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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Pre- and post-processing of cluster galaxies out to 5 × R 200: the extreme case of A2670

ABSTRACT We study galaxy interactions in the large-scale environment around A2670, a massive (M200 = $8.5 \pm 1.2~\times 10^{14} \, \mathrm{{M}_{\odot }}$) and interacting galaxy cluster at z = 0.0763. We first characterize the environment of the cluster out to 5× R200 and find a wealth of substructures, including the main cluster core, a large infalling group, and several other substructures. To study the impact of these substructures (pre-processing) and their accretion into the main cluster (post-processing) on the member galaxies, we visually examined optical images to look for signatures indicative of gravitational or hydrodynamical interactions. We find that ∼21 per cent of the cluster galaxies have clear signs of disturbances, with most of those (∼60 per cent) likely being disturbed by ram pressure. The number of ram-pressure stripping candidates found (101) in A2670 is the largest to date for a single system, and while they are more common in the cluster core, they can be found even at >4 × R200, confirming cluster influence out to large radii. In support of a pre-processing scenario, most of the disturbed galaxies follow the substructures found, with the richest structures having more disturbed galaxies. Post-processing also seems plausible, as many galaxy–galaxy mergers are seen near the cluster core, which is not expected in relaxed clusters. In addition, there is a comparable fraction of disturbed galaxies in and outside substructures. Overall, our results highlight the complex interplay of gas stripping and gravitational interactions in actively assembling clusters up to 5 × R200, motivating wide-area studies in larger cluster samples.

Astronomy & Astrophysics↗
DOE OSTI2026

Constraints on the normal branch of DGP gravity from SPT galaxy clusters with DES and HST weak-lensing mass calibration and from P l a n c k PR4 CMB anisotropies

We present constraints on the normal branch of the Dvali-Gabadadze-Porrati (nDGP) braneworld gravity model from the abundance of massive galaxy clusters. On scales below the nDGP crossover scale r c , the nDGP model features an effective gravitylike fifth force that alters the growth of structure, leading to an enhancement of the halo mass function (HMF) on cluster scales. The enhanced cluster abundance allows for constraints on the nDGP model using cluster samples. We employ the South Pole Telescope (SPT) cluster sample, selected through the thermal Sunyaev-Zel’dovich effect with the SPT and with mass calibration using weak-lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample contains 1,005 clusters with redshifts 0.25 < z < 1.78 , which are confirmed with the multicomponent matched filter algorithm using optical and near-infrared data. Weak-lensing data from DES and HST enable a robust mass measurement of the cluster sample. We use DES Year 3 data for 688 clusters with redshifts z < 0.95 , and HST data for 39 clusters with redshifts 0.6 < z < 1.7 . We account for the enhancement in the HMF through a semi-analytic correction factor to the standard cosmology HMF derived from the spherical collapse model in the nDGP model. We then further calibrate this model using N -body simulations. In addition, for the first time, we analyze the primary CMB temperature and polarization anisotropy measurements from Planck PR4 within the nDGP model. We obtain a competitive constraint from the joint analysis of the SPT cluster abundance with the Planck PR4 data, and report an upper bound of 1 / H 0 r c < 1.41 at 95% when assuming a cosmology with massive neutrinos.

Vogt, S. M.L. [Munich U. Observ.; LMU Munich (main↗
NASA NTRS1984

Ages of intermediate-age Magellanic Cloud star clusters

Ages of intermediate-age Large Magellanic Cloud star clusters have been estimated without locating the faint, unevolved portion of cluster main sequences. Six clusters with established color-magnitude diagrams were selected for study: SL 868, NGC 1783, NGC 1868, NGC 2121, NGC 2209, and NGC 2231. Since red giant photometry is more accurate than the necessarily fainter main-sequence photometry, the distributions of red giants on the cluster color-magnitude diagrams were compared to a grid of 33 stellar evolutionary tracks, evolved from the main sequence through core-helium exhaustion, spanning the expected mass and metallicity range for Magellanic Cloud cluster red giants. The time-dependent behavior of the luminosity of the model red giants was used to estimate cluster ages from the observed cluster red giant luminosities. Except for the possibility of SL 868 being an old globular cluster, all clusters studied were found to have ages less than 10 to the 9th yr. It is concluded that there is currently no substantial evidence for a major cluster population of large, populous clusters greater than 10 to the 9th yr old in the Large Magellanic Cloud.

