Career Award Research Articles

On-Demand Delivery Platforms and Restaurant Sales

Restaurants are increasingly relying on on-demand delivery platforms (e.g., DoorDash, Grubhub, and Uber Eats) to reach customers and fulfill takeout orders. Although on-demand delivery is a valuable option for consumers, whether restaurants benefit from or are being hurt by partnering with these platforms remains unclear. This paper investigates whether and to what extent the platform delivery channel substitutes restaurants’ own takeout/dine-in channels and the net impact on restaurant revenue. Empirical analyses show that restaurants overall benefit from on-demand delivery platforms—these platforms increase restaurants’ total takeout sales while creating positive spillovers to customer dine-in visits. However, the platform effects are substantially heterogeneous, depending on the type of restaurants (independent versus chain) and the type of customer channels (takeout versus dine-in). The overall positive effect on fast-food chains is four times as large as that on independent restaurants. For takeout, delivery platforms substitute independent restaurants’ but complement chain restaurants’ own takeout sales. For dine-in, delivery platforms increase both independent and chain restaurants’ dine-in visits by a similar magnitude. Therefore, the value of delivery platforms to independent restaurants mostly comes from the increase in dine-in visits, whereas the value to chain restaurants primarily comes from the gain in takeout sales. Further, the platform delivery channel facilitates price competition and reduces the opportunity for independent restaurants to differentiate with premium services and dine-in experience, which may explain why independent restaurants do not benefit as much from on-demand delivery platforms. This paper was accepted by D. J. Wu, information systems. Funding: Z. Li is grateful to the National Science Foundation Division of Social and Economic Sciences for support provided through the CAREER award [Grant 2243736]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01010 .

Towards normalizing good practice across the whole modeling cycle: its instrumentation and future research topics

Choices made in modeling matter and demand more explication since they determine how much we can trust modeling insights and predictions within their social, political and ethical contexts. Good Modeling Practice (GMP) is a key research area for strengthening and maturing the modeling field and community, through identifying, formulating and sharing knowledge about the craft of modeling. This craft represents the knowledge that modelers learn in practice about how they get things done, and how they adapt their practices to new situations. This Joint Special Issue is motivated by the importance of sharing good modeling practices from a whole modeling lifecycle viewpoint. We attempt to add conceptual clarity to this research area by defining the plethora of concepts and decision points used to characterize the choices to be made throughout the modeling process, and by synthesizing some of the existing efforts on GMP. We characterize a broad list of articles in the literature on GMP and identify a list of essential topics demanding more attention. This list is only a preliminary one as we anticipate that a more comprehensive list of knowledge gaps will be unearthed from the submissions to the Joint Special Issue collection on GMP, of which this is an introduction. We also propose a vision for GMP and suggest instrumental ways that good practice can become not just well-known but normal practice. This instrumentation focuses on journal standards, collective commitment and culture especially by research community societies, early career awards for advancing GMP, and legal requirements or accreditation. A vital instrument in all this is the design and development of a modeling curriculum that distills core requisite knowledge about modeling, as well as proven-to-work routines and practices that can be scaled up in different contexts.

Dr. Casey Fiesler: Making tech ethics go viral

MIT Science Policy Review spoke with Dr. Casey Fiesler about her work to bring technology ethics to the general public through social media. Dr. Fiesler is an Associate Professor of Information Science (and Computer Science by courtesy) at the University of Colorado Boulder, specializing in the areas of technology ethics, internet law and policy, and online communities. Dr. Fiesler’s work on research ethics for data science, ethics education in computing, and broadening participation in computing has been supported by the National Science Foundation (NSF), and she is the recipient of an NSF CAREER Award. As a public scholar, she frequently speaks on topics of technology ethics and policy, and her research has been covered in a wide variety of media outlets, from The New York Times to Teen Vogue (though she’s particularly proud of her TikToks). Dr. Fiesler holds a PhD in Human-Centered Computing and a JD from Vanderbilt Law School. We had a conversation with Dr. Fiesler about her widely followed social media channels, where she addresses technology ethics with the general public. You can follow her on Instagram and TikTok @professorcasey.

