Search NASA⌕ Search

SEARCH · Search NASA

Results for “trigger efficiency”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Matched Guiding and Controlled Injection in Dark-Current-Free, 10-GeV-Class, Channel-Guided Laser-Plasma Accelerators

Here we measure the high-intensity laser propagation throughout meter-scale, channel-guided laser-plasma accelerators by adjusting the length of the plasma channel on a shot-by-shot basis, showing high-quality guiding of 500 TW laser pulses over 30 cm in a hydrogen plasma of density n 0 ≈ 1 X 10 17 cm -3 . We observed transverse energy transport of higher-order modes in the first ≈ 12 cm of the plasma channel, followed by quasimatched propagation, and the gradual, dark-current-free depletion of laser energy to the wake. We quantify the laser-to-wake transfer efficiency limitations of currently available petawatt-class lasers and demonstrate via simulation how control over the laser mode can significantly improve beam parameters. Using 21.3 J of laser energy, and triggering localized electron injection, we observed electron bunches with single, quasimonoenergetic peaks up to 9.2 GeV with charge extending beyond 10 GeV.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Guanidinium-Assisted Surface Matrix Engineering for Highly Efficient Perovskite Quantum Dot Photovoltaics

Metal halide perovskite quantum dots (Pe-QDs) are of great interest in new-generation photovoltaics (PVs). However, it remains challenging in the construction of conductive and intact Pe-QD films to maximize their functionality. Herein, a ligand-assisted surface matrix strategy to engineer the surface and packing states of Pe-QD solids is demonstrated by a mild thermal annealing treatment after ligand exchange processing (referred to as “LE-TA”) triggered by guanidinium thiocyanate. The “LE-TA” method induces the formation of surface matrix on CsPbI 3 QDs, which is dominated by the cationic guanidinium (GA+) rather than the SCN-, maintaining the intact cubic structure and facilitating interparticle electrical interaction of QD solids. Consequently, the GA-matrix-confined CsPbI 3 QDs exhibit remarkably enhanced charge mobility and carrier diffusion length compared to control ones, leading to a champion power conversion efficiency of 15.21% when assembled in PVs, which is one of the highest among all Pe-QD solar cells. Additionally, the “LE-TA” method shows similar effects when applied to other Pe-QD PV systems like CsPbBr 3 and FAPbI 3 (FA = formamidinium), indicating its versatility in regulating the surfaces of various Pe-QDs. This work may afford new guidelines to construct electrically conductive and structurally intact Pe-QD solids for efficient optoelectronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Entanglement and many-body effects in collective neutrino oscillations

Collective neutrino oscillations play a crucial role in transporting lepton flavor in astrophysical settings, such as supernovae, where the neutrino density is large. In this regime, neutrino-neutrino interactions are important and simulations in the mean-field approximation show evidence for collective oscillations occurring at timescales much shorter than those associated with vacuum oscillations. In this work, we study the out-of-equilibrium dynamics of a corresponding spin model using matrix product states and show how collective bipolar oscillations can be triggered by many-body correlations if appropriate initial conditions are present. We find entanglement entropies scaling at most logarithmically in the system size suggesting that classical tensor network methods could be efficient in describing collective neutrino dynamics more generally. These observation provide a clear path forward, not only to increase the accuracy of current simulations, but also to elucidate the mechanism behind collective flavor oscillations without resorting to the mean-field approximation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
DOE OSTI2024

Heterogeneous Nucleating Agent for High‐Boiling‐Point Nonhalogenated Solvent‐Processed Organic Solar Cells and Modules