Flower, P. J.↗
NASA NTRS1993

X-ray emission from a complete sample of Abell clusters of galaxies

The Rosat all sky survey is used to investigate the X-ray properties of a sample of Abell clusters with measured redshifts and accurate positions. The sample comprises the 145 clusters within a 561 square degree region at high galactic latitude. The mean redshift is 0.17. The sample mean exposure time is higher than average and its mean galactic column density is very low. These together produce a flux limit of about 4.2 x 10(exp -13) erg/sq cm/s in the 0.5 to 2.5 keV energy band. By analyzing the excess of positive fluctuations of the X-ray flux at the cluster positions, compared with the fluctuations of randomly drawn background fields, it is possible to extend these results below the nominal flux limit. It is found that 80 of richness R equal to or greater than 0 and 86 of R equal to or greater than 1 clusters are X-ray emitters with fluxes above 1.10 to the power of minus 13 erg/sq cm/s. Nearly 90 of the clusters meeting the requirements to be in Abell's statistical sample emit above the same level. It is concluded that almost all Abell clusters are real clusters and the Abell catalogue is not strongly contaminated by projection effects. The Kaplan-Meier product limit estimator is used to calculate the cumulative X-ray luminosity function. It is shown that the shape of the luminosity functions are similar for different richness classes, but the characteristic luminosities of richness 2 clusters are about twice of those of richness 1 clusters which are in turn about twice of those of richness zero clusters. This result is another manifestation of the luminosity richness relation for Abell cluster.

Briel, Ulrich G.↗
NASA NTRS1994

Clustering, randomness, and regularity in cloud fields. 4: Stratocumulus cloud fields

To complete the analysis of the spatial distribution of boundary layer cloudiness, the present study focuses on nine stratocumulus Landsat scenes. The results indicate many similarities between stratocumulus and cumulus spatial distributions. Most notably, at full spatial resolution all scenes exhibit a decidedly clustered distribution. The strength of the clustering signal decreases with increasing cloud size; the clusters themselves consist of a few clouds (less than 10), occupy a small percentage of the cloud field area (less than 5%), contain between 20% and 60% of the cloud field population, and are randomly located within the scene. In contrast, stratocumulus in almost every respect are more strongly clustered than are cumulus cloud fields. For instance, stratocumulus clusters contain more clouds per cluster, occupy a larger percentage of the total area, and have a larger percentage of clouds participating in clusters than the corresponding cumulus examples. To investigate clustering at intermediate spatial scales, the local dimensionality statistic is introduced. Results obtained from this statistic provide the first direct evidence for regularity among large (more than 900 m in diameter) clouds in stratocumulus and cumulus cloud fields, in support of the inhibition hypothesis of Ramirez and Bras (1990). Also, the size compensated point-to-cloud cumulative distribution function statistic is found to be necessary to obtain a consistent description of stratocumulus cloud distributions. A hypothesis regarding the underlying physical mechanisms responsible for cloud clustering is presented. It is suggested that cloud clusters often arise from 4 to 10 triggering events localized within regions less than 2 km in diameter and randomly distributed within the cloud field. As the size of the cloud surpasses the scale of the triggering region, the clustering signal weakens and the larger cloud locations become more random.

Lee, J.↗
NASA NTRS1997

Cooling Flow Spectra in Ginga Galaxy Clusters

The primary focus of this research project has been a joint analysis of Ginga LAC and Einstein SSS X-ray spectra of the hot gas in galaxy clusters with cooling flows is reported. We studied four clusters (A496, A1795, A2142 & A2199) and found their central temperatures to be cooler than in the exterior, which is expected from their having cooling flows. More interestingly, we found central metal abundance enhancements in two of the clusters, A496 and A2142. We have been assessing whether the abundance gradients (or lack thereof) in intracluster gas is correlated with galaxy morphological gradients in the host clusters. In rich, dense galaxy clusters, elliptical and SO galaxies are generally found in the cluster cores, while spiral galaxies are found in the outskirts. If the metals observed in clusters came from proto-ellipticals and proto-S0s blowing winds, then the metal distribution in intracluster gas may still reflect the distribution of their former host galaxies. In a research project which was inspired by the success of the Ginga LAC/Einstein SSS work, we analyzed X-ray spectra from the HEAO-A2 MED and the Einstein SSS to look for temperature gradients in cluster gas. The HEAO-A2 MED was also a non-imaging detector with a large field of view compared to the SSS, so we used the differing fields of view of the two instruments to extract spatial information. We found some evidence of cool gas in the outskirts of clusters, which may indicate that the nominally isothermal mass density distributions in these clusters are steepening in the outer parts of these clusters.