Serum proteomic profiling of physical activity reveals CD300LG as a novel exerkine with a potential causal link to glucose homeostasis

Background:Physical activity has been associated with preventing the development of type 2 diabetes and atherosclerotic cardiovascular disease. However, our understanding of the precise molecular mechanisms underlying these effects remains incomplete and good biomarkers to objectively assess physical activity are lacking.Methods:We analyzed 3072 serum proteins in 26 men, normal weight or overweight, undergoing 12 weeks of a combined strength and endurance exercise intervention. We estimated insulin sensitivity with hyperinsulinemic euglycemic clamp, maximum oxygen uptake, muscle strength, and used MRI/MRS to evaluate body composition and organ fat depots. Muscle and subcutaneous adipose tissue biopsies were used for mRNA sequencing. Additional association analyses were performed in samples from up to 47,747 individuals in the UK Biobank, as well as using two-sample Mendelian randomization and mice models.Results:Following 12 weeks of exercise intervention, we observed significant changes in 283 serum proteins. Notably, 66 of these proteins were elevated in overweight men and positively associated with liver fat before the exercise regimen, but were normalized after exercise. Furthermore, for 19.7 and 12.1% of the exercise-responsive proteins, corresponding changes in mRNA expression levels in muscle and fat, respectively, were shown. The protein CD300LG displayed consistent alterations in blood, muscle, and fat. Serum CD300LG exhibited positive associations with insulin sensitivity, and to angiogenesis-related gene expression in both muscle and fat. Furthermore, serum CD300LG was positively associated with physical activity and negatively associated with glucose levels in the UK Biobank. In this sample, the association between serum CD300LG and physical activity was significantly stronger in men than in women. Mendelian randomization analysis suggested potential causal relationships between levels of serum CD300LG and fasting glucose, 2 hr glucose after an oral glucose tolerance test, and HbA1c. Additionally, Cd300lg responded to exercise in a mouse model, and we observed signs of impaired glucose tolerance in male, but not female, Cd300lg knockout mice.Conclusions:Our study identified several novel proteins in serum whose levels change in response to prolonged exercise and were significantly associated with body composition, liver fat, and glucose homeostasis. Serum CD300LG increased with physical activity and is a potential causal link to improved glucose levels. CD300LG may be a promising exercise biomarker and a therapeutic target in type 2 diabetes.Funding:South-Eastern Norway Regional Health Authority, Simon Fougners Fund, Diabetesforbundet, Johan Selmer Kvanes’ legat til forskning og bekjempelse av sukkersyke. The UK Biobank resource reference 53641. Australian National Health and Medical Research Council Investigator Grant (APP2017942). Australian Research Council Discovery Early Career Award (DE220101226). Research Council of Norway (Project grant: 325640 and Mobility grant: 287198). The Medical Student Research Program at the University of Oslo. Novo Nordisk Fonden Excellence Emerging Grant in Endocrinology and Metabolism 2023 (NNF23OC0082123).Clinical trial number:clinicaltrials.gov: NCT01803568.

A Game-Theoretic Framework for Generic Second-Order Traffic Flow Models Using Mean Field Games and Adversarial Inverse Reinforcement Learning

