Discovering melting temperature prediction models of inorganic solids by combining supervised and unsupervised learning.

The melting temperature is important for materials design because of its relationship with thermal stability, synthesis, and processing conditions. Current empirical and computational melting point estimation techniques are limited in scope, computational feasibility, or interpretability. We report the development of a machine learning methodology for predicting melting temperatures of binary ionic solid materials. We evaluated different machine-learning models trained on a dataset of the melting points of 476 non-metallic crystalline binary compounds using materials embeddings constructed from elemental properties and density-functional theory calculations as model inputs. A direct supervised-learning approach yields a mean absolute error of around 180 K but suffers from low interpretability. We find that the fidelity of predictions can further be improved by introducing an additional unsupervised-learning step that first classifies the materials before the melting-point regression. Not only does this two-step model exhibit improved accuracy, but the approach also provides a level of interpretability with insights into feature importance and different types of melting that depend on the specific atomic bonding inside a material. Motivated by this finding, we used a symbolic learning approach to find interpretable physical models for the melting temperature, which recovered the best-performing features from both prior models and provided additional interpretability.

Spectrum of somatic mutational features of colorectal tumors in ancestrally diverse populations.

Ancestrally diverse and admixed populations, including the Hispanic/Latino/a/x/e community, are underrepresented in cancer genetic and genomic studies. Leveraging the Latino Colorectal Cancer Consortium, we analyzed whole exome sequencing data on tumor/normal pairs from 718 individuals with colorectal cancer (128 Latino, 469 non-Latino) to map somatic mutational features by ethnicity and genetic ancestry. Global proportions of African, East Asian, European, and Native American ancestries were estimated using ADMIXTURE. Associations between global genetic ancestry and somatic mutational features across genes were examined using logistic regression. TP53 , APC , and KRAS were the most recurrently mutated genes. Compared to non-Latino individuals, tumors from Latino individuals had fewer KRAS (OR=0.64, 95%CI=0.41-0.97, p=0.037) and PIK3CA mutations (OR=0.55, 95%CI=0.31-0.98, p=0.043). Genetic ancestry was associated with presence of somatic mutations in 39 genes (FDR-adjusted LRT p<0.05). Among these genes, a 10% increase in African ancestry was associated with significantly higher odds of mutation in KNCN (OR=1.34, 95%CI=1.09-1.66, p=5.74×10 -3 ) and TMEM184B (OR=1.53, 95%CI=1.10-2.12, p=0.011). Among RMGs, we found evidence of association between genetic ancestry and mutation status in CDC27 (LRT p=0.0084) and between SMAD2 mutation status and AFR ancestry (OR=1.14, 95%CI=1.00-1.30, p=0.046). Ancestry was not associated with tumor mutational burden. Individuals with above-average Native American ancestry had a lower frequency of microsatellite instable (MSI-H) vs microsatellite stable tumors (OR=0.45, 95%CI=0.21-0.99, p=0.048). Our findings provide new knowledge about the relationship between ancestral haplotypes and somatic mutational profiles that may be useful in developing precision medicine approaches and provide additional insight into genomic contributions to cancer disparities. Significance: Our data in ancestrally diverse populations adds essential information to characterize mutational features in the colorectal cancer genome. These results will help enhance equity in the development of precision medicine strategies.

ConcreteXAI: A multivariate dataset for concrete strength prediction via deep-learning-based methods.

Concrete is a prominent construction material globally, owing to its reputed attributes such as robustness, endurance, optimal functionality, and adaptability. Formulating concrete mixtures poses a formidable challenge, mainly when introducing novel materials and additives and evaluating diverse design resistances. Recent methodologies for projecting concrete performance in fundamental aspects, including compressive strength, flexural strength, tensile strength, and durability (encompassing homogeneity, porosity, and internal structure), exist. However, actual approaches need more diversity in the materials and properties considered in their analyses. This dataset outlines the outcomes of an extensive 10-year laboratory investigation into concrete materials involving mechanical tests and non-destructive assessments within a comprehensive dataset denoted as ConcreteXAI. This dataset encompasses evaluations of mechanical performances and non-destructive tests. ConcreteXAI integrates a spectrum of analyzed mixtures comprising twelve distinct concrete formulations incorporating diverse additives and aggregate types. The dataset encompasses 18,480 data points, establishing itself as a cutting-edge resource for concrete analysis. ConcreteXAI acknowledges the influence of artificial intelligence techniques in various science fields. Emphatically, deep learning emerges as a precise methodology for analyzing and constructing predictive models. ConcreteXAI is designed to seamlessly integrate with deep learning models, enabling direct application of these models to predict or estimate desired attributes. Consequently, this dataset offers a resourceful avenue for researchers to develop high-quality prediction models for both mechanical and non-destructive tests on concrete elements, employing advanced deep learning techniques.