White, Raymond E., III↗
NASA NTRS1999

A Distant, X-Ray Luminous Cluster of Galaxies at Redshift 0.83

We have observed the most distant (= 0.829) cluster of galaxies in the Einstein Extended Medium Sensitivity Survey (EMSS), with the ASCA and ROSAT satellites. We find an X-ray temperature of 12.3(sup 3.1, sub 2.2) keV for this cluster, and the ROSAT map reveals significant substructure. The high temperature of MS1054-0321 is consistent with both its approximate velocity dispersion, based on the redshifts of 12 cluster members we have obtained at the Keck and the Canada-France-Hawaii telescopes, and with its weak lensing signature. The X-ray temperature of this cluster implies a virial mass approximately 7.4 x 10(exp 14) /h solar mass, if the mean matter density in the universe equals the critical value (OMEGA(sub 0) = 1), or larger if OMEGA(sub 0) < 1. Finding such a hot, massive cluster in the EMSS is extremely improbable if clusters grew from Gaussian perturbations in an OMEGA(sub 0) = 1 universe. Combining the assumptions that OMEGA(sub 0) = 1 and that the initial perturbations were Gaussian with the observed X-ray temperature function at low redshift, we show that this probability of this cluster occurring in the volume sampled by the EMSS is less than a few times 10(exp -5). Nor is MS1054-0321 the only hot cluster at high redshift; the only two other z > 0.5 EMSS clusters already observed with ASCA also have temperatures exceeding 8 keV. Assuming again that the initial perturbations were Gaussian and OMEGA(sub 0) = 1, we find that each one is improbable at the < 10(exp -2) level. These observations, along with the fact that these luminosities and temperatures of the high-z clusters all agree with the low-z L(sub x) - T(sub x) relation, argue strongly that OMEGA(sub 0) < 1. Otherwise, the initial perturbations must be non-Gaussian, if these clusters' temperatures do indeed reflect their gravitational potentials.

Donahue, Megan↗
NASA NTRS1998

The CfA-Rosat Survey of Distant Clusters of Galaxies

We (Vikhlinin, McNamara, Forman, Jones, Hornstrup, Quintana) have completed a new survey of distant clusters of galaxies, which we use to to study cluster evolution over cosmological timescales. The clusters were identified as extended X-ray sources in 650 ROSAT PSPC images of high Galactic latitude fields. Our catalog of approximately 230 extended X-ray sources covers 160 square degrees on the sky. Ours is the largest of the several ROSAT serendipitous cluster surveys in progress (e.g. SHARC, Rosati, WARPS etc.). Using V,R,I imagery obtained at several observatories, we find that greater than 90% of the X-ray sources are associated with distant clusters of galaxies. We have obtained spectroscopic redshifts for nearly 80 clusters in our catalog, and we have measured photometric redshifts for the remaining clusters. Our sample contains more than 20 clusters at z > 0.5. I will discuss the logN-logS relationship for our clusters. Because our large survey area, we are able to confirm the evolution of the most luminous distant clusters first seen in the Einstein Extended Medium Sensitivity Survey. In addition, I will discuss the relationships between optical richness, core radius, and X-ray luminosity for distant, X-ray-selected clusters.

McNamara, Brian↗
NASA NTRS1999

Characterizing the Small Scale Structure in Clusters of Galaxies

We have applied a wavelet transform analysis to quantify the small-scale (less than a few arcminutes) X-ray structure of clusters of galaxies to several clusters. Three of these clusters show interesting structure. In addition to the small scale structure in surface brightness, we have searched for temperature structure using Advanced Satellite for Cosmology and Astrophysics (ASCA) observations. For A754, we find a filamentary structure, similar to that previously detected in A85. For A119, we find small scale structure associated with both cool and hot regions with a hot filament extending to the northeast. For A1750, one of the first clusters to show substructure from Einstein, we have a rederived the substructure from the ROSAT observations. The ASCA observations show a hot region between the two small scale features - sub-clusters. The hot region shows that the two subclusters are merging and producing a shock wave at the leading edge of the interaction. This analysis had been applied to a sample of 17 clusters to determine the radial distribution of substructure in a sample. The number of detected substructures was rather small and it was decided to select a different cluster sample at larger redshift. This provided a larger radial range over which substructure could be detected. This new cluster sample also includes about 20 clusters and is being analyzed. Finally, we are considering how to compare the substructure parameters of nearby clusters with a more distant sample to see if one can extract cosmological information. Since substructure frequency should depend on the cosmological density parameter, it may be possible to derive new constraints by comparing nearby and distant clusters.