A traffic system can be interpreted as a multiagent system, wherein vehicles choose the most efficient driving approaches guided by interconnected goals or strategies. This paper aims to develop a family of mean field games (MFG) for generic second-order traffic flow models (GSOM), in which cars control individual velocity to optimize their objective functions. GSOMs do not generally assume that cars optimize self-interested objectives, so such a game-theoretic reinterpretation offers insights into the agents’ underlying behaviors. In general, an MFG allows one to model individuals on a microscopic level as rational utility-optimizing agents while translating rich microscopic behaviors to macroscopic models. Building on the MFG framework, we devise a new class of second-order traffic flow MFGs (i.e., GSOM-MFG), which control cars’ acceleration to ensure smooth velocity change. A fixed-point algorithm with fictitious play technique is developed to solve GSOM-MFG numerically. In numerical examples, different traffic patterns are presented under different cost functions. For real-world validation, we further use an inverse reinforcement learning approach (IRL) to uncover the underlying cost function on the next-generation simulation (NGSIM) data set. We formulate the problem of inferring cost functions as a min-max game and use an apprenticeship learning algorithm to solve for cost function coefficients. The results show that our proposed GSOM-MFG is a generic framework that can accommodate various cost functions. The Aw Rascle and Zhang (ARZ) and Light-Whitham-Richards (LWR) fundamental diagrams in traffic flow models belong to our GSOM-MFG when costs are specified. History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference. Funding: X. Di is supported by the National Science Foundation [CAREER Award CMMI-1943998]. E. Iacomini is partially supported by the Italian Research Center on High-Performance Computing, Big Data and Quantum Computing (ICSC) funded by MUR Missione 4-Next Generation EU (NGEU) [Spoke 1 “FutureHPC & BigData”]. C. Segala and M. Herty thank the Deutsche Forschungsgemeinschaft (DFG) for financial support [Grants 320021702/GRK2326, 333849990/IRTG-2379, B04, B05, and B06 of 442047500/SFB1481, HE5386/18-1,19-2,22-1,23-1,25-1, ERS SFDdM035; Germany’s Excellence Strategy EXC-2023 Internet of Production 390621612; and Excellence Strategy of the Federal Government and the Länder]. Support through the EU DATAHYKING is also acknowledged. This work was also funded by the DFG [TRR 154, Mathematical Modelling, Simulation and Optimization Using the Example of Gas Networks, Projects C03 and C05, Project No. 239904186]. Moreover, E. Iacomini and C. Segala are members of the Indam GNCS (Italian National Group of Scientific Calculus).

(Sensor Division Early Career Award) Portable Diagnostics for Cancer Management at Printed Strips

Point-of-care testing (PoC) is revolutionizing the healthcare sector improving patient care in daily hospital practice and allowing reaching even remote areas. In the frame of cancer management, the design and validation of PoC enabling the non-invasive, rapid detection of cancer markers is urgently required to implement liquid biopsy. However, despite substantial advances in sensing technologies, the development, preparation, and use of self-testing devices is still confined to specialist laboratories and users. Decentralized analytical devices will enormously impact daily lives, enabling people to analyze diverse clinical, environmental, and food samples, evaluate them and make predictions to improve quality of life, particularly in remote, resource-scarce areas. In recent years, paper-based analytical tools have attracted a great deal of attention; the well-known properties of paper, such as abundance, affordability, lightness, and biodegradability, combined with features of printed electrochemical sensors, have enabled the development of sustainable devices that drive (bio)sensors beyond the state of the art. Their blindness toward colored/turbid matrices (i.e., blood, soil), their portability, and the capacity of paper to autonomously filter/purge/react with target species make such devices powerful in establishing point-of-need tools for use by non-specialists. Depending on analytical requisites, different types of paper (filter, office) and configurations (1D, 2D, 3D) can be adopted. Focusing on stable blood-based markers with high-specificity, such as miRNAs, is of crucial importance. A wide overview regarding application ranging from DNA to miRNA detection will be provided, with the aim in showing the potentialities of portable electrochemical biosensors for improving society involvement in monitoring circulating nucleic acids associated to diverse conditions, i.e. cancer, Alzheimer’s disease, HIV.

2024 IEEE EDS Early Career Award

2024 IEEE EDS Early Career Award

Twenty-five Years of Award-winning Education Publication Excellence in ASHS Journals

The American Society for Horticultural Science Education Publication Excellence award was established in 1997 to recognize the most outstanding publication in ASHS journals related to education and teaching. This article reviews the award history, authors, and topics of the awarded papers. The award was recognized annually from 1998 to 2023 except for 3 years when no award was given. The majority of awarded papers were published in HortTechnology. Awards were presented to 70 authors from 23 institutions in 20 states and two other countries. Of the awarded papers, three had single authors, and 20 had multiple authors. Several awarded authors have been recognized in other ASHS publication awards or professional career awards. The majority of awarded papers focused on undergraduate students, teaching methods, and floriculture or ornamental topics.