Selective anisotropic growth of Bi2S3 nanoparticles with adjustable optical properties.

We report on the controlled synthesis and functionalization in two steps of elongated Bi2S3 nanoparticles within a wide range of sizes. First, we show the effect of the temperature and reaction time on the synthesis of two series of nanoparticles by the reaction of thioacetamide with bismuth(III) neodecanoate in the presence of organic surfactants. At 105 °C and long reaction times, nanoneedles of about 45 nm in length containing larger crystallites are obtained, while highly crystalline nanorods of about 30 nm in length are dominant at 165 °C, regardless of the reaction time. The optical properties of both types of nanoparticles show an enhancement of the band gap compared to bulk Bi2S3. This is likely to arise from quantum confinement effects caused by the small particle dimensions relative to the typical exciton size, together with an increase in near-infrared absorption due to the anisotropic particle shape. Second, a ligand exchange approach has been developed to transfer the Bi2S3 nanoparticles to aqueous solutions by grafting dimercaptosuccinic acid onto the surface of the particles. The as-prepared coated nanoparticles show good stability in water, in a wide biological pH range, and in phosphate-buffered saline solutions. Overall, this work highlights the controlled design at all levels - from the inorganic core to the organic surface coating - of elongated Bi2S3 nanoparticles, leading to a tunable optical response by tuning their morphology and size.

Research Infrastructure Core Facilities at Research Centers in Minority Institutions: Part I-Research Resources Management, Operation, and Best Practices.

Funded by the National Institutes of Health (NIH), the Research Centers in Minority Institutions (RCMI) Program fosters the development and implementation of innovative research aimed at improving minority health and reducing or eliminating health disparities. Currently, there are 21 RCMI Specialized (U54) Centers that share the same framework, comprising four required core components, namely the Administrative, Research Infrastructure, Investigator Development, and Community Engagement Cores. The Research Infrastructure Core (RIC) is fundamentally important for biomedical and health disparities research as a critical function domain. This paper aims to assess the research resources and services provided and evaluate the best practices in research resources management and networking across the RCMI Consortium. We conducted a REDCap-based survey and collected responses from 57 RIC Directors and Co-Directors from 98 core leaders. Our findings indicated that the RIC facilities across the 21 RCMI Centers provide access to major research equipment and are managed by experienced faculty and staff who provide expert consultative and technical services. However, several impediments to RIC facilities operation and management have been identified, and these are currently being addressed through implementation of cost-effective strategies and best practices of laboratory management and operation.

Effect of the Addition of Agribusiness and Industrial Wastes as a Partial Substitution of Portland Cement for the Carbonation of Mortars.

The present research work shows the effect on the carbonation of Portland cement-based mortars (PC) with the addition of green materials, specifically residues from two groups: agricultural and industrial wastes, and minerals and fibres. These materials have the purpose of helping with the waste disposal, recycling, and improving the durability of concrete structures. The specimens used for the research were elaborated with CPC 30R RS, according to the Mexican standard NMX-C-414, which is equivalent to the international ASTM C150. The aggregates were taken from the rivers Lerma and Huajumbaro, in the State of Michoacan, Mexico, and the water/cement relation was 1:1 in weight. The carbonation analyses were performed with cylinder specimens in an accelerated carbonation test chamber with conditions of 65 +/- 5% of humidity and 25 +/- 2 °C temperature. The results showed that depending on the PC substitutions, the carbonation front advance of the specimens can increase or decrease. It is highlighted that the charcoal ashes, blast-furnace slags, and natural perlite helped to reduce the carbonation advance compared to the control samples, consequently, they contributed to the durability of concrete structures. Conversely, the sugarcane bagasse ash, brick manufacturing ash, bottom ash, coal, expanded perlite, metakaolin, and opuntia ficus-indica dehydrated fibres additions increased the velocity of carbonation front, helping with the sequestration of greenhouse gases, such as CO2, and reducing environmental pollution.

Engineering the Multi-Enzymatic Activity of Cerium Oxide Nanoparticle Coatings for the Antioxidant Protection of Implants.

