Growing three-dimensional objects with light.

Vat photopolymerization (VP) additive manufacturing enables fabrication of complex 3D objects by using light to selectively cure a liquid resin. Developed in the 1980s, this technique initially had few practical applications due to limitations in print speed and final part material properties. In the four decades since the inception of VP, the field has matured substantially due to simultaneous advances in light delivery, interface design, and materials chemistry. Today, VP materials are used in a variety of practical applications and are produced at industrial scale. In this perspective, we trace the developments that enabled this printing revolution by focusing on the enabling themes of light, interfaces, and materials. We focus on these fundamentals as they relate to continuous liquid interface production (CLIP), but provide context for the broader VP field. We identify the fundamental physics of the printing process and the key breakthroughs that have enabled faster and higher-resolution printing, as well as production of better materials. We show examples of how in situ print process monitoring methods such as optical coherence tomography can drastically improve our understanding of the print process. Finally, we highlight areas of recent development such as multimaterial printing and inorganic material printing that represent the next frontiers in VP methods.

A quantitative thermodynamic metric for identifying deep eutectic solvents.

Deep eutectic solvents (DESs) are a rapidly expanding class of liquid phase mixtures that offer many useful features. However, there currently exists no widely accepted criterion to identify whether or not a particular mixture is, in fact, a DES. This study introduces a quantitative metric based on the molar excess Gibbs energy of a eutectic mixture and proposes a threshold value in order to classify a eutectic system as a DES.

An interdisciplinary computational model for predicting traumatic brain injury: Linking biomechanics and functional neural networks.

The brain is a complex network consisting of neuron cell bodies in the gray matter and their axonal projections, forming the white matter tracts. These neurons are supported by an equally complex vascular network as well as glial cells. Traumatic brain injury (TBI) can lead to the disruption of the structural and functional brain networks due to disruption of both neuronal cell bodies in the gray matter as well as their projections and supporting cells. To explore how an impact can alter the function of brain networks, we integrated a finite element (FE) brain mechanics model with linked models of brain dynamics (Kuramoto oscillator) and vascular perfusion (Balloon-Windkessel) in this study. We used empirical resting-state functional magnetic resonance imaging (MRI) data to optimize the fit of our brain dynamics and perfusion models to clinical data. Results from the FE model were used to mimic injury in these optimized brain dynamics models: injury to the nodes (gray matter) led to a decrease in the nodal oscillation frequency, while damage to the edges (axonal connections/white matter) progressively decreased coupling among connected nodes. A total of 53 cases, including 33 non-injurious and 20 concussive head impacts experienced by professional American football players were simulated using this integrated model. We examined the correlation of injury outcomes with global measures of structural connectivity, neural dynamics, and functional connectivity of the brain networks when using different lesion methods. Results show that injurious head impacts cause significant alterations in global network topology regardless of lesion methods. Changes between the disrupted and healthy functional connectivity (measured by Pearson correlation) consistently correlated well with injury outcomes (AUC≥0.75), although the predictive performance is not significantly different (p>0.05) to that of traditional kinematic measures (angular acceleration). Intriguingly, our lesion model for gray matter damage predicted increases in global efficiency and clustering coefficient with increases in injury risk, while disrupting axonal connections led to lower network efficiency and clustering. When both injury mechanisms were combined into a single injury prediction model, the injury prediction performance depended on the thresholds used to determine neurodegeneration and mechanical tolerance for axonal injury. Together, these results point towards complex effects of mechanical trauma to the brain and provide a new framework for understanding brain injury at a causal mechanistic level and developing more effective diagnostic methods and therapeutic interventions.

Generating Human Arm Kinematics Using Reinforcement Learning to Train Active Muscle Behavior in Automotive Research.