Religion as a natural laboratory for understanding human behavior

What do we gain from the scientific study of religion? One possibility is that religious contexts are unique, and cognition within these contexts is worth understanding. Another possibility is that religion can be viewed as a laboratory for understanding psychology and culture more broadly. Rather than limiting the study of religion to a single context, I argue that the study of religion is useful precisely because it illuminates secular psychological and cultural processes. I first outline my practical approach to psychology and religion, focusing on how people use religion to advance mundane goals. I then discuss several domains in which studying religion has led to important insights, including culture, prejudice, and cognition. This article is an extended version of an Early Career Award address given at the International Association for the Psychology of Religion meeting in 2023 in Groningen, Netherlands.

Benchmarking Scholarship in Consumer Research: The p-Index of Thought Leadership

Abstract The assessment of consumer scholarship must move beyond a mere counting of the number of “A”s on a researcher’s CV to include at least some measure of impact. To facilitate a broader assessment of scholarship in consumer research, we provide detailed statistics on the productivity and citation impact of the field’s 340 main gatekeepers: the editors, associate editors, and editorial board members of the Journal of Consumer Research and the Journal of Consumer Psychology. In addition, we introduce a new metric, called the p-index, which can be interpreted as an indicator of a researcher’s propensity for thought leadership. Using this metric, we show that productivity and thought leadership do not necessarily go hand in hand in consumer research and that a combination of the two is a good predictor of the level of esteem that consumer scholars enjoy among their peers and of the receipt of major career awards. Our analyses provide greater transparency into how productivity, citation impact, and propensity for thought leadership are currently distributed among prominent consumer scholars. Furthermore, the detailed descriptive statistics reported can serve as useful benchmarks against which other consumer researchers’ records may be meaningfully compared.

Career trajectories of MD-PhD physician scientists: The loss of women investigators

Advances in biomedical research require a robust physician scientist workforce. Despite being equally successful at securing early career awards from the NIH as men, women MD-PhD physician scientists are less likely to serve as principal investigators on mid- and later careers awards. Here, we discuss the causes of gender disparities in academic medicine, the implications of losing highly trained women physician scientists, and the institutional and systemic changes needed to sustain this pool of talented investigators.

CD4+ T Cells from Women with Preeclampsia During Pregnancy Contribute to Hypertension, AT1-AA, and Neurovascular Dysfunction in a Pregnant Rat Model of PE

Objective: Preeclampsia (PE) is characterized by new onset hypertension (HTN) during pregnancy and is associated with activated CD4+ T cells and agonistic antibodies against the angiotensin II type 1 receptor (AT1-AA). Interestingly, having had COVID-19 during pregnancy is associated with increased incidence of a PE-like phenotype. Both PE and CV have long lasting neurological implications. We sought examine a role for CD4+ T cells from PE with or without a CV Hx to contribute to a PE phenotype and neurovascular dysfunction postpartum (PP). Methods: We utilized adoptive transfer of one million placental CD4+ T cells from PE and normotensive (NT) patients with or without a CV (Hx) during pregnancy injected i.p. into gestational day (GD) 12 nude athymic rats athymic and determined neurological consequences at 12 weeks PP. At GD19, blood pressure (MAP) and AT1-AA were measured. At 3 months PP, memory and error in navigation were assessed by Barnes maze consecutively for 5 days. CBF was measured by laser Doppler flowmetry. A two-way ANOVA was used for statistical analysis. Results: In pregnant recipient rats, MAP increased in CV Hx PE (113±2, n=8) and PE (115±2 mmHg, n=10) compared to CV Hx NT (104±4, n=7) and NT (98±2 mmHg, n=7 p<0.05). AT1-AA increased in PE (8±2 ΔBPM, n=4) and CV Hx PE (4±2 ΔBPM, n=4) compared to NT (-3±2 ΔBPM, n=4, p<0.05) and CV Hx NT (2±2 ΔBPM, n=2) respectively. At 3 months PP, PE and CV Hx PE had impaired CBF autoregulation compared to CV Hx NT and NT rats, and both PE groups exhibited greater mistakes and latency in maze navigation, however, the scores were worse in those receiving T cells from PE patients without a Hx of CV compared to all other groups. Conclusion: Our findings indicate that pregnant recipients of CD4+ T cells from PE with or without a CV Hx during pregnancy cause HTN and increased AT1-AA compared to recipients of NT or NT Hx CV-19 CD4 + T cells. Interestingly, recipients of T cells from PE patients without a Hx of CV had worse CBF and cognitive function at 3 months PP, demonstrating the importance of CD4+ T cells in HTN and impaired neurological function during pregnancy PP in patients with previous PE. This study was supported by NIH grants RO1HD067541 (BL), F31HD110230 (NC), P20GM121334 (BL, LMA), and American heart association (AHA) early career award 19CDA34670055 (LMA). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Call for Nominations: 2024 EDS Early Career Award

