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The Princeton Journal of Pre-Collegiate Research publishes original, peer-reviewed research by high school students across the sciences, social sciences, humanities, and interdisciplinary fields.

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Trending Publications

Environmental Science

Arctic Sea Ice Extent Decline, Albedo Feedback Amplification, and Implications for Northern Hemisphere Atmospheric Circulation: A 38-Year Satellite Record Analysis

Sigrid M. Thorvaldsen

Computer Science

Hierarchical Model-Based Reinforcement Learning for Dexterous Robotic Manipulation With Sparse Binary Rewards

Omar Y. Al-Hassani; Priya N. Chandrasekaran; Lena M. Kirchner

Psychology

Prosodic Transfer and Pitch Accent Acquisition in Mandarin-English L2 Learners: A Computational Acoustic and Perceptual Study

Linlin T. Zhao; Rodrigo M. Fernandez; Elke A. Bauer

Biology

Paleoproteomics and Isotopic Evidence for Denisovan Dietary Breadth in High-Altitude Environments: Collagen Carbon and Nitrogen Isotope Analysis of Baishiya Karst Cave Specimens

Katja M. Hoffmann; Riku T. Nakano; Abena K. Boateng

Medicine

Parental Vaccine Hesitancy Determinants and Pediatric Influenza Vaccination Coverage: A 24-State Survey Study With Structural Equation Modeling of Trust, Risk Perception, and Information Source

Amara T. Diallo; Miriam N. Vogel; Olumide K. Adeyemi

Biology

Guide RNA Design Parameters and CRISPR-Cas9 Editing Efficiency in Arabidopsis thaliana Transformation Systems

Biology

In Vitro Antifungal Activity of Thymus vulgaris Essential Oil Components Against Clinical Isolates of Candida albicans

Biology

Artificial Light at Night Suppresses Nocturnal Melatonin Secretion and Disrupts Migratory Restlessness in Sylvia atricapilla

Biology

Transcriptome-Wide Analysis of CD8+ T Lymphocyte Activation Dynamics During Acute Influenza A Virus Challenge in a Murine Model

Biology

Antibiotic Resistance Patterns in Escherichia coli Isolated from Urban Water Sources in the Northeastern United States

Antibiotic resistance in environmental bacteria represents a growing public health concern, particularly in densely populated urban areas where wastewater treatment may be incomplete. This study examines resistance patterns in Escherichia coli isolates obtained from urban water sources across three major cities in the northeastern United States. A total of 240 isolates were collected from 48 sampling sites over a six-month period and subjected to disk diffusion testing against twelve antibiotic agents spanning five drug classes. Results indicate that 67.4% of isolates exhibited resistance to at least one antibiotic, while multidrug resistanceâdefined as resistance to three or more drug classesâwas detected in 23.8% of isolates. Resistance rates were significantly higher downstream of wastewater treatment plant outflows (p < 0.001) and in sampling sites adjacent to high-density residential zones (p = 0.004). These findings underscore the need for enhanced surveillance programs and improved wastewater treatment protocols in urban environments to mitigate the spread of antibiotic-resistant organisms through municipal water systems.

Elena M. Hartley; David K. Osei; Priya R. Subramaniam

Chemistry

Reduced Graphene Oxide Lamellar Membranes for Selective Ion Rejection in Nanofiltration: Role of Interlayer Spacing and Surface Functionalization

Chemistry

Structure-Activity Relationships of Synthetic Peptide Inhibitors Targeting PCSK9-LDLR Protein-Protein Interaction

Chemistry

Cesium Formamidinium Lead Iodide Perovskite Solar Cells: Compositional Tuning for Enhanced Efficiency and Ambient Stability

Chemistry

Engineered FAST-PETase Variants for Accelerated Enzymatic Depolymerization of Post-Consumer Polyethylene Terephthalate

Chemistry

Amine-Functionalized UiO-67 Metal-Organic Frameworks for Enhanced Selective CO2 Adsorption Under Post-Combustion Capture Conditions

This study investigates selective CO2 adsorption in amine-functionalized zirconium metal-organic frameworks within the context of materials chemistry and porous coordination polymer research, an area of growing scientific importance given its implications for industrial carbon capture from cement and power generation point sources. Using volumetric gas adsorption measurements and grand canonical Monte Carlo simulation, we examine amine-CO2 chemisorption at Lewis acid-base pair sites within the MOF pore network in 24 synthesized MOF variants with systematic linker functionalization drawn from laboratory synthesis and characterization conditions at 298 K and 1 atm. Results indicate that diamine-functionalized UiO-67 achieves CO2 uptake of 4.8 mmol/g and CO2/N2 selectivity of 34, significantly exceeding parent frameworks (p < 0.001), with 47.3% greater CO2 uptake as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to materials chemistry and porous coordination polymer research and carry actionable implications for the design of programs and policies targeting industrial carbon capture from cement and power generation point sources.

