the pure solution  on CNTs from the bulk solution. UV-Vis was used to track the concentration of Ti3+ since the absorbance in the 505 nm wavelength is the standard Ti3+ signal. Figure 3 solution before and after keeping on the temperature from 30 °C to 70 °C for 20 min under both microwave irradiation and regular hydrothermal autoclave. The corresponding temperature-dependent solution concentration change was shown in Fig. 3+ adsorption capability under microwave heating than that under regular hydrothermal heating at the same temperature. Meanwhile, increasing microwave heating temperature (more microwave energy absorbed by CNTs) leaded to the significant enhance in Ti3+ adsorption since the concentration of Ti3+ solution dropped from 85.5% (30 °C) to 61.4% (70 °C). However, in the autoclave hydrothermal condition, the adsorption amount just showed a slightly enhance with increasing the temperature. Thus, microwave could also enhance the ability for CNTs to adsorb metal-ions and induce their localized hydrolysis on the surface of CNTs. In addition, the CNTs also lowered the nucleation temperature of TiO2 crystal. As shown in Supplementary Fig. 3 in DMSO solution for 10 min to 100 °C, the solution remained original yellow color and no significant solid products were found. It suggested that the hydrolysis reaction of TiCl3 could not occur at such low temperature in this case. On the contrary, in the similar process with CNTs added, the yellow solution became much lighter, accompanied by the formation of gray powders at the bottom. The gray powder can be further confirmed by the FESEM image of Supplementary Fig. 2 crystal. Meanwhile, we also found that, when the TiCl3 hydrolysis reaction was carried out at 100 °C via regular oil-bath heating, no significant reaction occurred even in the presence of CNTs as shown in the Supplementary Fig. 3+ into TiO2. Thus, the TiO2 nanocrystals formed only on the CNTs surface rather than in the bulk solution. To further confirm this hypothesis, we also used a 1-dimensional polytetrafluoroethylene (PTFE) with a very low tan δ as a reference material instead of CNTs. From the FESEM of PTFE-TiO2 . As the Fourier-transform infrared (FT-IR) spectra  and hydroxyl (–OH) on CNTs also played a vital role in this microwave reaction mainly from the following two aspects: first, oxygen-contained groups promoted the microwave absorbance and conversion of CNTs due to the enhanced dielectric property by increased polarizability which lead to the formation of more “super-hot” dots in the Joule heating process;tra Fig.  revealed2 nanospheres threaded by CNTs from the non-continuous distribution state to the completely encapsulated core-shell structure shown in Supplementary Fig. 2 nanocomposites with hierarchical morphologies could also be synthesized based on the present microwave-antenna strategy , showing its universality. The FESEM images in Fig. 2 nanocubes with exposed (001) and (010) facets, anatase TiO2 nanodecahedrons with exposed (001) and (101) facets, rutile TiO2 nanoflowers , they could also efficiently avoid aggregation of the TiO2 nanoparticles, corresponding to a uniform shape with small particle size compared with those obtained in the absence of CNTs48.Accordingly, by well controlling the surface oxygen-contained groups, we could continuously adjust the density of TiO2 nanocrystals illustrated in Fig. 3+ ions (purple dots) by carboxyl or hydroxyl groups. When the surface “super-hot” dots reached the nucleation temperature, the Ti3+ could hydrolyze into nuclei seeds  on the CNTs. Finally, with the increase of microwave time and temperature, more “super-hot” dots on the CNTs surface generated, which could absorb more Ti3+ ions, followed by hydrolyzing into more nuclei seeds of TiO2. The nuclei further grew into TiO2 nanocrystals (big brown dots) and finally formed the chloroplast structure. In this synthesis of CNTs-TiO2 nanocomposites, after microwave-heating for 20 min with the corresponding temperature in 180 °C, the density of TiO2 nanocrystals on the CNTs surface reached maximum. Further increasing microwave-heating time, the temperature could not increase the adsorption of Ti3+ ions onto CNTs surface due to the spatial hindrance. Further prolonging the microwave-heating time resulted in crystal growth, leading to the increased size of TiO2 nanocrystals as shown in Fig. At this point, we could summarize the microwave antennas mechanism of the in situ fabricating chloroplast structured CNTs-threaded TiOGrana assembled Thylakoid, where H2O can be oxidized to O2 by the photogenerated holes through photosystem I and II with the formation of proton (H+). While, the layer-like Stroma Lamella links each Thylakoid by providing electron/H+ transport channels to promote the Calvin cycle for efficient