Call for Nominations: 2024 EDS Early Career Award

AT1-AA Induced Placental Ischemia Contributes to Hypertension in Female Offspring

Objective: Preeclampsia (PE), new-onset hypertension during the third trimester of pregnancy alongside other organ dysfunction, is associated with chronic inflammation and fetal growth restriction. Agonistic autoantibodies against the angiotensin II type 1 receptor (AT1-AA) are produced in PE women and induce a PE-like phenotype when infused into pregnant rats. AT1-AA is still present in maternal circulation up to eight years postpartum. We have shown that AT1-AA exposure during pregnancy contributes to impairment of cerebral blood flow in the postpartum period. Moreover, PE offspring are at higher risk for cardiovascular disease and hypertension but AT1-AA’s role in this elevated risk is unknown. The objective of this study was to determine if AT1-AA contributes to the risk for hypertension in PE offspring. Therefore, we hypothesized that AT1-AA infusion during pregnancy would cause offspring to have low birth weight and hypertension in adulthood. Methods: We infused AT1-AA (1:40) starting on gestational day (GD) 14 and allowed the dams to give birth on GD21. Birth weight was measured within 12 hours. Offspring were aged to 4 months. At that time, one male and female per litter were randomly selected to undergo carotid catheterization. The following day mean arterial pressure (MAP) was measured and blood and tissues were collected. Immune cells were measured by flow cytometry. Sex hormones were measured by mass spectroscopy. AT1-AA was measured by cardiomyocyte bioassay. Renal endothelin was measured by ELISA and RT-PCR. A two-way ANOVA was used for statistical analysis. Results: AT1-AA male and female offspring (6.1±0.1g, n=19, p<0.05; 5.8±0.1g, n=19, p<0.05) were larger at birth than NP male and female offspring (5.8±0.1g, n=11; 5.5±0.1g, n=11). Female AT1-AA offspring had elevated MAP (124±2 mmHg, n=14, p<0.05) compared to NP female offspring (110±5 mmHg, n=8) while male AT1-AA offspring had similar MAP to male NP offspring. Circulating cytolytic NK cells trended higher in female AT1-AA offspring (2.73±0.37 % gated, n=9, p=0.0949) compared to female NP offspring (1.66±0.31 % gated, n=6). Female AT1-AA offspring also had elevated progesterone (41.3±6.0 pg/μL, n=8, p=0.0556) compared to female NP offspring (26.9±4.9 pg/μL, n=7). Renal PPET expression was increased in female AT1-AA offspring (2.6±0.69 fold increase, n=4, p<0.05) compared to female NP offspring. Renal endothelin-1 was increased in female AT1-AA offspring (1.02±0.23 pg/mg, n=7, p=0.062) compared to female NP offspring (0.59±0.06 pg/mg, n=6). Male and female AT1-AA offspring had increased circulating AT1-AA (9.5±2.2 ΔBPM, n=6, p<0.0001; 7.9±0.9 ΔBPM, n=8, p<0.001) compared to male and female NP offspring (-0.4±.4 ΔBPM, n=7; -0.8±0.4 ΔBPM, n=4). Conclusion: These data demonstrate that AT1-AA infusion during pregnancy can predispose female offspring to have elevated blood pressure in adulthood. This study was supported by NIH grants RO1HD067541 (BL), F31HD110230 (NC), P20GM121334 (BL, LMA), HL151407 (DCC) and American heart association (AHA) early career award 19CDA34670055 (LMA). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

