Inherent Selectivity Research Articles

Omission of intraoperative drain placement during robotic partial nephrectomy and robotic radical prostatectomy is safe: an analysis of 18,000 patients

PurposePlacement of a drain during robotic assisted partial nephrectomy (RAPN) and robotic assisted radical prostatectomy (RARP) is standard practice for many urologists and can aid in assessment and management of complications such as urine leak, lymphocele, or bleeding. However, drain placement can cause discomfort and delay patient discharge, with questionable benefit. We aim to assess the correlation between drain placement with post operative complications.MethodsThe NSQIP targeted database was queried for patients who underwent RAPN or RARP from 2019 to 2021. Our primary outcomes included 30-day complication rates stratified by intraoperative drain placement. Secondary outcomes included procedure-specific complications, length of stay (LOS), and readmissions. Multivariable regression analyses, with Bonferroni correction, were performed for each post-operative complication.ResultsWe identified 4738 and 13,948 patients who underwent RAPN and RARP, respectively. Drains were not placed in 2258 (47.7%) and 6700 (48%) patients, respectively. On adjusted multivariable analysis in the RAPN cohort, omission of drain placement was associated with decreased LOS (β -0.45; 99.58% CI [-0.59, -0.32]) but no difference in overall complication rates. After adjusted analysis in the RARP cohort, omission of drain placement was associated with decreased risk of any complication (OR 0.73 [0.62–0.87]), infectious complication (OR 0.66 [0.49–0.89]), and LOS (β -0.30 [-0.37, -0.24]).ConclusionsUsing a large contemporary database, this study demonstrates that omission of drains during RAPN and RARP was safe without increased risk of postoperative complications. Despite inherent selection bias in this cohort, our data suggests that routine drain placement is not necessary for these procedures.

Construction of Spatially Separated Acid-Base Sites inside and outside Metal-Organic Framework Nanocrystals for Efficient Cascade Reactions.

The development of acid-base catalysts for one-pot cascade reactions remains challenging because of the inherent incompatibility of inorganic acid and base active sites. Here, we introduced an innovative approach that employs spatial separation to construct separated inorganic acid-base sites, achieving sequence control of acid-base cascade reactions. The as-prepared bifunctional catalyst applied metal-organic framework (MOF) nanocrystals as spatial separators, with the inside microcavities loaded with 1 nm inorganic polyoxometalate acid H3PMo12O40 clusters (NENU-5) and the outside crystal surface covered with basic CuCo layered double hydroxide (CuCo-LDH) nanosheets. By applying the resultant NENU-5@CuCo-LDH catalyst to cascade deacetalization-Knoevenagel condensation, over 99% of the benzaldehyde dimethyl acetal (BDMA) was transformed into the final benzylidene cyanoacetate (BCA) with a 99% yield at 80 °C and solvent-free conditions. Compared to the random NENU-5+CuCo-LDH, the NENU-5@CuCo-LDH with spatially separated acid-base sites indicated a higher reaction rate due to the designed reaction sequence and the shorter mass transfer pathway. Moreover, this hierarchical structure showed inherent shape selectivity and extraordinary stability. This study introduces spatial separation to address the incompatibility issue between inorganic acid and base active sites, offering novel perspectives for acid-base systems.

Locoregional Versus General Anesthesia in Prolonged Carotid Endarterectomy: A Propensity Score-Matched Study from the American College of Surgeons National Surgical Quality Improvement Program Database from 2011 to 2022