Abstract High‐boiling‐point nonhalogenated solvents are superior solvents to produce large‐area organic solar cells (OSCs) in industry because of their wide processing window and low toxicity; while, these solvents with slow evaporation kinetics will lead excessive aggregation of state‐of‐the‐art small molecule acceptors (e.g. L8‐BO), delivering serious efficiency losses. Here, a heterogeneous nucleating agent strategy is developed by grafting oligo (ethylene glycol) side‐chains on L8‐BO (BTO‐BO). The formation energy of the obtained BTO‐BO; while, changing from liquid in a solvent to a crystalline phase, is lower than that of L8‐BO irrespective of the solvent type. When BTO‐BO is added as the third component into the active layer (e.g. PM6:L8‐BO), it easily assembles to form numerous seed crystals, which serve as nucleation sites to trigger heterogeneous nucleation and increase nucleation density of L8‐BO through strong hydrogen bonding interactions even in high‐boiling‐point nonhalogenated solvents. Therefore, it can effectively suppress excessive aggregation during growth, achieving ideal phase‐separation active layer with small domain sizes and high crystallinity. The resultant toluene‐processed OSCs exhibit a record power conversion efficiency (PCE) of 19.42% (certificated 19.12%) with excellent operational stability. The strategy also has superior advantages in large‐scale devices, showing a 15.03‐cm 2 module with a record PCE of 16.35% (certificated 15.97%).

Chemistry↗
DOE OSTI2018

The non-linear onset of neutrino-driven convection in two- and three-dimensional core-collapse supernovae

Here, a toy model of the post-shock region of core-collapse supernovae is used to study the non-linear development of turbulent motions driven by convection in the presence of advection. Our numerical simulations indicate that buoyant perturbations of density are able to trigger self-sustained convection only when the instability is not linearly stabilized by advection. Large amplitude perturbations produced by strong shock oscillations or combustion inhomogeneities before the collapse of the progenitor are efficiently shredded through phase mixing and generate a turbulent cascade. Our model enables us to investigate several physical arguments that had been proposed to explain the impact of the dimensionality on the onset of explosions in global simulations of core-collapse supernovae. Three-dimensional (3D) simulations are found to lead to higher entropy values than two-dimensional (2D) ones. We attribute this to greater turbulent mixing and dissipation of the kinetic energy into heat in 3D. Finally, our results show that the increase of entropy is enhanced with finer numerical resolution and larger perturbation amplitude.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Thermally Activated Delayed Fluorescence (TADF) Path toward Efficient Electroluminescence in Purely Organic Materials: Molecular Level Insight

Since the seminal work of Tang and Vanslyke in 1987 on small-molecule emitters and that of Friend and co-workers in 1990 on conjugated-polymer emitters, organic light-emitting diodes (OLEDs) have attracted much attention from academia as well as industry, as the OLED market is estimated to reach the $30B mark by the end of 2018. In these first-generation organic emitters, on the basis of simple spin statistics, electrical excitation resulted in the formation of ~25% singlet excitons and ~75% triplet excitons. Radiative decay of the singlet excitons to the singlet ground state leads to a prompt fluorescence emission, while the triplet excitons only lead to weak phosphorescence due to the very small spin-orbit couplings present in purely organic molecules. The consequence is a ca. 75% energy loss, which triggered wide-ranging efforts to try and harvest as many of the triplet excitons as possible. In 1998, Thompson, Forrest, and their co-workers reported second-generation OLED emitters based on coordination complexes with heavy transition metals (e.g., iridium or platinum). Here, the triplet excitons stimulate efficient and fast phosphorescence due to the strong spin-orbit couplings enabled by the heavy-metal atoms. Internal quantum efficiencies (IQE) up to 100% have been reported, which means that for every electron injected into the device, a photon is emitted. Here, while these second-generation emitters are those mainly exploited in current OLED applications, there is strong impetus from both cost and environmental standpoints to find new ways of exploiting purely organic emitters, which in addition can offer greater flexibility to fine tune the electronic and optical properties by exploiting the synthetic organic chemistry toolbox.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Concept of Lattice Mismatch and Emergence of Surface States in Two-dimensional Hybrid Perovskite Quantum Wells