Computational human body models (HBMs) are important tools for predicting human biomechanical responses under automotive crash environments. In many scenarios, the prediction of the occupant response will be improved by incorporating active muscle control into the HBMs to generate biofidelic kinematics during different vehicle maneuvers. In this study, we have proposed an approach to develop an active muscle controller based on reinforcement learning (RL). The RL muscle activation control (RL-MAC) approach is a shift from using traditional closed-loop feedback controllers, which can mimic accurate active muscle behavior under a limited range of loading conditions for which the controller has been tuned. Conversely, the RL-MAC uses an iterative training approach to generate active muscle forces for desired joint motion and is analogous to how a child develops gross motor skills. In this study, the ability of a deep deterministic policy gradient (DDPG) RL controller to generate accurate human kinematics is demonstrated using a multibody model of the human arm. The arm model was trained to perform goal-directed elbow rotation by activating the responsible muscles and investigated using two recruitment schemes: as independent muscles or as antagonistic muscle groups. Simulations with the trained controller show that the arm can move to the target position in the presence or absence of externally applied loads. The RL-MAC trained under constant external loads was able to maintain the desired elbow joint angle under a simplified automotive impact scenario, implying the robustness of the motor control approach.

Integrating Human and Nonhuman Primate Data to Estimate Human Tolerances for Traumatic Brain Injury.

Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, subinjurious human volunteer data, and nonhuman primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element (FE) brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., diffuse axonal multi-axial general evaluation (DAMAGE)) showed better power of predicting tissue-level deformation in a variety of impact conditions and were subsequently used to characterize injury tolerance. The proposed human brain tolerances for mild and severe TBI were estimated and presented in the form of injury risk curves based on selected tissue-level and kinematics-based injury metrics. The application of the estimated injury tolerances was finally demonstrated using real-world automotive crash data.

A molecular dynamics study of a fully zwitterionic copolymer/ionic liquid-based electrolyte: Li+ transport mechanisms and ionic interactions.

The development of polymer electrolytes (PEs) is crucial for advancing safe, high-energy density batteries, such as lithium-metal and other beyond lithium-ion chemistries. However, reaching the optimum balance between mechanical stiffness and ionic conductivity is not a straightforward task. Zwitterionic (ZI) gel electrolytes comprising lithium salt and ionic liquid (IL) solutions within a fully ZI polymer network can, in this context, provide useful properties. Although such materials have shown compatibility with lithium metal in batteries, several fundamental structure-dynamic relationships regarding ionic transport and the Li+ coordination environment remain unclear. To better resolve such issues, molecular dynamics simulations were carried out for two IL-based electrolyte systems, N-butyl-N-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide ([BMP][TFSI]) with 1 M LiTFSI salt and a ZI gel electrolyte containing the IL and a ZI copolymer: poly(2-methacryloyloxyethyl phosphorylcholine-co-sulfobetaine vinylimidazole), poly(MPC-co-SBVI). The addition of ZI polymer decreases the [TFSI]- -[Li]+ interactions and increases the IL ion diffusivities, and consequently, the overall ZI gel ionic conductivity. The structural analyses showed a large preference for lithium-ion interactions with the polymer phosphonate groups, while the [TFSI]- anions interact directly with the sulfonate group and the [BMP]+ cations only display secondary interactions with the polymer. In contrast to previous experimental data on the same system, the simulated transference numbers showed smaller [Li]+ contributions to the overall ionic conductivities, mainly due to negatively charged lithium aggregates and the strong lithium-ion interactions in the systems.

Test-retest reliability and long-term stability of three-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data.

PURPOSE: Several recent studies have used a three-tissue constrained spherical deconvolution pipeline to obtain quantitative metrics of brain tissue microstructure from diffusion-weighted MRI data. The three tissue compartments, consisting of white matter, gray matter, and CSF-like (free water) signals, are potentially useful in the evaluation of brain microstructure in a range of pathologies. However, the reliability and long-term stability of these metrics have not yet been evaluated. METHODS: This study examined estimates of whole-brain microstructure for the three tissue compartments, in three separate test-retest cohorts. Each cohort had different lengths of time between baseline and retest, ranging from within the same scanning session in the shortest interval to 3 months in the longest interval. Each cohort was also collected with different acquisition parameters. RESULTS: The CSF-like compartment displayed the greatest reliability across all cohorts, with intraclass correlation coefficient (ICC) values being above 0.95 in each cohort. White matter-like and gray matter-like compartments both demonstrated very high reliability in the immediate cohort (both ICC > 0.90); however, this declined in the 3-month interval cohort to both compartments having ICC > 0.80. Regional CSF-like signal fraction was examined in bilateral hippocampus and had an ICC > 0.80 in each cohort. CONCLUSION: The three-tissue constrained spherical deconvolution techniques provide reliable and stable estimates of tissue-microstructure composition, up to 3 months longitudinally in a control population. This forms an important basis for further investigations using three-tissue constrained spherical deconvolution techniques to track changes in microstructure across a variety of brain pathologies.