Imbalance of oxidants is a universal contributor to the failure of implanted devices and tissues. A sustained oxidative environment leads to cytotoxicity, prolonged inflammation, and ultimately host rejection of implanted devices/grafts. The incorporation of antioxidant materials can inhibit this redox/inflammatory cycle and enhance implant efficacy. Cerium oxide nanoparticles (CONP) is a highly promising agent that exhibits potent, ubiquitous, and self-renewable antioxidant properties. Integrating CONP as surface coatings provides ease in translating antioxidant properties to various implants/grafts. Herein, we describe the formation of CONP coatings, generated via the sequential deposition of CONP and alginate, and the impact of coating properties, pH, and polymer molecular weight, on their resulting redox profile. Investigation of CONP deposition, layer formation, and coating uniformity/thickness on their resulting oxidant scavenging activity identified key parameters for customizing global antioxidant properties. Results found lower molecular weight alginates and physiological pH shift CONP activity to a higher H2O2 to O2 --scavenging capability. The antioxidant properties measured for these various coatings translated to distinct antioxidant protection to the underlying encapsulated cells. Information gained from this work can be leveraged to tailor coatings towards specific oxidant-scavenging applications and prolong the function of medical devices and cellular implants.

Origin of enhanced water oxidation activity in an iridium single atom anchored on NiFe oxyhydroxide catalyst.

The efficiency of the synthesis of renewable fuels and feedstocks from electrical sources is limited, at present, by the sluggish water oxidation reaction. Single-atom catalysts (SACs) with a controllable coordination environment and exceptional atom utilization efficiency open new paradigms toward designing high-performance water oxidation catalysts. Here, using operando X-ray absorption spectroscopy measurements with calculations of spectra and electrochemical activity, we demonstrate that the origin of water oxidation activity of IrNiFe SACs is the presence of highly oxidized Ir single atom (Ir5.3+) in the NiFe oxyhydroxide under operating conditions. We show that the optimal water oxidation catalyst could be achieved by systematically increasing the oxidation state and modulating the coordination environment of the Ir active sites anchored atop the NiFe oxyhydroxide layers. Based on the proposed mechanism, we have successfully anchored Ir single-atom sites on NiFe oxyhydroxides (Ir0.1/Ni9Fe SAC) via a unique in situ cryogenic-photochemical reduction method that delivers an overpotential of 183 mV at 10 mA ⋅ cm-2 and retains its performance following 100 h of operation in 1 M KOH electrolyte, outperforming the reported catalysts and the commercial IrO2 catalysts. These findings open the avenue toward an atomic-level understanding of the oxygen evolution of catalytic centers under in operando conditions.

Estimation of the main conditions in (SARS-CoV-2) Covid-19 patients that increase the risk of death using Machine learning, the case of Mexico.

Nowadays, society faces a catastrophic problem related to respiratory syndrome due to the coronavirus SARS-CoV-2: the Covid-19 disease. This virus has changed our coexistence rules and, in consequence, has reshaped the daily activities in modern societies. Thus, there are many efforts to understand the virus behaviour in order to reduce its negative impact, and these efforts produce an incredible amount of information and data sources every week. Data scientists, which use techniques such as Machine learning, are focusing their abilities to develop mathematical models for analysing this critical situation. This paper uses Machine Learning techniques as tools to help understand some specific new patterns in Covid patients that arise from unknown complex interactions in the transmission-dynamic models of the SARS-CoV-2 virus, and their relation with the corresponding social contact patterns which are often known or can be inferred from populations variables. One of the main motivations of this research is to find the diseases that cause an increase in the risk of death in infected people with the Covid-19 virus. Mexico is the case of study in this research. The general conditions of health that cause death are well known generally in the world. However, these conditions in each country can differ depending on different factors such as the general health status of people. The results show that the principal causes of death in Mexico are related to age, bad eating habits, chronic diseases, and contact with infected people having not proper care. Results from the analysis show a remarkable accuracy of 87%, which is considered satisfactory.

Ibero-American Endometriosis Patient Phenome: Demographics, Obstetric-Gynecologic Traits, and Symptomatology.