Forman, William R.↗
NASA NTRS1998

A Very Hot, High Redshift Cluster of Galaxies: More Trouble for Omega(0) = 1

We have observed the most distant (= 0.829) cluster of galaxies in the Einstein Extended Medium Sensitivity Survey (EMSS), with the ASCA and ROSAT satellites. We find an X-ray temperature of 12.3 (sup +3.1) (sub -2.2)keV for this cluster, and the ROSAT map reveals significant substructure. The high temperature of MS1054-0321 is consistent with both its approximate velocity dispersion, based on the redshifts of 12 cluster members we have obtained at the Keck and the Canada-France-Hawaii telescopes, and with its weak lensing signature. The X-ray temperature of this cluster implies a virial mass approx. 7.4 x 10 (sup 14) h (sup -1) M (circle dot), if the mean matter density in the universe equals the critical value (OMEGA (sub 0) = 1), or larger if OMEGA (sub 0) is less than 1. Finding such a hot, massive cluster in the EMSS is extremely improbable if clusters grew from Gaussian perturbations in an OMEGA (sub 0) = 1 universe. Combining the assumptions that OMEGA (sub 0) = 1 and that the initial perturbations were Gaussian with the observed X-ray temperature function at low redshift, we show that this probability of this cluster occurring in the volume sampled by the EMSS is less than a few times 10 (sup -5). Nor is MS1054-0321 the only hot cluster at high redshift; the only two other z greater than 0.5 EMSS clusters already observed with ASCA also have temperatures exceeding 8 keV. Assuming again that the initial perturbations were Gaussian and OMEGA (sub 0) = 1, we find that each one is improbable at the less than 10 (sup -2) level. These observations, along with the fact that these luminosities and temperatures of the high-z clusters all agree with the low-z L (sub X) - T (sub X) relation, argue strongly that OMEGA (sub 0) less than 1. Otherwise, the initial perturbations must be non-Gaussian, if these clusters' temperatures do indeed reflect their gravitational potentials.

Donahue, Megan↗
NASA NTRS2004

Using Fuzzy Clustering for Real-time Space Flight Safety

To ensure space flight safety, it is necessary to monitor myriad sensor readings on the ground and in flight. Since a space shuttle has many sensors, monitoring data and drawing conclusions from information contained within the data in real time is challenging. The nature of the information can be critical to the success of the mission and safety of the crew and therefore, must be processed with minimal data-processing time. Data analysis algorithms could be used to synthesize sensor readings and compare data associated with normal operation with the data obtained that contain fault patterns to draw conclusions. Detecting abnormal operation during early stages in the transition from safe to unsafe operation requires a large amount of historical data that can be categorized into different classes (non-risk, risk). Even though the 40 years of shuttle flight program has accumulated volumes of historical data, these data don t comprehensively represent all possible fault patterns since fault patterns are usually unknown before the fault occurs. This paper presents a method that uses a similarity measure between fuzzy clusters to detect possible faults in real time. A clustering technique based on a fuzzy equivalence relation is used to characterize temporal data. Data collected during an initial time period are separated into clusters. These clusters are characterized by their centroids. Clusters formed during subsequent time periods are either merged with an existing cluster or added to the cluster list. The resulting list of cluster centroids, called a cluster group, characterizes the behavior of a particular set of temporal data. The degree to which new clusters formed in a subsequent time period are similar to the cluster group is characterized by a similarity measure, q. This method is applied to downlink data from Columbia flights. The results show that this technique can detect an unexpected fault that has not been present in the training data set.