capturing of CO2 to produce sugar. In the photocatalytic NO removal reaction, as the light-dependent reaction, the CT sample with the chloroplast structure can be excited by UV light with generated holes in TiO2 microsphere to deeply oxidize NO to NO3−  nanocrystals threaded by CNTs. In CNT-TiO3 aqueous solution with 10–15 wt% HCl (Merck-Schuchardt) was added to the suspension with magnetic stirring for 10 min. Subsequently, the mixture was moved to a 40 ml quartz vessel with a Teflon lid. It was treated at 180 °C for 30 min with a heating rate of 15 °C min−1 and with an initial pressure of 35 bar by N2 in a single chamber microwave digestion system . The resulting powder was washed with deionized water and absolute ethanol for 3 times, followed by vacuum drying at 80 °C for 4 h.In a typical synthesis, 20 mg CNTs were dispersed in 18 mL dimethyl sulfoxide  for 30 min to form a black suspension in an ultrasonic cleaner. As references, CNTs with different acid treatment time were used instead of the CNT without acid treatment. Then, 2 mL 15 wt% TiClC0– C)/C0 × 100%, where C0 and C refer to the NO concentration determined before and after reaction.The photocatalytic NO oxidation in gas phase was carried out at ambient temperature in a continuous flow reactor with volume of 10.8 L (36 × 20 × 15 cm). For UV lights driven photocatalysis, eight of UV lamps  located vertically above the reactor was used as light source. In each run of experiments, an air gas flow containing 500 ppb NO was allowed to pass through 0.20 g photocatalyst at the flow velocity of 4.0 L/min. After reaching adsorption-desorption equilibrium on the photocatalyst, the lamp was turned on to start the photocatalysis reaction. The concentration of NO was continuously measured by using a chemiluminescence NO analyzer . The NO removal rate (%) was calculated based on the following equation: NO removal rate (%) = (Supplementary Information


This data file includes experimental details on how to make uncoated iron oxide nanoparticles using a green electrochemical method. It provides the raw data on the antibacterial activity of one of these formulations, and the full computational data and methodology used to generate that data, of several different magnetite clusters of specific spin multiplicities for 4, 5, 7 and 9 iron atom magnetite clusters. This data will assist other researchers wishing to replicate or expand on these results for the investigation and use of nanomagnetite for antibacterial applications. Specifications tableValue of the data•The biological data shows potent activity. The data is provided here for other researchers to compare with their own as magnetite is further investigated as a potential antibacterial material.•The computational results are the most detailed study of magnetite in the literature to the best of our knowledge. It is our hope that other researchers can use these as a starting point for even better refining the computational models in the future.•This is a scalable, green, inexpensive synthesis of nanomagnetite with low dispersity. The synthesis described here will hopefully be helpful to others looking to replicate or make similar systems.The data includes the experimental protocols required to make the nanomagnetite, the experimental details for both the computational and biological studies, the biological data used for our analyses, and full co-ordinates for all the computational models referenced in the accompanying article: “The Synthesis and Characterization of a Nanomagnetite with Potent Antibacterial Activity and Low Mammalian Toxicity 11.14·7H2O, Sigma-Aldrich) and sodium hydroxide (Sigma-Aldrich) were analytical grade and used as received without further purification. The DU145 human adenocarcinoma cell line was purchased from Pasteur Institute (Iran). X-ray powder diffraction was carried out on a D8 Advance X’Pert X-Ray diffractometer (Bruker); Fourier-transform infrared spectroscopy was done on a JASCO 640 plus machine (4000–400 cm−1) at room temperature using KBr pellets; vibrating magnetometry was analyzed using a Kavir Precise Magnetic instrument ; field emission scanning electron microscopy was carried out using a Mira 3-XMU instrument capable of 700,000× magnification.Iron (II) sulfate heptahydrate  were performed using the Gaussian 09 suite of programs The computational calculations for our proposed iron oxide clusters  was carried out in 96-well plates using serial dilutions of a stock of sonicated 3000 MP Ultrasonic Homogenizer nanoparticles (those prepared as above at pH = 13) according to the standard protocol 1.6n = 10). The control group was given 1 ml of distilled water using the oral gavage technique once a day for 14 days. The second group was treated with 1 ml of nanomagnetite solution (10 ppm/ml/day) once