BackgroundCarotid endarterectomy (CEA) is an effective treatment for carotid stenosis. Previous studies yielded conflicting findings regarding postoperative outcomes after CEA when comparing those under general and locoregional anesthesia. However, these findings may be influenced by an inherent selection bias, as general anesthesia is frequently selected for more complex CEA. To counteract this selection bias, this study compared the 30-day outcomes of locoregional and general anesthesia in patients who underwent prolonged CEA. MethodsAdult patients undergoing CEA were identified in ACS-NSQIP targeted database from 2011-2022. Only cases with prolonged operative times (over third quartile) were selected. Patients who had symptomatic and asymptomatic carotid stenosis were examined separately. A 1:3 propensity-score matching was used to address pre-operative differences between patients under locoregional and general anesthesia to assess thirty-day postoperative outcomes. Moreover, factors associated with prolonged CEA were identified by multivariable logistic regression. ResultsAmong symptomatic patients who underwent prolonged CEA, 246 and 4,286 were under locoregional and general anesthesia, respectively. In asymptomatic patients, 388 had locoregional anesthesia and 5,137 had general anesthesia. After propensity-score matching, patients under locoregional and general anesthesia had comparable 30-day mortality (symptomatic: 1.63% vs 0.81%, p=0.28; asymptomatic: 0.77% vs 0.52%, p=0.70) and stroke (symptomatic: 4.88% vs 4.34%, p=0.72; asymptomatic: 1.29% vs 1.46%, p=1.00). All other 30-day outcomes were comparable between groups, except for symptomatic patients under locoregional had shorter operation time (p<0.01) and asymptomatic patients under locoregional had lower cranial nerve injury (2.06% vs 4.90%, p=0.02). High-risk anatomical factors, male sex, younger age, and certain comorbidities were associated with prolonged operative times. ConclusionPatients under both types of anesthesia had mostly comparable 30-day postoperative outcomes, including mortality and stroke, for both symptomatic and asymptomatic patients. Therefore, locoregional and general anesthesia appear to be equally effective in CEA cases characterized by anticipated complexity and, consequently, prolonged operative times.

Tabular deep learning: a comparative study applied to multi-task genome-wide prediction

PurposeMore accurate prediction of phenotype traits can increase the success of genomic selection in both plant and animal breeding studies and provide more reliable disease risk prediction in humans. Traditional approaches typically use regression models based on linear assumptions between the genetic markers and the traits of interest. Non-linear models have been considered as an alternative tool for modeling genomic interactions (i.e. non-additive effects) and other subtle non-linear patterns between markers and phenotype. Deep learning has become a state-of-the-art non-linear prediction method for sound, image and language data. However, genomic data is better represented in a tabular format. The existing literature on deep learning for tabular data proposes a wide range of novel architectures and reports successful results on various datasets. Tabular deep learning applications in genome-wide prediction (GWP) are still rare. In this work, we perform an overview of the main families of recent deep learning architectures for tabular data and apply them to multi-trait regression and multi-class classification for GWP on real gene datasets.MethodsThe study involves an extensive overview of recent deep learning architectures for tabular data learning: NODE, TabNet, TabR, TabTransformer, FT-Transformer, AutoInt, GANDALF, SAINT and LassoNet. These architectures are applied to multi-trait GWP. Comprehensive benchmarks of various tabular deep learning methods are conducted to identify best practices and determine their effectiveness compared to traditional methods.ResultsExtensive experimental results on several genomic datasets (three for multi-trait regression and two for multi-class classification) highlight LassoNet as a standout performer, surpassing both other tabular deep learning models and the highly efficient tree based LightGBM method in terms of both best prediction accuracy and computing efficiency.ConclusionThrough series of evaluations on real-world genomic datasets, the study identifies LassoNet as a standout performer, surpassing decision tree methods like LightGBM and other tabular deep learning architectures in terms of both predictive accuracy and computing efficiency. Moreover, the inherent variable selection property of LassoNet provides a systematic way to find important genetic markers that contribute to phenotype expression.