A Multibody Model for Predicting Spatial Distribution of Human Brain Deformation Following Impact Loading.

With an increasing focus on long-term consequences of concussive brain injuries, there is a new emphasis on developing tools that can accurately predict the mechanical response of the brain to impact loading. Although finite element models (FEM) estimate the brain response under dynamic loading, these models are not capable of delivering rapid (∼seconds) estimates of the brain's mechanical response. In this study, we develop a multibody spring-mass-damper model that estimates the regional motion of the brain to rotational accelerations delivered either about one anatomic axis or across three orthogonal axes simultaneously. In total, we estimated the deformation across 120 locations within a 50th percentile human brain. We found the multibody model (MBM) correlated, but did not precisely predict, the computed finite element response (average relative error: 18.4 ± 13.1%). We used machine learning (ML) to combine the prediction from the MBM and the loading kinematics (peak rotational acceleration, peak rotational velocity) and significantly reduced the discrepancy between the MBM and FEM (average relative error: 9.8 ± 7.7%). Using an independent sports injury testing set, we found the hybrid ML model also correlated well with predictions from a FEM (average relative error: 16.4 ± 10.2%). Finally, we used this hybrid MBM-ML approach to predict strains appearing in different locations throughout the brain, with average relative error estimates ranging from 8.6% to 25.2% for complex, multi-axial acceleration loading. Together, these results show a rapid and reasonably accurate method for predicting the mechanical response of the brain for single and multiplanar inputs, and provide a new tool for quickly assessing the consequences of impact loading throughout the brain.

Development of a Single-Degree-of-Freedom Mechanical Model for Predicting Strain-Based Brain Injury Responses.

Linking head kinematics to injury risk has been the focus of numerous brain injury criteria. Although many early forms were developed using mechanics principles, recent criteria have been developed using empirical methods based on subsets of head impact data. In this study, a single-degree-of-freedom (sDOF) mechanical analog was developed to parametrically investigate the link between rotational head kinematics and brain deformation. Model efficacy was assessed by comparing the maximum magnitude of displacement to strain-based brain injury predictors from finite element (FE) human head models. A series of idealized rotational pulses covering a broad range of acceleration and velocity magnitudes (0.1-15 krad/s2 and 1-100 rad/s) with durations between 1 and 3000 ms were applied to the mechanical models about each axis of the head. Results show that brain deformation magnitude is governed by three categories of rotational head motion each distinguished by the duration of the pulse relative to the brain's natural period: for short-duration pulses, maximum brain deformation depended primarily on angular velocity magnitude; for long-duration pulses, brain deformation depended primarily on angular acceleration magnitude; and for pulses relatively close to the natural period, brain deformation depended on both velocity and acceleration magnitudes. These results suggest that brain deformation mechanics can be adequately explained by simple mechanical systems, since FE model responses and experimental brain injury tolerances exhibited similar patterns to the sDOF model. Finally, the sDOF model was the best correlate to strain-based responses and highlighted fundamental limitations with existing rotational-based brain injury metrics.

Etching of electrodeposited Cu2O films using ammonia solution for photovoltaic applications.

Impurities at the surface of electrodeposited p-Cu2O films have been efficiently removed through the use of concentrated aqueous ammonia solution as a wet etching agent. The performance of the Cu2O homojunction photovoltaic devices incorporating etched p-Cu2O as the bottom layer is higher compared to devices with as-deposited p-Cu2O layers due to an improvement of the homojunction interface quality. Reducing the density of defect states that act as carrier recombination centers led to larger open circuit voltages. The ammonia etchant has been found to preferentially interact with the {100} facets of Cu2O and expose a greater number of {111} facets, resulting in increased interface area for etched homojunction devices, which also improved short circuit current density values.