a day, for 14 days. The third group received 1 ml of nanomagnetite solution (100 ppm/ml/day) once a day, for 14 days. The fourth group received 1 ml of nanomagnetite solution (1000 ppm/ml/day) once a day, for 14 days. At the end of the feeding schedule (14 days) the animals were sacrificed by cervical dislocation and the blood samples were collected from the heart and centrifuged (3000 rpm for 5 min) to recover the serum. The serum was then immediately frozen at −80 °C until needed for the assays detailed in the supporting material and described below.All weights are dry weights. Adult male Wistar rats (190–210 g) obtained from the Laboratory Animal Center University of Zabol were used in this study. Rats were randomly divided into four groups (


Crystalline solids are generally known as excellent heat conductors, amorphous materials or glasses as thermal insulators. It has thus come as a surprise that certain crystal structures defy this paradigm. A prominent example are type-I clathrates and other materials with guest-host structures. They sustain low-energy Einstein-like modes in their phonon spectra, but are also prone to various types of disorder and phonon-electron scattering and thus the mechanism responsible for their ultralow thermal conductivities has remained elusive. Our thermodynamic and transport measurements on various clathrate single crystal series and their comparison with ab initio simulations reveal an all phononic Kondo effect as origin. This insight devises design strategies to further suppress the thermal conductivity of clathrates and other related materials classes, with relevance for thermoelectric waste heat recovery and, more generally, phononic applications. It may also trigger theoretical work on strong correlation effects in phonon systems. Thermoelectric clathrates host guest atoms that can rattle inside their surrounding cages, yielding unusual phononic properties. Ikeda et al. show that ab initio calculations fail to account for thermodynamic and thermal transport data and propose a Kondo-like mechanism to explain the discrepancy. The perfect thermoelectric material combines low phonon thermal conductivity with high electrical conductivity and high thermopower. Materials classes with cage-like structures such as the clathrates8 or skutterudites9 show surprisingly low phonon thermal conductivities κph, even in perfect single crystals. Type-I clathrates G8H46, that show promising thermoelectric figures of merit11, consist of host atoms (H) that encapsulate guest atoms (G) in a framework of oversized cages , inconsistencies with other results have been pointed out more recently. Notably, such modeling can neither explain the exceptionally long phonon lifetimes21 nor the large thermal conductivity differences between structurally very similar n- and p-type versions of these materials22. The recent achievement of ab initio calculations of the thermal conductivity of a type-I clathrate, based on intrinsic phonon-phonon Umklapp scattering processes23, represents a major step forward. It showed that, unlike in the resonance scattering picture, the phonon lifetimes are reduced in a wide frequency range. Nevertheless, also DFT calculations can explain experimental thermal conductivity data only at low temperatures, but strongly undershoot them at high temperatures.The increasing world wide energy consumption and the associated climate change call for enhancing the overall energy efficiency of technological energy conversion processes. Thermoelectricsges Fig. . Recent Here we propose an alternative mechanism, a Kondo-like phonon-phonon interaction, that can explain the difference as the disentanglement of Kondo-coupled acoustic and rattling phonon modes above the phonon Kondo temperature. In general terms, the Kondo effect describes non-commutative scattering of an extended wave from a localized entity with internal degree of freedom. It has been studied in many different settings, including magnetic, orbital, charge, and local vibrational degrees of freedom  is a large crystal-like maximum. It is narrow and occurs at a surprisingly low temperature of only about 10 K. As will be further detailed below, this narrow low-temperature maximum is a strong indication for the phonon transport not being controlled by the natural energy scale kBΘD of the Debye temperature ΘD but by a much smaller energy scale kBΘE.We start by giving an overview of the phonon thermal conductivities ere Fig. . Althoug20, where we replaced the Debye temperature ΘD by a variable temperature ΘE   of all samples in this series shows a maximum at low temperatures that is systematically enhanced at nearly constant temperature with increasing x  data for different x merge . Strong point-defect scattering of Rayleigh type, with an ω4 