Drone-Based Localization of Hazardous Chemicals by Passive Smart Dust

The distribution of tiny sensors over a specific area was first proposed in the late 1990s as a concept known as smart dust. Several efforts focused primarily on computing and networking capabilities, but quickly ran into problems related to power supply, cost, data transmission, and environmental pollution. To overcome these limitations, we propose using paper-based (confetti-like) chemosensors that exploit the inherent selectivity of chemical reagents, such as colorimetric indicators. In this work, cheap and biodegradable passive sensors made from cellulose could successfully indicate the presence of hazardous chemicals, e.g., strong acids, by a significant color change. A conventional color digital camera attached to a drone could easily detect this from a safe distance. The collected data were processed to define the hazardous area. Our work presents a combination of the smart dust concept, chemosensing, paper-based sensor technology, and low-cost drones for flexible, sensitive, economical, and rapid detection of hazardous chemicals in high-risk scenarios.

Highly Selective Ammonia Detection in NiO‐Functionalized Graphene Micropatterns for Beef Quality Monitoring

AbstractGraphene has emerged as one of the most promising materials for next‐generation gas sensor platforms due to its high flexibility, transparency, and hydrophobicity. However, graphene shows inherent low selectivity in gas sensing. This has led to extensive development of noble‐metal decoration on graphene to modulate its surface chemistry for enhanced selectivity. While noble metals such as Pt, Pd, and Au have widely been employed to functionalize graphene surface, non‐noble metal decoration of graphene has remained underexplored. Here, an unprecedented room‐temperature self‐activated graphene gas sensor functionalized by NiO nanoparticles and its application to wearable devices monitoring ammonia gas in daily life are demonstrated. NiO‐functionalized graphene micropatterns show ultra‐high selectivity to ammonia with a low detection limit of 2.547 ppt. Density functional theory (DFT) calculations reveal that the strong attraction between NiO and NH3 induced by charge depletion and the vertex region of NiO accelerate the adsorption of NH3 molecules. Furthermore, a wearable graphene device demonstrates the capability to detect ammonia emissions from beef, triggering an alarm call when a specific threshold is exceeded. This work proposes the functionalization of graphene with transition metal oxides, extending beyond the conventional noble metal decoration, and the potential utilization of the graphene for wearable devices.

Unactivated Pd-catalyzed oxidative cross-coupling reactions of arenes with benzene: a theoretical and experimental study

Oxidative C–H bond activated palladium-catalyzed aromatic cross-coupling reactions offer advantages of fewer required synthetic steps but disadvantages of loss of inherent regioselectivity and selectivity. In one proposed catalytic cycle, each arene adds to a palladium carboxylate catalyst according to a concerted metalation deprotonation mechanism, followed by reductive elimination to couple the arenes and the replenishment of the catalyst by an oxidant. Using this proposed mechanism, molecular structures and free energy profiles were calculated for coupling of benzene with a range of heteroarenes containing various substituents and with several Pd-carboxylate catalysts. Reaction kinetics were analysed using the energetic span model, predicting turnover frequencies (TOFs) of heterocoupling (HB and BH) and homocoupling (HH and BB) pathways and assessing degrees of turnover frequency control (XTOFs). More electron-withdrawing carboxylate ligands and more electron-donating heteroarene substituents decrease activation and reaction free energies of addition steps, with C-2 more favourable than C-3. When heteroarene and catalyst choice are particularly favourable, the first association can become exergonic, causing a significant change in catalytic kinetics. When not exergonic, free energy for addition of benzene is almost always higher, leading to large energetic spans and slower TOFs (BB &lt; HB &lt; BH &lt; HH). Non-exergonic pathways are also faster with more electron-withdrawing ligands and more electron-donating heteroarene substituents because of lower energy addition steps and a smaller energetic span. For exergonic associations, the low-energy arene–catalyst intermediate creates a larger energetic span, slowing down the HH and HB pathways. More electron-withdrawing ligands and more electron-donating substituents increase the relative TOF for BH, and the reaction requires only small excesses of benzene to induce heterocoupling.

Synthesis of Highly Substituted Oxaboroles from Oxaboranes via a Selective Petasis Borono-Mannich Reaction.

