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Photocatalysis using complexes of d0 metals with ligand-to-metal charge-transfer (LMCT) excited states is an active area of research. Because titanium is the second most abundant transition metal in the earth’s crust, d0 complexes of TiIV are an appropriate target for this research. Recently, our group has demonstrated that the arylethynyltitanocene Cp2Ti(C2Ph)2CuBr is not emissive in room-temperature fluid solution, whereas the corresponding Cp* complex, Cp*2Ti(C2Ph)2CuBr, is emissive. The Cp* ligand is hypothesized to provide steric constraint that inhibits excited-state structural rearrangement. However, modifying the structure also changes the orbital character of the excited state. To investigate the impact of the excited-state orbital character on the photophysics, herein we characterize complexes similar to Cp*2Ti(C2Ph)2CuBr—but one with a more electron-rich arylethynyl ligand, ethynyldimethylaniline (C2DMA), and one with a more electron-poor arylethynyl ligand, ethynyl-α,α,α-trifluorotoluene. We have also prepared complexes with the C2DMA ligand but with different Cp ligands that adjust the steric bulk and constraint around the Ti, by replacing the Cp* ligands with either indenyl ligands or an ansa-cyclopentadienyl ligand where the two Cp ligands are bridged by a dimethylsilylene. All four target complexes have been characterized crystallographically and structure activity relationships are highlighted. 

A thermal desorption GC-tandem MS method was developed to measure vapor-phase PFAS and their potential migration into indoor air. 

Public sector leverages artificial intelligence (AI) to enhance the efficiency, transparency, and accountability of civic operations and public services. This includes initiatives such as predictive waste management, facial recognition for identification, and advanced tools in the criminal justice system. While public-sector AI can improve efficiency and accountability, it also has the potential to perpetuate biases, infringe on privacy, and marginalize vulnerable groups. Responsible AI (RAI) research aims to address these concerns by focusing on fairness and equity through participatory AI. We invite researchers, community members, and public sector workers to collaborate on designing, developing, and deploying RAI systems that enhance public sector accountability and transparency. Key topics include raising awareness of AI's impact on the public sector, improving access to AI auditing tools, building public engagement capacity, fostering early community involvement to align AI innovations with public needs, and promoting accessible and inclusive participation in AI development. The workshop will feature two keynotes, two short paper sessions, and three discussion-oriented activities. Our goal is to create a platform for exchanging ideas and developing strategies to design community-engaged RAI systems while mitigating the potential harms of AI and maximizing its benefits in the public sector. 

Classroom sensing systems can capture data on teacher-student behaviours and interactions at a scale far greater than human observers can. These data, translated to multi-modal analytics, can provide meaningful insights to educational stakeholders. However, complex data can be difficult to make sense of. In addition, analyses done on these data are often limited by the organization of the underlying sensing system, and translating sensing data into meaningful insights often requires custom analyses across different modalities. We present Edulyze, an analytics engine that processes complex, multi-modal sensing data and translates them into a unified schema that is agnostic to the underlying sensing system or classroom configuration. We evaluate Edulyze’s performance by integrating three sensing systems (Edusense, ClassGaze, and Moodoo) and then present data analyses of five case studies of relevant pedagogical research questions across these sensing systems. We demonstrate how Edulyze’s flexibility and customizability allow us to answer a broad range of research questions made possible by Edulyze’s translation of a breadth of raw sensing data from different sensing systems into relevant classroom analytics.  

The tuning of the luminescent properties of PtII complexes for possible use in organic light-emitting diodes (OLEDs) and sensing applications is commonly achieved by altering the electronic properties of the ligands. Our group recently demonstrated that the trifluoropropynyl ligand is strongly electron-withdrawing and possibly useful for blueshifting emission. Herein, we report the synthesis of two complexes of this trifluoropropynyl ligand, namely PtLC2CF3 and PtLFC2CF3 (L = 1,3-di(2-pyridyl)benzene; LF = 4,6-difluoro-1,3-di(2-pyridyl)benzene). The PtLC2CF3 complex crystallized in the monoclinic space group P21/n with Z = 4. The PtLFC2CF3 complex crystalized in the triclinic space group P-1 with Z = 2. Changing the tridentate ligand from L to LF resulted in a change in the packing structure, with the latter showing a metallophilic interaction (Pt-Pt distance = 3.3341(3) Å). The solution photophysics of the trifluoropropynyl complexes is compared with that of the corresponding Cl complexes, PtLCl and PtLFCl. Replacement of the chloro ligand with the trifluoropropynyl ligand blueshifted the monomer emission by less than 5 nm but blueshifted the excimer emission peaks by 15–20 nm. The complexes of the trifluoropropynyl ligand also favor the excimer emission more than the complexes of the chloro ligand. The excimer emission is quenched by dissolved oxygen significantly more than the corresponding monomer emission. The excimer emission and monomer emission are well separated, and the ratio of monomer to excimer emission is strongly dependent on oxygen concentration. 