dependence, occurs only if λ is at least an order of magnitude larger than d, corresponding to phonon energies ħω of 2.5 meV (30 K) and below. At much larger energies (and temperatures), defect scattering should be weak and frequency independent28.Next, we prove, with two clathrate single crystal series, that the strong Umklapp scattering due to the new low-energy scale indeed dominates the lattice thermal conductivity in a wide temperature range. In the first such series, Bar a Fig.  and the g x Fig.  left. Thead Fig.  right. Trge Fig.  left, in8Gax16−Gex30+ that has been much investigated in the past. It is an ideal system to study the importance of phonon-electron scattering because it has a low and essentially constant amount of point defects .The second sample series is the prototypical clathrate Ba8Gax16−Gex30 +  single crystals reported in the literature31 show severely different κph(T) at low temperatures. Figure T < ΘE/2) with the modified Callaway model discussed in Supplementary Note 30. It takes Umklapp scattering 30 as discussed in Supplementary Note κph(T) data of a different Ba8Gax16−Gex30+ single crystal31 can be very well reproduced by strongly increasing κph caused by a different q smaller than twice the Fermi wave vector kF. For the crystal of ref. 30 we estimate this to hold below about 140 K  at higher temperatures has also been seen in other type-I clathrate single crystal series33, but precise high-temperature data on these are not available to date.A suppression of large low-temperature differences in κph(T) of various type-I clathrates above 50 K. In addition, boundary scattering cannot contribute significantly in single crystals at these temperatures. Thus, at elevated temperatures, κph is dominated by intrinsic phonon-phonon (Umklapp) scattering. This allows us to pin down its microscopic origin, as shown in what follows.Taking both our clathrate series together we have managed to rule out the influence of defect and phonon-electron scattering on 36, shows a universal scaling of the room-temperature phonon thermal conductivity with the product of sound velocity and Einstein temperature of the lowest-lying rattling mode(s), κph ∝ vsΘE  Kondo temperature TK (refs. 38). Figure f band of  energy ħωE = kBΘE corresponds to the narrow 4f band. The resulting hybridized band is again extremely flat in large portions of the Brillouin zone, giving rise to “heavy” phonons with extremely low group velocity vg at finite wave vectors. Even though the resulting T = 0 dispersion relation may look similar to the one obtained from ab initio lattice dynamics simulations23, there is an important difference: Being a strong correlation phenomenon, the phonon Kondo effect has a characteristic temperature dependence. Well above the Kondo temperature, the interacting states (violet) go over to the non-interacting ones (red and blue), an effect referred to as crossover from infrared slavery to asymptotic freedom38, which is absent in the lattice dynamics simulations .A similar energy renormalization is seen in heavy fermion metals, where the (spin) Kondo effect rescales the Fermi temperature κph still contains vs (to linear power) is attributed to the absence of a Fermi level in phonon systems. Whereas the electrical resistivity in heavy fermion metals is dominated by the heavy electrons at the Fermi wavevector kF, phonons of all wave vectors, including long-wavelength ones propagating with vs, contribute to κph.The fact that the observed universal scaling of 8Ga16Ge30 with ab initio lattice dynamics calculations (see Methods) and reveal key characteristics of the Kondo effect. For the specific heat, the agreement between experiment and theory is excellent at low and high temperatures, but a distinct deviation is seen in between  dependence, the hallmark of incoherent Kondo scattering above TK in the original spin Kondo effect38.Finally, we compare specific heat, thermal expansion, and thermal conductivity data for the prototypical clathrate Ba. E Fig. . It rele23. The latter thus takes phonon-phonon interactions to lowest order into account. However, temperature dependences resulting from strong phonon correlation effects, most notably the Kondo disentanglement of acoustic and rattling modes above the phonon Kondo temperature, cannot be captured by these simulations. This is why the comparison of experimental data and such calculations can be used to extract the characteristic temperature dependences due to the correlations . Specifically, the temperature-dependent specific heat is overestimated by the calculations as, unlike in the Kondo effect, no entropy is released by Bose populating temperature-independent anticrossings . The phonon density of states (DOS) and the specific heat are calculated in the harmonic approximation, the