Zipping, entanglement, and the elastic modulus of aligned single-walled carbon nanotube films.

Reliably routing heat to and from conversion materials is a daunting challenge for a variety of innovative energy technologies--from thermal solar to automotive waste heat recovery systems--whose efficiencies degrade due to massive thermomechanical stresses at interfaces. This problem may soon be addressed by adhesives based on vertically aligned carbon nanotubes, which promise the revolutionary combination of high through-plane thermal conductivity and vanishing in-plane mechanical stiffness. Here, we report the data for the in-plane modulus of aligned single-walled carbon nanotube films using a microfabricated resonator method. Molecular simulations and electron microscopy identify the nanoscale mechanisms responsible for this property. The zipping and unzipping of adjacent nanotubes and the degree of alignment and entanglement are shown to govern the spatially varying local modulus, thereby providing the route to engineered materials with outstanding combinations of mechanical and thermal properties.

Significant head accelerations can influence immediate neurological impairments in a murine model of blast-induced traumatic brain injury.

Although blast-induced traumatic brain injury (bTBI) is well recognized for its significance in the military population, the unique mechanisms of primary bTBI remain undefined. Animate models of primary bTBI are critical for determining these potentially unique mechanisms, but the biomechanical characteristics of many bTBI models are poorly understood. In this study, we examine some common shock tube configurations used to study blast-induced brain injury in the laboratory and define the optimal configuration to minimize the effect of torso overpressure and blast-induced head accelerations. Pressure transducers indicated that a customized animal holder successfully reduced peak torso overpressures to safe levels across all tested configurations. However, high speed video imaging acquired during the blast showed significant head accelerations occurred when animals were oriented perpendicular to the shock tube axis. These findings of complex head motions during blast are similar to previous reports [Goldstein et al., 2012, "Chronic Traumatic Encephalopathy in Blast-Exposed Military Veterans and a Blast Neurotrauma Mouse Model," Sci. Transl. Med., 4(134), 134ra160; Sundaramurthy et al., 2012, "Blast-Induced Biomechanical Loading of the Rat: An Experimental and Anatomically Accurate Computational Blast Injury Model," J. Neurotrauma, 29(13), pp. 2352-2364; Svetlov et al., 2010, "Morphologic and Biochemical Characterization of Brain Injury in a Model of Controlled Blast Overpressure Exposure," J. Trauma, 69(4), pp. 795-804]. Under the same blast input conditions, minimizing head acceleration led to a corresponding elimination of righting time deficits. However, we could still achieve righting time deficits under minimal acceleration conditions by significantly increasing the peak blast overpressure. Together, these data show the importance of characterizing the effect of blast overpressure on head kinematics, with the goal of producing models focused on understanding the effects of blast overpressure on the brain without the complicating factor of superimposed head accelerations.

Poly(dimethylsiloxane)-supported ionogels with a high ionic liquid loading.

The immiscibility of poly(dimethylsiloxane) (PDMS) and ionic liquids (ILs) was overcome to create PDMS-supported IL gels (ionogels) with IL loadings of up to 80% by mass through a simple sol-gel reaction at room temperature. By stirring a mixture of a functionalized PDMS oligomer, formic acid, and an IL (or lithium-in-IL solution), a resin was formed that could be cast to create a freestanding, flexible ionogel. PDMS-supported ionogels exhibited favorable ionic conductivity (ca. 3 mS cm(-1)) and excellent mechanical behavior (elastic modulus: ca. 60 kPa; fatigue life: >5000 cycles; mechanically stable at temperatures up to 200 °C). The activation energy of ionic conductivity was shown to be nearly identical for the ionogel and the neat IL, in contrast to ionogel systems wherein the scaffold material is miscible with the IL. This similarity indicates that IL/scaffold chemical interactions are key to the understanding of ionogel electrical performance, especially at elevated temperatures.

Zwitterionic Polymer Ionogel Electrolytes Supported by Coulombic Cross-Links: Impacts of Alkali Metal Cation Identity.