Background: An international collaborative study was conducted to determine the demographic and clinical profiles of Hispanic/Latinx endometriosis patients from Latin America and Spain using the Minimal Clinical Questionnaire developed by the World Endometriosis Research Foundation (WERF) Endometriosis Phenome and Biobanking Harmonization Project (EPHect). Methods: This is a cross-sectional study to collect self-reported data on demographics, lifestyle, and endometriosis symptoms of Hispanic/Latinx endometriosis patients from April 2019 to February 2020. The EPHect Minimal Clinical Questionnaire (EPQ-M) was translated into Spanish. Comprehension and length of the translated survey were assessed by Spanish-speaking women. An electronic link was distributed via social media of endometriosis patient associations from 11 Latin American countries and Spain. Descriptive statistics (frequency, means and SD, percentages, and proportions) and correlations were conducted using SPSSv26. Results: The questionnaire was completed by 1,378 participants from 23 countries; 94.6% had self-reported diagnosis of endometriosis. Diagnostic delay was 6.6 years. Most participants had higher education, private health insurance, and were employed. The most common symptoms were back/leg pain (85.4%) and fatigue (80.7%). The mean number of children was 1.5; 34.4% had miscarriages; the mean length of infertility was 3.7 years; 47.2% reported pregnancy complications. The most common hormone treatment was oral contraceptives (47.0%). The most common comorbidities were migraines (24.1%), polycystic ovary syndrome (PCOS) (22.2%), and irritable bowel syndrome (21.1%). Most participants (97.0%) experienced pelvic pain during menses; for 78.7%, pain was severe; 86.4% reported dyspareunia. The mean age of dysmenorrhea onset was 16.2 years (SD ± 6.1). Hormone treatments were underutilized, while impact was substantial. Pain catastrophizing scores were significantly correlated with pain intensity (p < 0.001). Conclusion: This is the first comprehensive effort to generate a clinical-demographic profile of Hispanic/Latinx endometriosis patients. Differences in clinical presentation compared to other cohorts included higher prevalence and severity of dysmenorrhea and dyspareunia and high levels of pain catastrophizing. Though future studies are needed to dissect the impact of race and ethnicity on pain and impact, this profile is the first step to facilitate the recognition of risk factors and diagnostic features and promote improved clinical management of this patient population. The EPHect questionnaire is an efficient tool to capture data to allow comparisons across ethnicities and geographic regions and tackle disparities in endometriosis research.

Machine learning with bond information for local structure optimizations in surface science.

Local optimization of adsorption systems inherently involves different scales: within the substrate, within the molecule, and between the molecule and the substrate. In this work, we show how the explicit modeling of different characteristics of the bonds in these systems improves the performance of machine learning methods for optimization. We introduce an anisotropic kernel in the Gaussian process regression framework that guides the search for the local minimum, and we show its overall good performance across different types of atomic systems. The method shows a speed-up of up to a factor of two compared with the fastest standard optimization methods on adsorption systems. Additionally, we show that a limited memory approach is not only beneficial in terms of overall computational resources but can also result in a further reduction of energy and force calculations.

Decision-making and therapeutic options in intact splenic artery aneurysms: single-center experience and literature review.

BACKGROUND: Splenic artery aneurysms are rare, potentially serious, and usually asymptomatic. Several methods are currently available to treat them, each with their own advantages and drawbacks. Therefore, its therapeutic paradigm has changed. METHODS: We review our database of splenic aneurysms (2009-2019) and undertake an exhaustive literature review. Demographic, clinical, diagnostic, therapeutic, early and follow-up outcome data were examined. Our experience comprised: 15 patients with 19 splenic aneurysms. 11 women (average age, 59.4 years) and 4 men (average age, 61.7 years). All asymptomatic. RESULTS: At diagnosis, aneurysms had a mean cross-sectional diameter of 3.4 cm (3.2 and 3.9 for women and men, respectively), the largest measuring 8.5 cm. Two independent aneurysms were detected in four patients. Diagnoses were always incidental to a CT scan. Treatments consisted of open surgery (2 patients), endovascular surgery (10 patients: 7 embolizations, 3 covered stent) and observation/follow-up (3 patients). The cases of open surgery (with splenectomy) were carried out without postoperative morbidity. One embolization failed (requiring subsequent open surgery) and two suffered localized splenic infarction, but without further complications. In patients treated with a covered stent, the aneurysm was always excluded, without complications. There was no 30-day or follow-up (average 26.2 months) mortality. Splenic aneurysms are diagnosed more frequently and earlier (in the asymptomatic phase), albeit incidentally, than in the past. CONCLUSIONS: The correct indication (identifying patients at risk) and individualization of treatment, in which endovascular techniques are the first-line option, have significantly improved morbidity and mortality outcomes in our hospital.