thermal expansion in the quasi-harmonic approximation, and the thermal conductivity from anharmonic interatomic force constantsory Fig. .21 that could be an indication of such a disentanglement of interacting acoustic and rattling modes at high temperatures. All these observations provide strong evidence for a previously unknown correlation effect – an all phononic Kondo effect – as microscopic origin of the peculiar thermodynamic and thermal transport behavior of clathrates.Further support, independent of any theoretical modeling, comes from recent inelastic neutron scattering experiments. They reveal unexpected changes of the optical dynamical structure factors as function of temperatured site within a soft plane40 in the tetracaidecahedra , that is being referred to so extensively, needs to be reconsidered. With the energy renormalization also κph of a given material could, for instance, be realized by nanostructuring or the introduction of dense dislocation arrays46. Long-wavelength phonons that are only weakly affected by the phonon Kondo effect could, at high temperatures, be effectively scattered by nanostructures that are small compared to their mean free path. Indeed, a few clathrates and skutterudites appear to violate the original Cahill An equally striking consequence is that Cahill’s definition of the minimum thermal conductivity mentclass2pt{minimin ref. 4, that isin ref. 4, that isx12−MxSb4S13  tetrahedrites, optical phonon branches involving out-of-plane vibrations of the three-fold coordinated Cu ions were predicted by ab initio calculations and were suggested as origin of the low thermal conductivities47. Interestingly, κph of this material fits perfectly into our universal scaling plot , Bi2Te3 (ref. 49), BiCuO (ref. 50), Cu3SbSe3 (ref. 51), and rattling-induced superconductors such as the β-pyrochlore oxides52, dodecaborides53, or VAl10 (ref. 54), and possibly even amorphous and glassy materials55. Detailed investigations, such as presented here, are needed to test whether also in these materials the phonon Kondo effect is at work.The occurrence of low-lying Einstein-like phonon modes that interact with acoustic phonons is not limited to the class of clathrates and related cage-like materials. For the recently discovered family of Culot Fig. . Other c56.Phonon Kondo systems transfer heat largely via low-frequency phonons of long mean free paths. As such they may be seen as promising intrinsic thermocrystals, for applications such as heat waveguides or thermal diodes in the emerging field of phononicsx, two cylindrical rods with the same nominal composition Ba8Cu4.8Gex−y41.2−□yGax (BCGGx) were prepared for each sample with x = 0.0, 0.2, 0.5, and 1.0 from high-purity elements using a high-frequency induction furnace. One rod with 7 mm in diameter and 60 mm in length served as the feed rod, the other one with the same diameter and 20 mm in length as the seed for the crystal growth. The single crystal growth was performed in a 4-mirror furnace equipped with 1000 W halogen lamps. The pulling speed of the rod was 3–5 mm h−1. Both rods rotated in opposite direction (speed: ~8 revolutions min−1) to ensure efficient mixing of the liquid and a uniform temperature distribution in the molten zone. A pressure of 1.5 bar Ar was used during the crystal growth.As starting material for the single crystal growth of BCGGx were collected using a HUBER-Guinier image plate system (Cu θ ≤ 100°). The lattice parameters  as internal standard.X-ray powder diffraction data on BCGGers Fig.  were obtω method is an ac technique for direct thermal conductivity measurements. During a 3ω experiment the sample is heated locally and thus, in contrast to steady-state heat-flow experiments, errors due to radiation at elevated temperatures are reduced to a negligibly low level24. Furthermore, this method is insensitive to geometrical errors. This is because the only geometrical parameter entering is the length of a line heater. As it is usually prepared by means of photo or electron-beam lithography and sputtering, its length is very well defined (see below). In fact, the error of our 3ω thermal conductivity data, which we estimate to be below 5%, is dominated by uncertainties in the heater resistance and its temperature dependence.Commonly used laser flash methods measure the thermal diffusivity and thus need to be combined with specific heat and density measurements to calculate the thermal conductivity, which typically reduces the accuracy of this technique. By contrast, the 32 was first deposited on the polished sample surfaces by chemical vapor deposition. Then, a 4 nm thick titanium sticking layer and the 64 nm thick gold film were sputtered in an Ardenne LS 320 S sputter system. The heater structures were made by standard optical