138 Supporting Early-Career Faculty Grant Proposals through Narrative Development Training: A Proposal Narrative Development Program for Early-Career Faculty

OBJECTIVES/GOALS: Faculty pursuing their first independent research grants often struggle to express their ideas in a concise, compelling way. Thus, we developed the “Research and Scholarship Storytelling Bootcamp” to equip these faculty with narrative development skills applicable across disciplines and mechanisms. METHODS/STUDY POPULATION: Early-career researchers who were preparing either their first NIH R-series application or an NSF CAREER award proposal were invited to participate. Enrollment was limited to 20 participants. Those accepted learned the “And-But-Therefore” narrative framework by reading a short book and attending 4 synchronous lectures. Between sessions, they applied the framework by drafting abstracts and Specific Aims/Project Summary documents and reviewing their fellow participants’ work. We assessed participants' comfort with storytelling, perceptions of preparedness, and confidence regarding funding chances, before and after the program using a visual analog scale (max 100 points) and calculated Cohen’s d to evaluate the effect size of any changes. RESULTS/ANTICIPATED RESULTS: Thirty people applied for 20 slots, indicating strong demand. Eleven NIH applicants and 9 NSF applicants enrolled. Before the program, participants rated their comfort with storytelling at 45 ± 25, their preparedness at 39 ± 24, and their funding confidence at 39 ± 26. Nine total participants completed all sessions, assignments, and surveys. Completion rates were comparable for NIH- and NSF-targeting participants. After the program, completing participants reported increases in their comfort with storytelling (68 ± 14 post vs 32 ± 20 pre, d = 1.46), perceived preparedness (64 ± 20 post vs 48 ± 26 pre, d=0.58), and confidence in funding chances (56 ± 19 post vs 40 ± 27 pre, d=0.75). DISCUSSION/SIGNIFICANCE: This program was the first of its kind for multidisciplinary early-career faculty at our institution. The program successfully achieved its objectives for those who completed all activities. Future analysis of survey comments and proposal success rates will reveal barriers to full program engagement and opportunities for further training.

Mean-Field Multiagent Reinforcement Learning: A Decentralized Network Approach

One of the challenges for multiagent reinforcement learning (MARL) is designing efficient learning algorithms for a large system in which each agent has only limited or partial information of the entire system. Whereas exciting progress has been made to analyze decentralized MARL with the network of agents for social networks and team video games, little is known theoretically for decentralized MARL with the network of states for modeling self-driving vehicles, ride-sharing, and data and traffic routing. This paper proposes a framework of localized training and decentralized execution to study MARL with the network of states. Localized training means that agents only need to collect local information in their neighboring states during the training phase; decentralized execution implies that agents can execute afterward the learned decentralized policies, which depend only on agents’ current states. The theoretical analysis consists of three key components: the first is the reformulation of the MARL system as a networked Markov decision process with teams of agents, enabling updating the associated team Q-function in a localized fashion; the second is the Bellman equation for the value function and the appropriate Q-function on the probability measure space; and the third is the exponential decay property of the team Q-function, facilitating its approximation with efficient sample efficiency and controllable error. The theoretical analysis paves the way for a new algorithm LTDE-Neural-AC, in which the actor–critic approach with overparameterized neural networks is proposed. The convergence and sample complexity are established and shown to be scalable with respect to the sizes of both agents and states. To the best of our knowledge, this is the first neural network–based MARL algorithm with network structure and provable convergence guarantee. Funding: X. Wei is partially supported by NSFC no. 12201343. R. Xu is partially supported by the NSF CAREER award DMS-2339240.