We present a novel and efficient method for the synthesis of highly substituted non-benzofused oxaboroles. Reactions of oxaboranes, morpholine, and salicylaldehyde in toluene heated to 85 °C for 4 h produce the corresponding oxaborole products in yields up to 93%. The process is effective across a diverse substrate scope and can be scaled to produce gram quantities of densely functionalized oxaboroles in excellent yield. Exclusive aryl transfer over vinyl transfer is observed. Computational insights further elucidate the inherent selectivity of this process.

Promoting effect of Cu as electron transfer medium on NH3-SCO reaction in asymmetric Ag-Ov-Ti-Sm-Cu ring active site

Selective catalytic oxidation of ammonia (NH3-SCO) has become an effective method to reduce ammonia (NH3) emissions, and is a key part to solve the problem of NH3 pollution. Nevertheless, the optimization of this technology’s performance relies heavily on innovation and the development of catalyst design. In this study, a SmCuAgTiOx catalyst with an asymmetric Ag-Ov-Ti-Sm-Cu ring active site was prepared and applied to the NH3-SCO reaction. The low conversion of Cu-based catalysts in NH3 at low temperature and the inherent low N₂ selectivity of Ag-based catalysts were solved. The successful creation of the asymmetric ring active site improved the catalyst’s reduction performance. Additionally, Cu, acting as an electron transfer medium, plays a crucial role in enhancing electron transfer within the asymmetric ring active site, thus increasing the redox cycle of the catalyst during the reaction. In addition, some lattice oxygen is lost in the catalyst, resulting in the formation of a large number of oxygen vacancies. This process stimulates the adsorption and activation of surface-adsorbed oxygen, facilitating the conversion of NH3 to an amide (NH2) intermediate during the reaction and reducing non-selective oxidation. The N2 selectivity was improved without significantly affecting the performance of Ag-based catalyst. In-situ diffuse reflectance fourier transform infrared spectroscopy (In-situ DRIFTS) analysis reveals that the SmCuAgTiOx catalyst primarily follows an “internal” selective catalytic reduction (iSCR) mechanism in the NH3-SCO reaction, complemented by the imide mechanism. The asymmetric Ag-Ov-Ti-Sm-Cu ring active site developed in this study provides a new perspective for efficiently solving NH3 pollution in the future.

Flexible Acetylcholine Neural Probe with a Hydrophobic Laser-Induced Graphene Electrode and a Fluorous-Phase Sensing Membrane.

This work develops the first laser-induced graphene (LIG)-based electrochemical sensor with a superhydrophobic fluorous membrane for a flexible acetylcholine (ACh) sensor. ACh regulates several physiological functions, including synaptic transmission and glandular secretion. The ACh sensing membrane is doped with a fluorophilic cation-exchanger that can selectively measure ACh based on the inherent selectivity of the fluorous phase for hydrophobic ions, such as ACh. The fluorous-phase sensor improves the selectivity for ACh over Na+ and K+ by 2 orders of magnitude (compared to traditional lipophilic membranes), thus lowering the detection limit in artificial cerebrospinal fluid (aCSF) from 331 to 0.38 μ M, thereby allowing measurement in physiologically relevant ranges of ACh. Engraving LIG under argon creates a hydrophobic surface with a 133.7° contact angle, which minimizes the formation of a water layer. The flexible solid-contact LIG fluorous sensor exhibited a slope of 59.3 mV/decade in aCSF and retained function after 20 bending cycles, thereby paving the way for studying ACh's role in memory and neurodegenerative diseases.