lithography techniques using a Karl Suess MJB4 mask aligner.For our studies, the narrow metal line serving as both the heater and the thermometer had a width of 20 μm and a length of 1 mm, with an uncertainly of 1 μm. To avoid electrical contact between heater and sample a thin layer of SiOω, which thus leads to a 2ω temperature oscillation of both the heater and the sample. Due to the linear temperature dependence of the metallic heater, the 2ω temperature oscillation translates into a 3ω voltage oscillation, which is detected using a lock-in amplifier . Applying the 3ω method24 to bulk geometry, the measured in-phase temperature oscillation of the heater/thermometer line is expected to be linear in logarithmic frequency f as long as the thermal penetration depth is large compared to the heater half width b and at least five times smaller than the sample thickness t  as a sample we found the background signal to be negligibly small within the entire frequency range (0.5 Hz to 50 kHz) of the experiment. Using the same resistor, the accuracy of our voltage controlled AC constant current source was found to be ±0.1% within the whole frequency range and for all tested excitations (100 μA to 10 mA). By further measurements on different resistors, load dependences were ruled out.Prior to the thermal 3ω measurements were done in the temperature range between 80 and 330 K. With this setup, we reproduced the thermal conductivity data on single crystalline Ba8Ga16Ge30 of Sales et al.41 within the error bar, estimated to only 3% in that work. This precision, which is remarkable for a steady-state experiment, was reached with special radiation shields and specific sample geometries57.3L0 = 2.44 · 10−8 WΩK−2 and electrical resistivity data measured on the same samples.Below 100 K the data were completed by additional steady-state heat-flow experiments. The phonon thermal conductivities of all investigated materials were calculated by subtracting an electronic contribution, determined using the Wiedemann-Franz law with a constant Lorenz number of 4He specific heat option of a Physical Property Measurement System (PPMS) from Quantum Design. The addenda was measured separately prior to each sample measurement.The specific heat was measured with a relaxation-type method using the Cp, first the electronic contribution was determined. At low temperatures, the specific heat can be approximated by Cp/T = γ + βT2, where the Sommerfeld coefficient γ represents the electron contribution and the β parameter quantifies the Debye-like phonon contribution. For BCGGx, the data below 3.5 K are very well described by such linear fits (not shown).To study the phonon contribution to Cp/T3 vs logT. Within such a representation, rattling modes appear as bell-shaped contributions on top of a Debye-like phonon background (not shown).Rattling modes, originating from localized oscillations of the guest atoms, can be revealed by analyzing RH = 1/(ne).The electrical resistivity and the Hall coefficient were determined by a standard 6-wire technique using the horizontal rotator option of a Physical Property Measurement System (PPMS) from Quantum Design. Temperature-dependent Hall effect measurements were performed in a magnetic field of 9 T. The Hall resistivity was confirmed to be linear in fields up to 9 T at all temperatures down to 2.5 K. The Hall coefficient was analyzed within a simple one-band model, αL(T) = l−1dl/dT were carried out by using a high-resolution capacitive dilatometer58, which enables the detection of length changes Δl ≥ 10−2 Å. Relative length changes were measured along a principle axis of cubic Ba8Ga16Ge30. αL(T) is obtained by numerical differentiation of the Δl(T)/l data with respect to temperature  as proposed by Perdew, Burke, and Ernzerhof (PBE)61.Ab initio density functional theory (DFT) simulations were conducted using the Vienna Ab initio Simulation Package (VASP)8Ga16Ge30 with Ba at the Wyckoff sites 2a and 6d, Ge at 6c and 24k, and Ga at 16i was investigated, using a 5 × 5 × 5 k-point mesh and a plane wave cutoff of 500 eV. For a fixed lattice constant of 10.74 Å, corresponding to the experimental value, the atomic positions were relaxed. To test the impact of a more realistic Ga/Ge distribution22, we have in addition performed selected calculations for a disordered structure (see Supplementary Notes −3 eV Å−1), symmetry non-equivalent displacements were introduced into the relaxed structure of both the ordered and the disordered . Expanding the range of anharmonic interactions from nearest to next-nearest neighbor shells leaves the temperature dependence largely unaffected . The ALAMODE  and phonopy (https://atztogo.github.io/phonopy/) packages are open source.Licenses for VASP are available on the VASP website (Supplementary Information