Nathaniel J. Forsythe; Mei-Ling Xu; Andrei V. Popescu

Physics

High-Fidelity Photon Pair Entanglement in GaAs Photonic Crystal Nanocavities via Spontaneous Parametric Downconversion

Physics

JWST NIRSpec Transmission Spectroscopy of a Sub-Neptune Exoplanet: Detection of H2O, CO2, and CH4 Absorption in a Temperate Atmosphere

Physics

P-Wave Amplitude Decay and Magnitude Estimation Uncertainty in Regional Earthquake Early Warning Systems: Analysis of 8,412 Pacific Northwest Events

Physics

LIGO-Virgo Binary Neutron Star Merger Detection and Multi-Messenger Counterpart Analysis: Constraints on the Neutron Star Equation of State From Tidal Deformability

Physics

Phonon-Mediated Thermal Conductivity in Suspended Graphene Membranes: Temperature Dependence from 77 K to 500 K

This study investigates phonon-mediated thermal transport in suspended single-layer graphene membranes across a 77-500 K temperature range within the context of condensed matter physics and 2D materials science, an area of growing scientific importance given its implications for thermal management design in graphene-based nanoelectronic and photonic devices. Using optothermal Raman thermometry with laser power-dependent temperature calibration, we examine Umklapp phonon-phonon scattering and defect-boundary scattering limiting phonon mean free path in 18 suspended graphene membranes spanning three defect density categories drawn from cryogenic and elevated-temperature measurement stages under high-vacuum conditions. Results indicate that thermal conductivity decreases from 4,200 W/mK at 100 K to 1,840 W/mK at 500 K in pristine samples, with defect engineering enabling tunable conductivity across a 4.2-fold range (p < 0.001), with 4.2-fold tunable conductivity range as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to condensed matter physics and 2D materials science and carry actionable implications for the design of programs and policies targeting thermal management design in graphene-based nanoelectronic and photonic devices.

Dmitri A. Volkov; Isabella C. Romano; Jin-Su Kwon

Computer Science

Convergence Rate Bounds for Stochastic Gradient Descent With Adaptive Step Sizes on Non-Convex Loss Surfaces

Computer Science

Privacy-Preserving Federated Learning With Adaptive Differential Privacy Calibration for Heterogeneous Client Distributions

Computer Science

Temporal Fusion Transformers With Hierarchical Attention for Long-Horizon Multivariate Time-Series Forecasting

Computer Science

Hierarchical Model-Based Reinforcement Learning for Dexterous Robotic Manipulation With Sparse Binary Rewards

Computer Science

Graph Convolutional Networks for Prediction of Protein-Ligand Binding Affinity From Molecular Graph Representations

This study investigates graph convolutional network prediction of protein-ligand binding affinity within the context of computational chemistry and machine learning for drug discovery, an area of growing scientific importance given its implications for virtual screening pipelines and lead optimization in pharmaceutical drug discovery. Using graph convolutional neural network (GCN) architecture trained on molecular graph encodings with cross-validation benchmarking, we examine message-passing aggregation over molecular graphs capturing atomic interactions predictive of binding affinity in 11,908 protein-ligand complexes from the PDBbind v2020 refined set drawn from curated structural biology databases under standardized featurization protocols. Results indicate that the proposed GCN architecture achieves a Pearson r of 0.84 and RMSE of 1.14 kcal/mol on the PDBbind core set, outperforming fingerprint baselines by 11.4% in correlation (p < 0.001), with 11.4% improvement over fingerprint baseline as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to computational chemistry and machine learning for drug discovery and carry actionable implications for the design of programs and policies targeting virtual screening pipelines and lead optimization in pharmaceutical drug discovery.

Aisha N. Patel; Etienne M. Beauchamp; Lucas H. Rodrigues

Engineering

Guided Lamb Wave Propagation in Embedded Piezoelectric Transducer Networks for Structural Health Monitoring of Steel Bridge Girders

Engineering

Phase Change Material Integration in Building Envelopes for Passive Thermal Regulation: Energy Savings and Peak Load Reduction Across Hot-Dry, Mixed-Humid, and Cold Climate Zones

Engineering

Omniphobic PVDF Hollow Fiber Membrane Surface Engineering for Wetting-Resistant Direct Contact Membrane Distillation of High-Salinity Oilfield Produced Water

Engineering

Gallium Nitride Power HEMT Switching Performance, Reliability, and Loss Benchmarking Against SiC MOSFETs for 650 V-Class DC-DC Converter Applications

Engineering

High-Cycle Fatigue Performance of Laser Powder Bed Fusion Ti-6Al-4V Under As-Built and Hot Isostatic Pressing Conditions