Lee, Charles↗
NASA NTRS2001

Greenhouse tomato limited cluster production systems: crop management practices affect yield

Limited-cluster production systems may be a useful strategy to increase crop production and profitability for the greenhouse tomato (Lycopersicon esculentum Mill). In this study, using an ebb-and-flood hydroponics system, we modified plant architecture and spacing and determined the effects on fruit yield and harvest index at two light levels. Single-cluster plants pruned to allow two leaves above the cluster had 25% higher fruit yields than did plants pruned directly above the cluster; this was due to an increase in fruit weight, not fruit number. Both fruit yield and harvest index were greater for all single-cluster plants at the higher light level because of increases in both fruit weight and fruit number. Fruit yield for two-cluster plants was 30% to 40% higher than for single-cluster plants, and there was little difference in the dates or length of the harvest period. Fruit yield for three-cluster plants was not significantly different from that of two-cluster plants; moreover, the harvest period was delayed by 5 days. Plant density (5.5, 7.4, 9.2 plants/m2) affected fruit yield/plant, but not fruit yield/unit area. Given the higher costs for materials and labor associated with higher plant densities, a two-cluster crop at 5.5 plants/m2 with two leaves above the cluster was the best of the production system strategies tested.

Non-NASA Center↗
NASA NTRS2001

Clustered DNA damages induced by high and low LET radiation, including heavy ions

Clustered DNA damages--here defined as two or more lesions (strand breaks, oxidized purines, oxidized pyrimidines or abasic sites) within a few helical turns--have been postulated as difficult to repair accurately, and thus highly significant biological lesions. Further, attempted repair of clusters may produce double strand breaks (DSBs). However, until recently, there was no way to measure ionizing radiation-induced clustered damages, except DSB. We recently described an approach for measuring classes of clustered damages (oxidized purine clusters, oxidized pyrimidine clusters, abasic clusters, along with DSB). We showed that ionizing radiation (gamma rays and Fe ions, 1 GeV/amu) does induce such clusters in genomic DNA in solution and in human cells. These studies also showed that each damage cluster results from one radiation hit (and its track), thus indicating that they can be induced by very low doses of radiation, i.e. two independent hits are not required for cluster induction. Further, among all complex damages, double strand breaks comprise--at most-- ~20%, with the other clustered damages being at least 80%.

NASA Discipline Radiation Health↗
NASA NTRS1998

Method and system for data clustering for very large databases

Multi-dimensional data contained in very large databases is efficiently and accurately clustered to determine patterns therein and extract useful information from such patterns. Conventional computer processors may be used which have limited memory capacity and conventional operating speed, allowing massive data sets to be processed in a reasonable time and with reasonable computer resources. The clustering process is organized using a clustering feature tree structure wherein each clustering feature comprises the number of data points in the cluster, the linear sum of the data points in the cluster, and the square sum of the data points in the cluster. A dense region of data points is treated collectively as a single cluster, and points in sparsely occupied regions can be treated as outliers and removed from the clustering feature tree. The clustering can be carried out continuously with new data points being received and processed, and with the clustering feature tree being restructured as necessary to accommodate the information from the newly received data points.

Zhang, Tian↗
NASA NTRS2007

Monitoring by Use of Clusters of Sensor-Data Vectors

The inductive monitoring system (IMS) is a system of computer hardware and software for automated monitoring of the performance, operational condition, physical integrity, and other aspects of the health of a complex engineering system (e.g., an industrial process line or a spacecraft). The input to the IMS consists of streams of digitized readings from sensors in the monitored system. The IMS determines the type and amount of any deviation of the monitored system from a nominal or normal ( healthy ) condition on the basis of a comparison between (1) vectors constructed from the incoming sensor data and (2) corresponding vectors in a database of nominal or normal behavior. The term inductive reflects the use of a process reminiscent of traditional mathematical induction to learn about normal operation and build the nominal-condition database. The IMS offers two major advantages over prior computational monitoring systems: The computational burden of the IMS is significantly smaller, and there is no need for abnormal-condition sensor data for training the IMS to recognize abnormal conditions. The figure schematically depicts the relationships among the computational processes effected by the IMS. Training sensor data are gathered during normal operation of the monitored system, detailed computational simulation of operation of the monitored system, or both. The training data are formed into vectors that are used to generate the database. The vectors in the database are clustered into regions that represent normal or nominal operation. Once the database has been generated, the IMS compares the vectors of incoming sensor data with vectors representative of the clusters. The monitored system is deemed to be operating normally or abnormally, depending on whether the vector of incoming sensor data is or is not, respectively, sufficiently close to one of the clusters. For this purpose, a distance between two vectors is calculated by a suitable metric (e.g., Euclidean distance) and "sufficiently close" signifies lying at a distance less than a specified threshold value. It must be emphasized that although the IMS is intended to detect off-nominal or abnormal performance or health, it is not necessarily capable of performing a thorough or detailed diagnosis. Limited diagnostic information may be available under some circumstances. For example, the distance of a vector of incoming sensor data from the nearest cluster could serve as an indication of the severity of a malfunction. The identity of the nearest cluster may be a clue as to the identity of the malfunctioning component or subsystem. It is possible to decrease the IMS computation time by use of a combination of cluster-indexing and -retrieval methods. For example, in one method, the distances between each cluster and two or more reference vectors can be used for the purpose of indexing and retrieval. The clusters are sorted into a list according to these distance values, typically in ascending order of distance. When a set of input data arrives and is to be tested, the data are first arranged as an ordered set (that is, a vector). The distances from the input vector to the reference points are computed. The search of clusters from the list can then be limited to those clusters lying within a certain distance range from the input vector; the computation time is reduced by not searching the clusters at a greater distance.