Ambient classroom sensing systems offer a scalable and non-intrusive way to find connections between instructor actions and student behaviors, creating data that can improve teaching and learning. While these systems effectively provide aggregate data, getting reliable individual student-level information is difficult due to occlusion or movements. Individual data can help in understanding equitable student participation, but it requires identifiable data or individual instrumentation. We propose ClassID, a data attribution method for within a class session and across multiple sessions of a course without these constraints. For within-session, our approach assigns unique identifiers to 98% of students with 95% accuracy. It significantly reduces multiple ID assignments compared to the baseline approach (3 vs. 167) based on our testing on data from 15 classroom sessions. For across-session attributions, our approach, combined with student attendance, shows higher precision than the state-of-the-art approach (85% vs. 44%) on three courses. Finally, we present a set of four use cases to demonstrate how individual behavior attribution can enable a rich set of learning analytics, which is not possible with aggregate data alone. 

Policies significantly shape computation’s societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at the “border” of HCI and policy. This paper asks: What if HCI considers policy integral to its intellectual concerns, placing system-people-policy interaction not at the border but nearer the center of HCI research, practice, and education? What if HCI fosters a mosaic of methods and knowledge contributions that blend system, human, and policy expertise in various ways, just like HCI has done with blending system and human expertise? We present this re-imagined HCI-policy relationship as a provocation and highlight its usefulness: It spotlights previously overlooked system-people-policy interaction work in HCI. It unveils new opportunities for HCI’s futuring, empirical, and design projects. It allows HCI to coordinate its diverse policy engagements, enhancing its collective impact on policy outcomes. 

Clinical practice guidelines, care pathways, and protocols are designed to support evidence-based practices for clinicians; however, their adoption remains a challenge. We set out to investigate why clinicians deviate from the “Wake Up and Breathe” protocol, an evidence-based guideline for liberating patients from mechanical ventilation in the intensive care unit (ICU). We conducted over 40 hours of direct observations of live clinical workflows, 17 interviews with frontline care providers, and 4 co-design workshops at three different medical intensive care units. Our findings indicate that unlike prior literature suggests, disagreement with the protocol is not a substantial barrier to adoption. Instead, the uncertainty surrounding the application of the protocol for individual patients leads clinicians to deprioritize adoption in favor of tasks where they have high certainty. Reflecting on these insights, we identify opportunities for technical systems to help clinicians in effectively executing the protocol and discuss future directions for HCI research to support the integration of protocols into clinical practice in complex, team-based healthcare settings. 

Teaching is one of many professions for which personalized feedback and reflection can help improve dialogue and discussion between the professional and those they serve. However, professional development (PD) is often impersonal as human observation is labor-intensive. Data-driven PD tools in teaching are of growing interest, but open questions about how professionals engage with their data in practice remain. In this paper, we present ClassInSight, a tool that visualizes three levels of teachers’ discussion data and structures reflection. Through 22 reflection sessions and interviews with 5 high school science teachers, we found themes related to dissonance, contextualization, and sustainability in how teachers engaged with their data in the tool and in how their professional vision, the use of professional expertise to interpret events, shifted over time. We discuss guidelines for these conversational support tools to support personalized PD in professions beyond teaching where conversation and interaction are important. 

Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicine. As a team of data scientists, clinicians, and HCI researchers, we conducted a series of design workshops to explore more effective approaches to AI concept ideation and problem formulation. We detail our process, the challenges we encountered, and practices and artifacts that proved effective. We discuss the research implications for improved collaboration and stakeholder engagement, and discuss the role HCI might play in reducing the high failure rate experienced in AI innovation.