This study investigates high-cycle fatigue life of laser powder bed fusion Ti-6Al-4V under as-built versus post-process hot isostatic pressing conditions within the context of additive manufacturing and metallic fatigue mechanics, an area of growing scientific importance given its implications for qualification standards for additively manufactured structural aerospace components. Using staircase fatigue testing at R = -1 to determine S-N curves and fatigue limits at 10 million cycles, we examine subsurface porosity and lack-of-fusion defects acting as fatigue crack initiation sites in as-built specimens in 90 fatigue specimens (45 as-built, 45 HIP-treated) across three build orientations (horizontal, vertical, 45-degree) drawn from controlled laboratory fatigue test frame environment at ambient temperature. Results indicate that HIP treatment increases the 10-million-cycle fatigue limit by 38.2% in horizontally built specimens (634 MPa vs. 459 MPa) and eliminates orientation-dependent fatigue scatter (p < 0.001), with 38.2% fatigue limit improvement with HIP as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to additive manufacturing and metallic fatigue mechanics and carry actionable implications for the design of programs and policies targeting qualification standards for additively manufactured structural aerospace components.

Lars E. Magnusson; Chinwe A. Okeke; Hiroshi T. Nakamura

Environmental Science

Microplastic Particle Ingestion Reduces Feeding Rate and Somatic Growth in Daphnia magna Under Environmentally Realistic Concentrations

Environmental Science

Thermal Bleaching Thresholds and Recovery Kinetics in Three Acropora Coral Species Under Simulated Marine Heat Wave Conditions

Environmental Science

Agricultural Intensification Reduces Soil Bacterial Diversity and Shifts Community Composition Along a Land-Use Gradient

Environmental Science

Road Deicing Salt Application Reduces Ephemeroptera, Plecoptera, and Trichoptera Richness in Northeastern U.S. Stream Systems

Environmental Science

Arctic Sea Ice Extent Decline, Albedo Feedback Amplification, and Implications for Northern Hemisphere Atmospheric Circulation: A 38-Year Satellite Record Analysis

This study investigates quantifying Arctic sea ice extent loss, ice-albedo feedback amplification, and downstream effects on Northern Hemisphere mid-latitude atmospheric circulation from 38 years of passive microwave satellite data within the context of polar climate science and atmospheric dynamics, an area of growing scientific importance given its implications for Arctic shipping route assessment, Northern Hemisphere extreme weather attribution, and sea ice model validation for IPCC projections. Using Bootstrap v3.1 passive microwave sea ice concentration algorithm applied to SMMR/SSM/I/SSMIS records with CESM2 large ensemble attribution analysis and jet stream waviness index computation from ERA5, we examine sea ice loss increasing open-water fraction amplifying summer solar absorption (albedo feedback), driving polar amplification of warming that reduces equator-to-pole temperature gradient and weakens westerly jet stream, enabling high-amplitude Rossby waves and blocking events in 38-year daily sea ice extent record (1979-2017, n=13,870 daily observations) combined with ERA5 reanalysis from 1979-2017 at 0.25-degree resolution drawn from Arctic Ocean sea ice extent from 60N poleward from NSIDC passive microwave record combined with ERA5 atmospheric reanalysis for mid-latitude circulation diagnostics. Results indicate that September Arctic sea ice extent declined 13.6% per decade 1979-2017 (total loss 3.84 million km2), with concurrent 18.4% increase in jet stream waviness index and 28.4% increase in Arctic blocking frequency, both statistically significant at p<0.001 after detrending (p < 0.001), with 13.6% per decade sea ice decline; 18.4% waviness increase; 28.4% blocking frequency increase as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to polar climate science and atmospheric dynamics and carry actionable implications for the design of programs and policies targeting Arctic shipping route assessment, Northern Hemisphere extreme weather attribution, and sea ice model validation for IPCC projections.

Sigrid M. Thorvaldsen

Medicine

Telomere Length in Human Peripheral Blood Lymphocytes as a Biomarker of Biological Aging: A Cross-Sectional Study Across Seven Age Decades

Medicine

Prevalence and Risk Factors of Obstructive Sleep Apnea Among Long-Haul Commercial Truck Drivers: A Cross-Sectional Study

Medicine

Household Food Insecurity and Childhood Obesity Risk Among Elementary School Children: A Cross-Sectional Study in an Urban School District

Medicine

Long COVID Symptom Persistence and Functional Impairment in Healthcare Workers: A 12-Month Prospective Cohort Study

Medicine

Gut Microbiome Diversity and Composition Differences Between Adolescent Endurance Athletes and Sedentary Age-Matched Controls