Iverson, David L.↗
NASA NTRS2010

The Atacama Cosmology Telescope: Cosmology from Galaxy Clusters Detected Via the Sunyaev-Zel'dovich Effect

We present constraints on cosmological parameters based on a sample of Sunyaev-Zel'dovich-selected galaxy clusters detected in a millimeter-wave survey by the Atacama Cosmology Telescope. The cluster sample used in this analysis consists of 9 optically-confirmed high-mass clusters comprising the high-significance end of the total cluster sample identified in 455 square degrees of sky surveyed during 2008 at 148 GHz. We focus on the most massive systems to reduce the degeneracy between unknown cluster astrophysics and cosmology derived from SZ surveys. We describe the scaling relation between cluster mass and SZ signal with a 4-parameter fit. Marginalizing over the values of the parameters in this fit with conservative priors gives (sigma)8 = 0.851 +/- 0.115 and w = -1.14 +/- 0.35 for a spatially-flat wCDM cosmological model with WMAP 7-year priors on cosmological parameters. This gives a modest improvement in statistical uncertainty over WMAP 7-year constraints alone. Fixing the scaling relation between cluster mass and SZ signal to a fiducial relation obtained from numerical simulations and calibrated by X-ray observations, we find (sigma)8 + 0.821 +/- 0.044 and w = -1.05 +/- 0.20. These results are consistent with constraints from WMAP 7 plus baryon acoustic oscillations plus type Ia supernova which give (sigma)8 = 0.802 +/- 0.038 and w = -0.98 +/- 0.053. A stacking analysis of the clusters in this sample compared to clusters simulated assuming the fiducial model also shows good agreement. These results suggest that, given the sample of clusters used here, both the astrophysics of massive clusters and the cosmological parameters derived from them are broadly consistent with current models.

Sehgal, Neelima↗
NASA NTRS2012

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

Wagstaff, Kiri L.↗
DOE OSTI2025

Environmental controls on the kinetics of iron-sulfur cluster nucleation and nanoparticle formation

Anoxic, sulfidic conditions have been prevalent since the early Proterozoic and favor aqueous iron-sulfur (FeS aq ) clusters as a major fraction of the soluble, reduced iron and sulfur pool. FeS aq cluster formation and nucleation is driven by the high affinity between ferrous iron (Fe(II)) and sulfide (HS − ), ultimately yielding particles that precipitate as iron sulfide minerals. FeS aq clusters were recently shown to be bioavailable sources of iron and sulfur for a variety of anaerobes, yet little is known of the factors that influence the kinetics of their formation and nucleation. Here we apply computational and spectroscopic approaches to investigate the dynamics of FeS aq nucleation, cluster growth, precipitation, and redissolution as a function of Fe(II)/HS − concentration, temperature, and pH. Experiments were conducted under excess HS − to mimic euxinic conditions common to contemporary anaerobic aquatic ecosystems and those of the Proterozoic. Density functional theory calculations reveal the key role of water oxygen-iron interactions in stabilizing small FeS aq clusters and promoting solubility. Dynamic light scattering revealed a concentration-dependent increase in the kinetics of FeS aq nucleation and cluster aggregation. Increasing temperature promoted FeS aq cluster nucleation and aggregation while also enhancing dissolution. Alkaline pH also promoted FeS aq nucleation and cluster aggregation. At 25 °C, pH 7.0, and at reactant concentrations of 30 µM, FeS aq clusters < 10 nm in diameter remained in solution for > 2 h. These results underscore the importance of temperature, pH, and reactant concentration in the kinetics of FeS aq nucleation and cluster growth that, in turn, influence their bioavailability in anaerobic ecosystems.

Aquatic ecosystems↗