Non-Invasive Disposable 2D Ti3C2Tx based Enzyme Free Electrochemical Sweat Glucose Biosensor

Modern analytical chemistry, enhanced by advancements in materials science, is crucial for cost-effectively improving measurement techniques, and making analyses more accessible and user-friendly. Disposable screen-printed electrodes have emerged as promising tools, offering miniaturization, high reproducibility, high sensitivity, rapid detection, and inherent selectivity. This study highlights the development of advanced 2D Ti3C2Tx MXene-modified disposable screen-printed gold electrodes for non-invasive, enzyme-free glucose sensing. Ti3C2Tx was synthesized using direct and in-situ etchants to achieve controlled surface chemistry. Techniques such as XRD, UV–visible spectroscopy, SEM, and TEM were employed to characterize Ti3C2Tx synthesized with different etchants. The synthesized materials were then used as working electrodes to investigate their electrocatalytic performance, tailored for continuous glucose monitoring. Upon optimization, in-situ LiF and HCl etched Li-Ti3C2Tx@SPGE exhibited a high sensitivity of 569.70 µA mM−1cm−2, followed by HF-Ti3C2Tx@SPGE and N-Ti3C2Tx@SPGE with sensitivities of 249.62 and 297.78 µA mM−1cm−2, respectively. These sensors demonstrated low limits of detection (LOD) of 5 nM, 28 nM, and 19 nM within a linear range of 5 to 350 µM. Additionally, real sweat analysis was conducted by collecting natural sweat through fast walking and evaluating sensor performance after beverage consumption. Overall, Li-Ti3C2Tx@SPGE, without any additional composition or doping, shows potential as a superior commercial sweat sensor for routine glucose monitoring. This study underscores the innovative use of 2D materials in developing highly sensitive, non-invasive sweat glucose sensors that are both effective and accessible.

A Co-Doping Materials Design Strategy for Selective Ozone Electrocatalysts.

Catalysts for electrochemical ozone production (EOP) face inherent selectivity challenges stemming from thermodynamic constraints. This work establishes a design strategy for minimizing these limitations and inducing EOP activity in tin oxide, which is an intrinsically EOP-inactive material. We propose that selective ozone production using tin oxide catalysts can be broadly achieved by co-doping with two elements: first, n-type dopants to enhance electrical conductivity, and second, transition metal dopants that leach and homogeneously generate essential hydroperoxyl radical intermediates. Synthesizing tantalum, antimony, and tungsten n-type dopants with nickel, cobalt, and iron as transition metal dopants confirms that properly co-doping tin oxide yields EOP-active catalysts. This study offers a robust framework for advancing EOP catalyst design and serves as a case study for the application of fundamental co-catalysis and solid-state physics principles to induce catalytic activity in inert materials.

Predicting State-Level Firearm Suicide Rates: A Machine Learning Approach Using Public Policy Data

IntroductionOver 40,000 people die by suicide annually in the U.S., and firearms are the most lethal suicide method. There is limited evidence on the effectiveness of many state-level policies on reducing firearm suicide. The objective of this study was to identify public policies that best predict state-level firearm suicide rates. MethodsData from the Centers for Disease Control and Prevention's WONDER system and the State Firearm Law Database, a longitudinal catalog of 134 firearm safety laws, were analyzed. The analysis included 1,450 observations from 50 states spanning 1991-2019. An ElasticNet regression technique was used to analyze the relationship between the policy variables and firearm suicide rates. Nested cross-validation was performed to tune the model hyperparameters. The study data were collected and analyzed in 2023 and 2024. ResultsThe optimized ElasticNet approach had a mean MSE of 2.07, which was superior to non-regularized and dummy regressor models. The most influential policies for predicting the firearm suicide rate on average included laws requiring firearm dealers that sell handguns to have a state license and laws requiring individuals to obtain a permit to purchase a firearm through an approval process that includes law enforcement, among others. ConclusionsOn average, firearm suicide rates were lower in state-years that had each influential policy active. Notably, these analyses were ecological and non-causal. However, this study was able to use a supervised machine learning approach with inherent feature selection and many policy types to make predictions using unseen data (i.e., balancing Lasso and Ridge regularization penalties).