Zwitterionic (ZI) polymers enable the formation of noncovalent cross-links within ionic liquid electrolytes (ILEs) to create nonflammable, mechanically robust, and highly conductive ionogel electrolytes. In this study, ZI homopolymer poly(2-methacryloyloxyethyl phosphorylcholine) [poly(MPC)] scaffolds are synthesized in situ within lithium and/or sodium salt-based ILEs to construct a series of ionogels that contain between 3 and 15 wt % poly(MPC). Room-temperature ionic conductivity values of these ionogels are found to vary between approximately 1.3 and 2.2 mS cm-1. For sodium only and 1:1 lithium/sodium equimolar mixed salt ionogels containing 6 wt % poly(MPC), the ionic conductivity is found to improve by 14% compared to the neat ILE due to the presence of the ZI scaffold. Moreover, comparing the elastic modulus values of lithium- versus sodium-containing ionogels revealed a difference of up to 1 order of magnitude [10.6 vs 111 kPa, respectively, for 3 wt % poly(MPC)]. Molecular dynamics simulations of ionogel precursor solutions corroborate the experimental results by demonstrating differences in the lithium/ZI monomer and sodium/ZI monomer cluster size distributions formed, which is hypothesized to influence the scaffold network cross-link density obtained upon photopolymerization. This work provides insights into why ZI polymer-supported ionogel properties that are relevant for the development of safer electrolytes for lithium-ion and sodium-ion batteries depend upon the chemical identity of the alkali metal cation.

Development of a biofidelic computational model of human pelvis for predicting biomechanical responses and pelvic fractures.

BACKGROUND AND OBJECTIVE: The pelvis, a crucial structure for human locomotion, is susceptible to injuries resulting in significant morbidity and disability. This study aims to introduce and validate a biofidelic computational pelvis model, enhancing our understanding of pelvis injury mechanisms under lateral loading conditions. METHODS: The Finite Element (FE) pelvic model, representing a mid-sized male, was developed with variable cortical thickness in pelvis bones. Material properties were determined through a synthesis of existing constitutive models, parametric studies, and multiple validations. Comprehensive validation included various tests, such as load-displacement assessments of sacroiliac joints, quasi-static and dynamic lateral compression on the acetabulum, dynamic side impacts on the acetabulum and iliac wing using defleshed pelvis, and lateral impacts by a rigid plate on the full body's pelvis region. RESULTS: Simulation results demonstrated a reasonable correlation between the pelvis model's overall response and cadaveric testing data. Predicted fracture patterns of the isolated pelvis exhibited fair agreement with experimental results. CONCLUSIONS: This study introduces a credible computational model, providing valuable biomechanical insights into the pelvis' response under diverse lateral loading conditions and fracture patterns. The work establishes a robust framework for developing and enhancing the biofidelity of pelvis FE models through a multi-level validation approach, stimulating further research in modeling, validation, and experimental studies related to pelvic injuries. The findings are expected to offer critical perspectives for predicting, preventing, and mitigating pelvic injuries from vehicular accidents, contributing to advancements in clinical research on medical treatments for pelvic fractures.

Zwitterionic Materials for Enhanced Battery Electrolytes.

Zwitterions (ZIs), which are molecules bearing an equal number of positive and negative charges and typically possessing large dipole moments, can play an important role in improving the characteristics of a wide variety of novel battery electrolytes. Significant Coulombic interactions among ZI charged groups and any mobile ions present can lead to several beneficial phenomena within electrolytes, such as increased salt dissociation, the formation of ordered pathways for ion transport, and enhanced mechanical robustness. In some cases, ZI additives can also boost electrochemical stability at the electrolyte/electrode interface and enable longer battery cycling. Here, a brief summary of selected key historical and recent advances in the use of ZI materials to enrich the performance of three distinct classes of battery electrolytes is presented. These include: ionic liquid-based, conventional solvent-based, and solid matrix-based (non-ceramic) electrolytes. Exploring a greater chemical diversity of ZI types and electrolyte pairings will likely lead to more discoveries that can empower next-generation battery designs in the years to come.

Amphiphilic Polyelectrolyte Complexes for Fouling-Resistant and Easily Tunable Membranes.