We present that surface states are ubiquitous to semiconductors and significantly impact the physical properties and, consequently, the performance of optoelectronic devices. Moreover, surface effects are strongly amplified in lower dimensional systems such as quantum wells and nanostructures. Layered halide perovskites (LHPs) are two-dimensional solution-processed natural quantum wells where optoelectronic properties can be tuned by varying the perovskite layer thickness n, i.e., the number of octahedra spanning the layer. They are efficient semiconductors with technologically relevant stability. Here, a generic elastic model and electronic structure modeling are applied to LHPs heterostructures with various layer thickness. We show that the relaxation of the interface strain is triggered by perovskite layers above a critical thickness. This leads to the release of the mechanical energy arising from the lattice mismatch, which nucleates the surface reorganization and may potentially induce the formation of previously observed lower energy edge states. Finally, these states, which are absent in three-dimensional perovskites are anticipated to play a crucial role in the design of LHPs for optoelectronic systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Baryogenesis from decaying magnetic helicity in axiogenesis

Generating axion dark matter through the kinetic misalignment mechanism implies the generation of large asymmetries for Standard Model fermions in the early universe. Even if these asymmetries are washed out at later times, they can trigger a chiral plasma instability in the early universe. Similarly, a direct coupling of the axion with the hypercharge gauge field can trigger a tachyonic instability. These instabilities produce helical magnetic fields, which are preserved until the electroweak phase transition. At the electroweak phase transition, these become a source of baryon asymmetry, which can be much more efficient than the original axiogenesis proposal. We discuss constraints on axion dark matter production from the overproduction of the baryon asymmetry as well as a minimal, albeit fine-tuned setup, where both the correct dark matter abundance and baryon asymmetry can be achieved. For a given axion decay constant, this leads to a sharp prediction for the mass of the radial direction of the Peccei Quinn field, which is a soft mass scale in supersymmetric theories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Giant Cloud Condensation Nuclei Facilitate Drizzle Formation in Stratocumulus—Insights From a Combined Observation‐Modeling Framework

The mechanism for initiating drizzle drop remains a gap in the current understanding of warm rain formation. One prevalent hypothesis suggests that the presence of Giant Cloud Condensation Nuclei (GCCN) generates drizzle‐sized drops necessary to trigger the Collision‐Coalescence (C‐C) process. Here, in this study, this hypothesis is investigated using a novel framework that integrates in situ observations, remote sensing measurements, and idealized models. Results show that GCCN can efficiently generate drizzle drops through condensation, producing a broad Droplet Size Distribution (DSD) comparable to in situ observations. The large drizzle drop and broad DSD strongly facilitate C‐C, further accelerating drizzle initiation. To compare with observation, the model‐generated DSDs are used to generate radar Doppler spectra where radar reflectivity and Doppler skewness is estimated. The simulated radar quantities correspond well with radar observations, providing critical evidence for the GCCN‐induced drizzle initiation mechanism.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Cosmic Muon Veto for the mini-ICAL detector at IICHEP, Madurai

A 51-kiloton magnetised Iron Calorimeter (ICAL) detector, using Resistive Plate Chambers (RPCs) as active detector elements, aims to study atmospheric neutrinos. A prototype - 1/600 of the weight of ICAL, called mini-ICAL was installed in the INO transit campus at Madurai. A modest proof-of-principle cosmic muon veto detector of about 1 m × 1 m × 0.3 m dimensions was setup a few years ago, using scintillator paddles. The measured cosmic muon veto efficiency of 99.98% and simulation studies of muon-induced background events in the ICAL detector surrounded by an efficient veto detector were promising. This led to the idea of constructing a bigger cosmic muon veto around the mini-ICAL detector. Details of the design and construction of the detector including the electronics, trigger and DAQ systems planned will be briefly presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗
DOE OSTI2020

The CLAS12 High Threshold Cherenkov Counter

The High Threshold Cherenkov Counter (HTCC) is one of the detector systems of the CLAS12 spectrometer, and is used to generate a fast trigger signal in electron scattering experiments in the polar angle range from 5°to 35°. The HTCC is installed in front of the drift chambers and introduces a minimal amount of additional material within the acceptance. The HTCC is one unit whose core component is a multifocal mirror that consists of 60 lightweight ellipsoidal mirrors. It is important that the HTCC provides efficient coverage of the CLAS12 forward acceptance with no gaps. In order to achieve this, each sector of the CLAS12 Forward Detector is covered by 2 identical half-sector mirrors that focus Cherenkov light on 8 phototubes. The HTCC has a total of 48 channels with Electron Tubes 9823QKB photomultipliers that have a 5-in quartz face plate to detect Cherenkov light. The system provides rejection of charged -mesons with momenta below 4.8 GeV for the reliable identification of scattered electrons. In this paper the details of the design, construction, calibration, and performance results of the HTCC are presented