Learning-Augmented Mechanism Design: Leveraging Predictions for Facility Location

In this work, we introduce an alternative model for the design and analysis of strategyproof mechanisms that is motivated by the recent surge of work in “learning-augmented algorithms.” Aiming to complement the traditional worst-case analysis approach in computer science, this line of work has focused on the design and analysis of algorithms that are enhanced with machine-learned predictions. The algorithms can use the predictions as a guide to inform their decisions, aiming to achieve much stronger performance guarantees when these predictions are accurate (consistency), while also maintaining near-optimal worst-case guarantees, even if these predictions are inaccurate (robustness). We initiate the design and analysis of strategyproof mechanisms that are augmented with predictions regarding the private information of the participating agents. To exhibit the important benefits of this approach, we revisit the canonical problem of facility location with strategic agents in the two-dimensional Euclidean space. We study both the egalitarian and utilitarian social cost functions, and we propose new strategyproof mechanisms that leverage predictions to guarantee an optimal trade-off between consistency and robustness. Furthermore, we also prove parameterized approximation results as a function of the prediction error, showing that our mechanisms perform well, even when the predictions are not fully accurate. Funding: The work of E. Balkanski was supported in part by the National Science Foundation [Grants CCF-2210501 and IIS-2147361]. The work of V. Gkatzelis and X. Tan was supported in part by the National Science Foundation [Grant CCF-2210502] and [CAREER Award CCF-2047907]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/moor.2022.0225 .

(Battery Division Early Career Award Sponsored by Neware Technology Limited) Understanding Interfacial Mechanisms that Govern Performance in Solid-State Batteries

Solid-state batteries offer the promise of improved energy density and safety compared to lithium-ion batteries. Here, I will present our emerging understanding of the key differences between how high-capacity anode materials behave in solid-state batteries compared to in conventional liquid-electrolyte batteries. The electro-chemo-mechanical evolution of materials at solid-solid electrochemical interfaces is different than at solid/liquid interfaces, and contact evolution in particular plays a critical role in determining the behavior of solid-state batteries. I will focus on mechanisms governing lithium metal anodes (including lithium in the “anode-free” configuration) and alloy anodes. Lithium metal anodes in solid-state batteries are intrinsically limited by void formation during stripping and dendrite growth during plating. Anode-free solid-state batteries, in which there is no initial lithium metal at the anode interface, offer extremely high energy density, but there is a lack of understanding of how their behavior differs from excess-lithium electrodes. Using X-ray tomography, cryo-FIB, and finite-element modeling, we show that anode-free solid-state batteries are intrinsically limited by current concentrations at the end of stripping due to localized lithium depletion. This causes accelerated short circuiting compared to lithium-excess cells. Based on these results, the beneficial influence of metal alloy interfacial layers on controlling lithium evolution and mitigating contact loss from localized lithium depletion, including at low stack pressures, will be discussed. X-ray tomography is further shown to be particularly useful in observing the dynamic evolution of lithium metal, including void formation and filament growth. The second part of the talk focuses on alloy anodes. Alloy anodes typically exhibit fast capacity decay in lithium-ion batteries because of excessive solid-electrolyte interphase growth. We show that alloy anodes in solid-state batteries can exhibit improved interfacial stability and enhanced cyclability. Furthermore, in situ measurement of stack pressure evolution during cycling shows that the volume changes of alloy anodes can lead to large pressure swings within the cell, giving insight into electrode composite evolution. Based on these insights, we present a new design for dense foil alloy anodes. This design offers a paradigm that does away with slurry coating, potentially reducing manufacturing costs. Taken together, these findings show the importance of controlling chemo-mechanics and interfaces in solid-state batteries for improved energy storage capabilities.