One‐Pot Multicomponent Selective Synthesis of 5‐Trifluoromethyl Pyrimidine Derivatives

AbstractA one‐pot multi‐component reaction strategy for the selective synthesis of 5‐trifluoromethyl pyrimidine derivatives has been established. This method avoids the inherent selectivity challenges in direct pyrimidine trifluoromethylation. The reaction demonstrates tolerance to various functional groups, yielding 5‐trifluoromethyl pyrimidine derivatives with up to 80% yields. Moreover, the practicality of this method has been underscored through scale‐up reactions.

Representativeness of race, ethnicity and social determinants of health in patients with advanced non-small cell lung cancer via linkage of real-world electronic health records, administrative claims, and consumer financial data.

e13559 Background: Social determinants of health (SDOH) are both drivers of health disparities and barriers to clinical research participation, particularly among historically underserved or marginalized populations. Using real-world data (RWD), diversity plans in cancer clinical trials frequently aim to reduce disparities and improve representation. Our aim was to describe the representativeness of patients with advanced non-small cell lung cancer (aNSCLC) with respect to race, ethnicity and person-level SDOH in a curated electronic health record (EHR) database. Methods: This was a retrospective cohort study of individuals diagnosed with aNSCLC between 2016–2022 in a US-based oncology EHR database (ConcertAI). Information on person-level SDOH was derived from linkage of curated EHR data to administrative claims and consumer financial data. These data were also compared to a sample of patients diagnosed with first primary NSCLC from the Surveillance, Epidemiology, and End Results (SEER) Program and American Community Survey (ACS), where possible. The Kaplan-Meier product limit estimator was used to compare time from advanced diagnosis to treatment initiation. Results: From an overall sample of 17,772 patients with aNSCLC, a total of 10,390 patients had 1 or more SDOH characteristics of interest documented. Compared with patients without linked SDOH data, a higher proportion of patients with linked person-level SDOH data were non-Hispanic (NH) White patients (79% vs 71%) and lower proportions were NH Black (10% vs 13%), NH Asian/Pacific Islander (2% vs 4%) and Hispanic patients (2% vs 4%). Patients with linked SDOH were also more likely to have aNSCLC progressed from an earlier stage and received biomarker testing for targetable alterations. Overall, 54% of patients were married, 38% completed a bachelor’s degree or higher, 14% worked in a blue-collar occupation, and 61% had an individual income of ≤$35,000. All 4 of these SDOH characteristics differed significantly by race/ethnicity. Differences in time-to-treatment initiation were statistically significant by marital status (median of 50 [married] vs 55 [not married] days; P=0.02) and individual income (median of 50 [&gt;$60,000] vs 55 [≤$30,000] days; P=0.04). Patients with aNSCLC in these data differed from patients in SEER and ACS estimates with respect to greater representation of patients who are NH White, have lower levels of education, and lower individual income. Conclusions: This analysis demonstrated the feasibility of measuring person-level SDOH using EHR from patients with aNSCLC treated at oncology clinics across the US. RWD with linked SDOH characteristics should be used cautiously to address the representativeness gap in evidence from oncology trials given their potential inherent selection biases.

Holographic multiplexing in a photopolymerisable hybrid sol-gel

Holographic multiplexing techniques enhance functionality and information storage by leveraging the inherent selectivity of holograms. This is crucial for advancing holographic sensors, which excel in simultaneously detecting multiple parameters from a single input signal. This study explores the potential of the recent photopolymerisable hybrid sol-gel (PHSG) material for application in Space sensing systems through the investigation of its holographic angular multiplexing capabilities. For the first time, to the best of our knowledge, we report the successful recording of up to five angularly multiplexed gratings with diffraction efficiencies (DE) ≥ 15% in 187 ± 18 µm PHSG layers. A 3 mW/cm2 laser beam was used to record gratings (0–20° angular separation) with a spatial frequency of 800 ± 20 lines/mm utilising different exposure times. The study revealed that each successive multiplexing in the single-layer region resulted in a decrease in the material's recording sensitivity. Holographic recording sensitivity and DE growth during the grating formation period depend on the number of gratings multiplexed in the layer. The seven-month-old, multiplexed gratings demonstrate consistent DE, stable angular selectivity and diffraction angle. This study positions the PHSG material as a promising candidate for developing reliable multiplexed devices.