Spontaneous rupture of a mechanical valve in a mitral position (On-X) with migration-embolization to aortic bifurcation from the perspective of the vascular surgeon.

We report on the case of spontaneous rupture of an On-X-pure pyrolytic carbon mechanical valve prosthesis implanted seven years earlier, in a mitral position, at our hospital. The patient was admitted with valvular dysfunction and acute pulmonary edema requiring emergency surgery (prosthesis replacement); the absence of a leaflet was confirmed intraoperatively. The patient presented severe respiratory failure, which prolonged the postoperative period. A CT scan showed that the migrated leaflet was located in the aortic bifurcation with no apparent arterial lesion. Four months later, once the patient had recovered, laparotomy and aortotomy were performed in order to retrieve the leaflet, which was found to have become included (neoendothelized) in the aortic wall without compromising the latter's integrity or obstructing the blood flow. A subsequent CT scan confirmed the persistence of the leaflet in its initial position. The literature review highlights two singular facts: 1) this is the second published case of the escape of a leaflet from an On-X prosthesis (the first patient died); 2) this is the first case in which a laparotomy was performed to retrieve the leaflet but finally a decision was made to leave it in situ. Seven months later, the patient remained asymptomatic.

A flow-through microfluidic system for the detection of circulating melanoma cells.

We report the fabrication of polyaniline nanofiber (PANI)-modified screen-printed electrode (PANI/SPE) incorporated in a poly-dimethylsiloxane (PDMS) microfluidic channel for the detection of circulating tumor cells. We employed this device to detect melanoma skin cancer cells through specific immunogenic binding of cell surface biomarker melanocortin 1 receptor (MC1R) to anti-MC1R antibody. The antibody-functionalized PANI/SPE was used in batch-continuous flow-through fashion. An aqueous cell suspension of ferri/ferrocyanide at a flow rate of 1.5 mL/min was passed over the immunosensor, which allowed for continuous electrochemical measurements. The sensor performed exceptionally well affording an ultralow limit of quantification of 1 melanoma cell/mL, both in buffer and when mixed with peripheral blood mononuclear cells, and the response was log-linear over the range of 10-9000 melanoma cells/10 mL.

Elucidation of temperature-programmed desorption of high-coverage hydrogen on Pt(211), Pt(221), Pt(533) and Pt(553) based on density functional theory calculations.

In this work we compute high-coverage hydrogen adsorption energies and geometries on the stepped platinum surfaces Pt(211) and Pt(533) which contain a (100)-step type and the Pt(221) and Pt(553) surface with a (111) step edge. We discuss these results in relation to ultra-high-vacuum temperature programmed desorption (TPD) data to elucidate the origin of the desorption features. Our results indicated that on surfaces with a (100)-step type, two distinct ranges of adsorption energy for the step and terrace are observed, which mirrors the TPD spectra for which we find a clear separation of the desorption peaks. For the (111) step type, the TPD spectra show much less separation of the step and terrace features, which we assign to the low individual adsorption energies for H atoms on this step edge. From our results we obtain a much clearer understanding of the surface-hydrogen bonding at high coverages and the origin of the different TPD features present for the two step types studied.

Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model.

We present the incorporation of a surrogate Gaussian process regression (GPR) atomistic model to greatly accelerate the rate of convergence of classical nudged elastic band (NEB) calculations. In our surrogate model approach, the cost of converging the elastic band no longer scales with the number of moving images on the path. This provides a far more efficient and robust transition state search. In contrast to a conventional NEB calculation, the algorithm presented here eliminates any need for manipulating the number of images to obtain a converged result. This is achieved by inventing a new convergence criteria that exploits the probabilistic nature of the GPR to use uncertainty estimates of all images in combination with the force in the saddle point in the target model potential. Our method is an order of magnitude faster in terms of function evaluations than the conventional NEB method with no accuracy loss for the converged energy barrier values.

Patient-specific organotypic blood vessels as an in vitro model for anti-angiogenic drug response testing in renal cell carcinoma.