Sharabian, Y. G.↗
DOE OSTI2023

Ultrafine-grained Fe-TiB 2 high-modulus nanocomposite steel with high strength and isotropic mechanical properties by laser powder bed fusion

Fe-TiB 2 metal matrix composite, also called high-modulus steels (HMSs), are of great interest for applications in fuel-efficient transportation infrastructure, aerospace, and wear industries due to their high specific stiffness and yield strength. However, conventional cast Fe-TiB 2 HMSs often contain coarse and sharp-edged TiB 2 particles which easily trigger premature cracking during loading. Here, we synthesized a Fe-TiB 2 nanocomposite HMS via laser powder bed fusion (LPBF) additive manufacturing of mixed micro-sized powders of Fe, Ti, and Fe 2 B. We investigated the microstructure formation and mechanical behavior of the Fe-TiB 2 HMS. We found that in situ chemical reaction of Ti and Fe 2 B enables the formation of TiB 2 particles at nanoscale during rapid solidification of LPBF. These nanoscale TiB 2 particles can serve as heterogeneous nucleation sites and promote the formation of ultrafine and equiaxed α-Fe grains with random crystallographic textures, which differ from many other additively manufactured (AM) metal alloys characteristic of strong crystallographic textures. As such, isotropic mechanical properties were achieved in the AM Fe-TiB 2 nanocomposite HMS with a high elastic modulus of ~ 240 GPa, an exceptional yield strength of ~ 1450 MPa, and a large plasticity of ~ 20% under compression. Quantitative analysis reveals that the high yield strength primarily originates from strengthening contributions of the ultrafine grains with an average grain size of ~450 nm, the nanoscale TiB 2 reinforcing particles of 20–180 nm, and a high density of printing-induced dislocations of the order of 10 15 m –2 . In situ synchrotron high-energy X-ray diffraction unveils the load partitioning from the softer α-Fe matrix to the stiffer and stronger TiB 2 nanoparticles, contributing to the sustained strain hardening during compression. Our work not only provides a general pathway for achieving high-performance metal matrix nanocomposites by in situ chemical reaction and precipitation of ceramic nanoparticles during additive manufacturing, but also offers mechanistic insights into the deformation mechanism of nanoparticle-reinforced HMS composites.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Linking plant functional trait plasticity and the large increase in forest water use efficiency

Elevated atmospheric CO 2 concentrations are anticipated to enhance photosynthesis and reduce stomatal conductance, thus increasing plant water use efficiency. A recent study based on eddy covariance flux observations from Northern Hemisphere forests showed a large increase in inherent water use efficiency (IWUE). Here we used an updated version of the same data set and robust uncertainty quantification to revisit these contemporary IWUE trends. We tested the hypothesis that the observed IWUE increase could be attributed to interannual trends in plant functional traits, potentially triggered by environmental change. We postulate that IWUE increased by ~1.3% yr -1 , which is less than previously reported but still larger than theoretical expectations. Numerical simulations with the Tethys-Chloris ecosystem model using temporally static plant functional traits cannot explain this increase. Simulations with plant functional trait plasticity, i.e., temporal changes in model parameters such as specific leaf area and maximum Rubisco capacity, match the observed trends in IWUE. Our results show that trends in plant functional traits, equal to 1.0% yr -1 , can explain the observed IWUE trends. Thus, at decadal or longer time scales, trait plasticity could potentially influence forest water, carbon, and energy fluxes with profound implications for both the monitoring of temporal changes in plant functional traits and their representation in Earth system models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