Dual preservative effects of SnO2-chitosan on Cu1+-doped boehmite composites for stable CO adsorption properties

In the realm of environment and energy sectors, the strategic designing of adsorbents utilizing active Cu1+-based composite materials stands as a pivotal initiative for effective CO removal. However, the commercial viability of these composites was hindered by oxidation tendency of active Cu1+ species during ambient storage, leading to a gradual decline in activity. Concurrently, selectivity constraints pose challenges to the practical utility of Cu1+-based CO adsorbents. In this study, a synergistic dual inorganic–organic stabilization system was introduced to address inherent stabilization and selectivity constraints. The SnO2-chitosan coupled stabilizer was employed to exploit dual chemical stabilization interactions which involved synergistic redox behavior within active Cu2O-SnO2 interface stemming from strong Sn-O interaction in contrast to that of Cu–O and coordinated chelation facilitated by the unsaturated nitrogen sourced from chitosan in conjunction with Cu1+ species. These mechanisms effectively preserved the oxidative susceptibility of active Cu1+ ions and impeded their gradual deactivation. The stable structural formation and complexation effects were validated through TEM, XPS, and in-situ FTIR analyses. Remarkably, incorporating a small quantity of SnO2 with organic chitosan stabilizer within the composite maintained ∼ 99 % stability of the initial adsorption uptake (∼24 cm3/g) even after 30-day storage under ambient conditions, surpassing the adsorption stability performance of the adsorption composite with only organic chitosan stabilizer. Furthermore, inclusion of SnO2 attenuated the surface basicity stemming from nitrogen-doping which favors the CO2 adsorption over CO, and due to the enhanced surface acidity as confirmed through NH3-TPD and in-situ FTIR analyses, a higher CO selectivity of 7.1 over CO2 was achieved. In conclusion, this study highlighted the advantageous potential of an inorganic–organic stabilizing system, specifically SnO2-chitosan combination, offering a viable approach to address stability and selectivity challenges associated with the Cu1+-based CO adsorption, presenting a viable pathway for the development of practical CO adsorbents.

Highly area-selective atomic layer deposition of device-quality Hf1-xZrxO2 thin films through catalytic local activation

Area-selective atomic layer deposition (AS-ALD) has garnered significant attention as a promising bottom-up patterning process for sub-10 nm scale technology, offering unmatched atomic-scale precision and pattern alignment capabilities in 3D nanofabrication. In this study, we present a groundbreaking methodology for achieving highly selective deposition of Hf1-xZrxO2 (HZO) thin films through catalytic local activation on noble metal surfaces (Ru and Pt) and TiN surfaces, without the need for surface inhibitory materials. To achieve inherent selectivity on metal surfaces, we employ O2 gas as a mildly oxidizing reactant and utilize cyclopentadienyl-tris(dimethylamido)-hafnium(zirconium) [Hf(Zr)Cp(NMe2)3] precursors, which require strong oxidizing agents, for HZO film formation. By catalytically dissociating O2 molecules, we successfully achieved area-selective deposition of HZO films greater than ∼7 nm on both blanket Ru versus Si substrates and Pt/Si-patterned substrates. Furthermore, we demonstrate the selective deposition of antiferroelectric HZO thin films with high dielectric constants of 34 and 31 on Ru and TiN substrates, respectively, using the inherent AS-ALD method combined with post-ozone treatment. Importantly, this catalytic local activation approach for achieving inherent deposition selectivity expands the potential utility of 3D bottom-up nanopatterning processes in next-generation nanoelectronic applications.