BACKGROUND: Anti-angiogenic treatment failure is often attributed to drug resistance, unsuccessful drug delivery, and tumor heterogeneity. Recent studies have speculated that anti-angiogenic treatments may fail due to characteristics inherent to tumor-associated blood vessels. Tumor-associated blood vessels are phenotypically different from their normal counterparts, having defective or permeable endothelial monolayers, abnormal sprouts, and abnormal vessel hierarchy. Therefore, to predict the efficacy of anti-angiogenic therapies in an individual patient, in vitro models that mirror individual patient's tumor vascular biology and response to anti-angiogenic treatment are needed. METHODS: We used a microfluidic in vitro organotypic model to create patient-specific biomimetic blood vessels from primary patient-specific tumor endothelial cells (TEnCs) and normal endothelial cells (NEnC). We assessed number of sprouts and vessel organization via microscopy imaging and image analysis. We characterized NEnC and TEnC vessel secretions via multiplex bead-based ELISA. FINDINGS: Using this model, we found that TEnC vessels exhibited more angiogenic sprouts than NEnC vessels. We also found a more disorganized and gap-filled endothelial monolayer. NEnCs and TEnC vessels exhibited heterogeneous functional drug responses across the five patients screened, as described in the clinic. INTERPRETATION: Our model recapitulated hallmarks of TEnCs and NEnCs found in vivo and captured the functional and structural differences between TEnC and NEnC vessels. This model enables a platform for therapeutic drug screening and assessing patient-specific responses with great potential to inform personalized medicine approaches. FUNDING: NIH grants R01 EB010039, R33 CA225281, R01CA186134 University of Wisconsin Carbone Cancer Center (CA014520), and University of Wisconsin Hematology training grant T32 HL07899.

Prediction of Stable and Active (Oxy-Hydro) Oxide Nanoislands on Noble-Metal Supports for Electrochemical Oxygen Reduction Reaction.

Developing cost-effective oxygen electrocatalysts with high activity and stability is key to their commercialization. However, economical earth-abundant catalysts based on first-row transition-metal oxides suffer from low electrochemical stability, which is difficult to improve without compromising their activity. Here, using density functional theory calculations, we demonstrate that noble-metal supports lead to bifunctional enhancement of both the stability and the oxygen reduction reaction (ORR) activity of metal (oxy-hydro) oxide nanoislands. We observe a significant stabilization of supported nanoislands beyond the intrinsic stability limits of bulk phases, which originates from a favorable lattice mismatch and reductive charge transfer from oxophilic supports. We discover that interfacial active sites (located between the nanoisland and the support) reinforce the binding strength of reaction intermediates, hence boosting ORR activity. Considering that both stability and activity lead to discovery of CoOOH|Pt, NiOOH|Ag, and FeO2|Ag as viable systems for alkaline ORR, we then use a multivariant linear regression method to identify elementary descriptors for efficient screening of promising cost-effective nanoisland|support catalysts.

Strong Substrate Mediation of Attractive Lateral Interactions of CO on Cu(110).

The mechanism of chemical reactions between adsorbed species is defined by the combined effects of the adsorbate-substrate potential landscape and lateral interactions. Such lateral interactions are therefore integral to catalytic processes, but their study is often complicated by "substrate mediation", the regulation of a two-body potential between adsorbed particles by the surface itself. Substrate mediation can influence the sign and magnitude of lateral interactions. There are notable exceptions of ordered structures forming at low coverage, indicative of short-range attractive forces where repulsive forces are expected to dominate, suggesting a strong substrate-mediated contribution. To explore further the origins of such interactions, we have investigated the adsorption of CO on Cu(110) using a combination of low-temperature microscopy and first-principles calculations. Our studies reveal that lateral adsorbate interactions, which are constrained by the metal surface, regulate the bonding between the adsorbate and substrate. Anisotropic CO-CO coupling is seen to arise from a perfect balance between the intermolecular accumulation of charge that acts as a glue (chemical coupling) at sufficiently large distances to avoid repulsive effects (dipole-dipole coupling and Pauli's repulsion between electron clouds).

One dimensional imager of neutrons on the Z machine.

We recently developed a one-dimensional imager of neutrons on the Z facility. The instrument is designed for Magnetized Liner Inertial Fusion (MagLIF) experiments, which produce D-D neutrons yields of ∼3 × 1012. X-ray imaging indicates that the MagLIF stagnation region is a 10-mm long, ∼100-µm diameter column. The small radial extents and present yields precluded useful radial resolution, so a one-dimensional imager was developed. The imaging component is a 100-mm thick tungsten slit; a rolled-edge slit limits variations in the acceptance angle along the source. CR39 was chosen as a detector due to its negligible sensitivity to the bright x-ray environment in Z. A layer of high density poly-ethylene is used to enhance the sensitivity of CR39. We present data from fielding the instrument on Z, demonstrating reliable imaging and track densities consistent with diagnosed yields. For yields ∼3 × 1012, we obtain resolutions of ∼500 µm.