GRB 221009A: The B.O.A.T. Burst that Shines in Gamma Rays

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest gamma-ray burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded, by 1 s, the low-energy (<10 MeV) pulse that triggered the Fermi Gamma-Ray Burst Monitor (GBM), as has been observed in other GRBs. The prompt phase of GRB 221009A lasted a few hundred seconds. It was so bright that we identify a bad time interval of 64 s caused by the extremely high flux of hard X-rays and soft gamma rays, during which the event reconstruction efficiency was poor and the dead time fraction quite high. The late-time emission decayed as a power law, but the extrapolation of the late-time emission during the first 450 s suggests that the afterglow started during the prompt emission. We also found that high-energy events observed by the LAT are incompatible with synchrotron origin, and, during the prompt emission, are more likely related to an extra component identified as synchrotron self-Compton (SSC). A remarkable 400 GeV photon, detected by the LAT 33 ks after the GBM trigger and directionally consistent with the location of GRB 221009A, is hard to explain as a product of SSC or TeV electromagnetic cascades, and the process responsible for its origin is uncertain. Because of its proximity and energetic nature, GRB 221009A is an extremely rare event.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Electrostatic Influence on Photoisomerization in Bacteriorhodopsin and Halorhodopsin

Bacteriorhodopsin (bR) and halorhodopsin (hR) are mutual membrane proteins that transport ions across the cell membrane in halobacteria. Their ion transport function is triggered by photoactivated isomerization of the retinal protonated Schiff base (RPSB) chromophore. In spite of their similar structures, bR and hR exhibit widely differing RPSB isomerization rates and quantum yields (with bR being both faster and more efficient than hR). Previous simulations of photoisomerization in bR and hR using ab initio multiple spawning (AIMS) with QM/MM have successfully reproduced the experimentally observed ordering of quantum yields and isomerization rates, but the origin of these differences remains elusive. In this work, we discover the role of electrostatic interactions in the protein pocket surrounding RPSB. We probe the influence of protein electrostatics by modifying the charge of the complex counterion in bR/hR to be more/less negative than the native state. We find that such modifications lead to bR-like behavior in hR and vice versa. This demonstrates the crucial role of electrostatic interactions in controlling the outcome of RPSB photoisomerization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Coordination Chemistry as a Universal Strategy for a Controlled Perovskite Crystallization

The most efficient and stable perovskite solar cells (PSCs) are made from complex mixture of precursors, which are practically always dissolved in combinations of the volatile (and toxic) N, N-Dimethylformamide (DMF), and the non-volatile (and green) dimethyl sulfoxide (DMSO) solvents. Typically, to then form a thin film, an extreme oversaturation of the perovskite precursor is initiated to trigger nucleation sites, e.g. by vacuum, an airstream or a so-called antisolvent. Unfortunately, most such oversaturation triggers do not expel the lingering (and highly coordinating) DMSO, which is highly coordinating, from the thin films; this detrimentally affects long-term stability. Here, for the first time, we introduce (the green) dimethyl sulfide (DMS) as a novel nucleation trigger for perovskite films combining, uniquely, high coordination and high vapor pressure. More precisely, DMS coordinates more strongly than all currently used solvents, hence replacing them, including DMSO, effectively during film formation. Crucially, DMS also has among the highest vapor pressures reported in literature, thus effectively leaving the thin film shortly after formation. This gives DMS a universal scope: DMS replaces other solvents by coordinating more strongly and removes itself once the film formation is finished. To demonstrate this novel coordination chemistry approach, we process MAPbI 3 PSCs, typically dissolved in hard-to-remove (and green) DMSO achieving 21.6% efficiency, among the highest reported efficiencies for this system. To confirm the universality of our strategy, we tested DMS for FAPbI 3 as another composition, which showed higher efficiency of 23.5% compared to 20.9% for fabricated device with CB. Finally, this work provides a universal strategy to control perovskite crystallization using coordination chemistry heralding the revival of perovskite compositions with pure DMSO, such as MAPbI 3 .

36 MATERIALS SCIENCE↗