Abstract 5228: Engineering CD123-targeting vδ1-enriched γδT cells with enhanced preclinical efficacy and safety profile

Abstract Acute myeloid leukemia (AML) is an aggressive hematological malignancy that, despite advances in treatment, has an overall 5-year survival rate of less than 30%. There is an urgent unmet need for new and effective therapies for AML. Chimeric antigen receptor (CAR)-modified cell therapies are emerging as promising options. However, the selection of suitable antigens is challenging, due to the heterogeneous expression of potential antigen targets in AML. Most antigens, such as CD123, are expressed on both leukemic cells and normal hematopoietic stem and progenitor cells. In addition, conventional autologous or allogeneic T cell therapies present many limitations, such as cytokine release syndrome (CRS) and graft-vs-host disease (GvHD). We therefore evaluated CD123-targeting CAR designs in vδ1-enriched γδT cells, an innate immune cell platform that is inherently non-MHC restricted, has low risks of CRS and GvHD, and is capable of discriminating between malignant and healthy cells in preclinical models. We engineered vδ1-enriched γδT cells from peripheral blood of healthy individuals to express various CD123-targeting CAR constructs. The CD123-targeting CAR-engineered vδ1-enriched γδT cells expressed high levels of natural cytotoxicity receptors, suggesting capability to target innate receptor ligands on AML cell surface. CD123-targeting CAR-expressing vδ1-enriched γδT cells demonstrated enhanced cytotoxicity in vitro against multiple AML cell lines, in both short-term co-culture and repeated antigen stimulation assays. CD123-targeting CAR-expressing vδ1-enriched γδT cells also showed enhanced efficacy and survival in vivo against mouse models of human AML. Interestingly, attenuation of CAR activity via novel designs preserved the inherent selectivity of vδ1-enriched γδT cells against AML cells relative to normal hematopoietic stem or progenitor cells, which also express CD123. Together, our results demonstrated through rational CAR designing and engineering in vδ1-enriched γδT cells, an allogeneic cell therapy targeting CD123 can be generated with selective cytotoxicity against AML cells while sparing normal hematopoietic stem cells. Such product designs have the potential to be a transformative therapeutic option for a broad population of AML patients. Citation Format: Mei Rosa Ng, Sarah L. Hayward, Di Zhang, Gary Gu, Bri Paulhus, Donjeta Gjuka, Collin Crowley, Sophie McLaughlin, Anya Lublinsky, Angel Mathew, Evans Berreondo Giron, Doanh Mai, Madisen Lee, Megha Wal, Rebecca Alade, Andre Simoes, Robin Lytle, James Gavin, Shira Cramer, Zhong-hua Yan, Taylor Hickman, Rashmi Choudhary, Cori Abikoff, Yana Wang, Istvan Kovacs, Nandhu M. Sobhana, Lan Cao. Engineering CD123-targeting vδ1-enriched γδT cells with enhanced preclinical efficacy and safety profile [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5228.

Calcium additives enhance coke migration and catalyst stability for Pt-based catalysts in ethane dehydrogenation

Understanding and mitigating catalyst deactivation in high-temperature alkane dehydrogenation reactions is critical toward improving their energy and atom efficiency. In this work, we show that a partial overcoat of calcium oxide on a Pt-In alloy catalyst reduces the rate of catalyst deactivation by 10-fold during the non-oxidative dehydrogenation of ethane to ethylene. During 20 h of time-on-stream at 600 °C in 5 % C2H6, the Ca-doped PtIn2 catalyst retains >85 % of its initial activity while the undoped PtIn2 deactivates to <20 %. Temperature-programmed oxidation and spatially-resolved Raman spectroscopy reveal that the improved catalyst stability stems from enhanced migration of coke precursors away from the catalytically-relevant Pt sites to the silica support. Strikingly, the Ca overcoat does not alter the inherent rate and selectivity of coke formation but rather its crystallinity and localization, which nonetheless has a dramatic impact on the long-term performance of the catalyst.