Commercial membranes today are manufactured from a handful of membrane materials. While these systems are well-optimized, their capabilities remain constrained by limited chemistries and manufacturing methods available. As a result, membranes cannot address many relevant separations where precise selectivity is needed, especially with complex feeds. This constraint requires the development of novel membrane materials that offer customizable features to provide specific selectivity and durability requirements for each application, enabled by incorporating different functional chemistries into confined nanopores in a scalable process. This study introduces a new class of membrane materials, amphiphilic polyelectrolyte complexes (APECs), comprised of a blend two distinct amphiphilic polyelectrolytes of opposite charge that self-assemble to form a polymer selective layer. When coated on a porous support from a mixture in a nonaqueous solvent, APECs self-assemble to create ionic nanodomains acting as water-conducting nanochannels, enveloped within hydrophobic nanodomains, ensuring structural integrity of the layer in water. Notably, this approach allows precise control over selectivity without compromising pore size, permeability, or fouling resistance. For example, using only one pair of amphiphilic copolymers, sodium sulfate rejections can be varied from >95% to <10% with no change in effective pore size and fouling resistance. Given the wide range of amphiphilic polyelectrolytes (i.e., combinations of different hydrophobic, anionic, and cationic monomers), APECs can create membranes with many diverse chemistries and selectivities. Resultant membranes can potentially address precision separations in many applications, from wastewater treatment to chemical and biological manufacturing.

Cervical vertebral and spinal cord injuries in rollover occupants.

BACKGROUND: Rollover crashes continue to be a substantial public health issue in North America. Previous research has shown that the cervical spine is the most injured spine segment in rollovers, but much of the past research has focused on risk factors rather than the actual cervical spine injuries. We sought to examine how different types of cervical spine injuries (vertebral and/or cord injury) vary with different occupant-related factors in rollovers and to compare these with non-rollovers. METHODS: We obtained crash and injury information from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) for 2005-2015 and Crash Investigation Sampling System (CISS) for 2017-2022. Based on weighted data, we calculated relative risks to assess how occupant sex, seat belt use, ejection status, and fatal outcome relate to the rate of different cervical spine injuries in rollovers and non-rollovers. RESULTS: In NASS-CDS occupants with cervical spine injuries (N = 111,040 weighted cases), about 91.5% experienced at least one vertebral injury whereas only 11.3% experienced a spinal cord injury (most of which had a concomitant vertebral fracture). All types of cervical spine injuries we examined were 3.4-5.2 times more likely to occur in rollovers compared to non-rollovers. These relative risks were similar for both sexes, belted and unbelted, non-ejected, and non-fatal occupants. The number of weighted CISS occupants with cervical spine injuries (N = 42,003) was smaller than in the NASS analysis, but cervical spine injuries remained 6.25 to 6.36 times more likely in rollovers compared to non-rollovers despite a more modern vehicle fleet. CONCLUSIONS: These findings underscore the continued need for rollover-specific safety countermeasures, especially those focused on cervical spine injury prevention, and elucidate the frequency, severity and other characteristics of the specific vertebral and spinal cord injuries being sustained in rollovers. Our findings suggest that countermeasures focused on preventing cervical vertebral fractures will also effectively prevent most cervical spinal cord injuries.

Results of an interlaboratory study on the working curve in vat photopolymerization.

The working curve informs resin properties and print parameters for stereolithography, digital light processing, and other photopolymer additive manufacturing (PAM) technologies. First demonstrated in 1992, the working curve measurement of cure depth vs radiant exposure of light is now a foundational measurement in the field of PAM. Despite its widespread use in industry and academia, there is no formal method or procedure for performing the working curve measurement, raising questions about the utility of reported working curve parameters. Here, an interlaboratory study (ILS) is described in which 24 individual laboratories performed a working curve measurement on an aliquot from a single batch of PAM resin. The ILS reveals that there is enormous scatter in the working curve data and the key fit parameters derived from it. The measured depth of light penetration Dp varied by as much as 7x between participants, while the critical radiant exposure for gelation Ec varied by as much as 70x. This significant scatter is attributed to a lack of common procedure, variation in light engines, epistemic uncertainties from the Jacobs equation, and the use of measurement tools with insufficient precision. The ILS findings highlight an urgent need for procedural standardization and better hardware characterization in this rapidly growing field.