Direct Observation of Solid-Phase Crystallization of an Amorphous Complex Oxide in the Transmission Electron Microscope

Many complex metal oxides possess intricate structure-function relations, resulting in a wide range of tunable functional properties. The introduction of disorder through amorphization provides additional opportunities for tuning the properties, particularly in the area of electrochemical energy storage and conversion [1]. Consequently, amorphous and partly crystalline complex oxides have garnered growing interest in recent years, with two primary goals: to harness their unique functional properties or to employ them as a chemically-flexible, far-from-equilibrium, thin-film processing platform that offers novel synthesis opportunities [2] due to the minimal energy input required to initiate crystallization and other structural relaxation processes.Relevant to both these aims, the processing-structure-function relationship for amorphous complex oxides is examined by in situ solid-phase crystallization (SPC) in the (scanning) transmission electron microscope ((S)TEM), focusing on perovskite-like (ABO3) mixed ion-electron conducting (MIEC) amorphous complex oxides which make promising low-temperature solid-oxide cell (SOC) electrodes [3] due to the widely observed disorder-mediated oxygen surface-exchange [4]. Key aspects of MIEC amorphous complex oxide processing, such as the evolution of structure and chemistry with temperature and the corresponding effect on conductivity are detailed in the amorphous La0.8Sr0.2MnO3-δ (LSM) system. Further, approaches to direct amorphous complex oxide processing towards desirable synthesis products based on these insights are discussed.Here, we elucidate down to the atomic scale the development of structural characteristics such as porosity, crystallized fraction, crystallite tension, size, curvature, and surface termination, with the aim to establish approaches to tune surface functionality in partly crystalline MIECs by heating or electron beam irradiation [5]. In situ electrical measurements are used to correlate these changes with electrical behavior, revealing that the onset of crystallization is correlated with an increase in electronic conductivity by small polaron hopping, attributed to oxygen-metal-oxygen bond alignment. Further, in the absence of epitaxial constraints, an immiscibility-driven self-organization of amorphous heterointerfacial structures manifests by spinodal decomposition, that is likely to be broadly relevant in low-temperature processing of amorphous complex oxides [6]. Thus, processing from an amorphous complex oxide precursor could be a method to establish uniformly-dispersed nanoscale heterointerfaces of tunable characteristic length scale throughout a thin film volume, while also enabling direct tuning of the porous mesostructure by electron beam irradiation. Overall, case studies of amorphous complex oxide crystallization are limited to relatively few chemical systems and processing conditions. Thus, extending this body of work to explore a broader processing space, and extracting fundamental insights to establish process-structure-function relationships are prerequisites to realizing the full potential of partly crystallineMIEC systems, and provides guidance for processing amorphous complex oxides generally. Acknowledgments The authors acknowledge the UC Irvine School of Engineering new faculty setup funds, the National Science Foundation CAREER award (DMR-2042638) and the UC Irvine Materials Research Institute (IMRI). References S. Yan ... H. Xia, Small (2019)D. Prakash, ... F. Cavallo, Small (2022).J. Zhang ... Y. Chen, Advanced Functional Materials (2022)T. Chen ... N. Perry, ACS Applied Materials & Interfaces (2019)J. Wardini ... W. Bowman (submitted)J. MacManus-Driscoll, Advanced Functional Materials (2010)

(Battery Division Early Career Award Sponsored by Neware Technology Limited) Towards Scalable and Sustainable Battery Recycling for Circular Energy Storage

The development of electric vehicles (EVs) and grid storage heavily relies on lithium-ion batteries (LIBs) that exhibit significantly improved performance, low cost, a reduced carbon footprint, and an abundance of resources. However, the growing consumption of LIBs has led to concerns regarding resource scarcity and price hikes for lithium and critical transition metals (such as Co and Ni) that are essential for LIB production. Currently, the recycling industry utilizes pyrometallurgical and hydrometallurgical processes to treat spent batteries. Although these methods are scalable, there is a pressing need for enhanced overall energy efficiency, reduced processing costs, and minimized environmental impact.Next-generation technologies, such as direct recycling, hold promise as a more cost-effective and energy-efficient alternative. However, the scalability of these approaches must be proven at an industrial scale. This presentation aims to delve into the state-of-the-art LIB battery recycling by comparing various technologies. In particular, we will extensively discuss the technical aspects of direct recycling, focusing on different LIB materials. Furthermore, we will address the key challenges and provide insights for the future development of LIB recycling.