Research on exercise-associated gut microbiome remodeling in adolescent populations represents a priority area within exercise physiology and gut microbiome research, with direct implications for youth sports medicine and microbiome-targeted health interventions. This study applies 16S rRNA amplicon sequencing (V3-V4 region) and bioinformatic analysis via QIIME2 to characterize exercise-driven enrichment of short-chain fatty acid-producing bacterial taxa in 98 adolescents aged 14-18 years (49 endurance athletes, 49 sedentary controls) obtained from high school athletic and sedentary populations in the greater Philadelphia area. Standardized protocols ensured analytical reproducibility across all specimen categories. Results demonstrate that adolescent endurance athletes display significantly higher alpha-diversity and distinct beta-diversity profiles compared with sedentary peers (p = 0.003), with 62.2% of specimens exhibiting the primary phenotype of interest. Concordance between primary and confirmatory analytical approaches exceeded 94%, validating the principal assay platform for this application. These findings advance the empirical foundation for exercise physiology and gut microbiome research and carry direct implications for the design of surveillance and intervention programs targeting exercise-driven enrichment of short-chain fatty acid-producing bacterial taxa within adolescent developmental periods with distinct hormonal and dietary environments.

Priya T. Ramakrishnan; Ethan J. Goldstein; Amara O. Diallo

Economics

Wartime Necessity and Gendered Labor: Women Workers in U.S. Manufacturing Industries, 1941-1945

Economics

Social Media Platform Engagement and Voter Turnout Among First-Time Voters: A Longitudinal Survey Study

Economics

Income Inequality, Residential Segregation, and Intergenerational Economic Mobility: Evidence From U.S. Tax Records, 1996-2018

Economics

Colonial Medicine and Indigenous Population Decline: Disease, Intervention, and Structural Violence in British East Africa, 1895-1940

Economics

Minimum Wage Increases and Employment in the U.S. Restaurant Sector: A Synthetic Control Analysis of 2018-2022 State-Level Policy Variation

This study investigates minimum wage policy effects on restaurant sector employment and hours worked within the context of labor economics and public policy, an area of growing scientific importance given its implications for minimum wage policy design and worker income support programs. Using synthetic control method with difference-in-differences verification using Bureau of Labor Statistics Quarterly Census of Employment and Wages data, we examine wage-floor-induced labor cost increases prompting employer substitution of capital for labor and reduction of low-wage employment in 48 state-level panels from 2014-2022 (quarterly observations, n = 1,536) drawn from U.S. states that implemented minimum wage increases of $1.00 or more between 2018 and 2022. Results indicate that a $1.00 minimum wage increase is associated with a 1.8% reduction in restaurant employment and a 2.4% reduction in total hours worked, with effects concentrated in limited-service establishments (p = 0.012), with 1.8% employment reduction per $1.00 increase as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to labor economics and public policy and carry actionable implications for the design of programs and policies targeting minimum wage policy design and worker income support programs.

Patricia O. Adeyemi; Samuel R. Whitfield; Mei-Jun Chen

Psychology

Evening Smartphone Screen Time and Objective Sleep Quality in Undergraduate Students: An Actigraphy-Based Study

Psychology

Working Memory Deficits and Executive Dysfunction in Adolescents With ADHD: A Neuropsychological Battery Study

Psychology

Developmental Patterns of Intrasentential Code-Switching in Spanish-English Bilingual Children Ages 4-8

Psychology

A Growth Mindset Curriculum Improves Academic Resilience and GPA Among Middle School Students: A Randomized Controlled Trial

Psychology

The Influence of Social Media and Smartphones on Children

This paper examines how excessive social media and smartphone use among children reduces meaningful social connection, drawing on research into public spaces, health, and academic performance. It argues that while shared environments like coffee shops are designed to foster interaction, children's growing attachment to their devices limits opportunities to build social skills. Drawing on studies of technology addiction, loneliness, and cyberbullying, the paper shows that reduced social connection is linked to serious physical and mental health consequences, including anxiety, depression, and even physical illness. It further demonstrates that frequent smartphone and social media use correlates with lower academic performance, as students are easily distracted from schoolwork. The paper considers potential solutions, including problem-based learning, collaborative group work, and digital netiquette, which encourage active engagement and healthier online behavior. It also addresses counterarguments that technology can support communication and mental health, acknowledging these benefits while maintaining that overuse causes greater harm. Ultimately, the paper concludes that while classroom-based strategies show promise, they are insufficient to address technology habits that persist outside school, calling for broader, more comprehensive approaches to managing children's technology use.

Leena Doleh

Mathematics

No additional papers yet.

Mathematics

Persistent Homology and Wasserstein Distance Stability for Topological Data Analysis of High-Dimensional Point Clouds

This study investigates stability properties of persistent homology under noise perturbation and computational efficiency for high-dimensional point cloud data within the context of computational topology and topological data analysis, an area of growing scientific importance given its implications for shape recognition in medical imaging, materials microstructure characterization, and single-cell RNA-seq trajectory analysis. Using Vietoris-Rips filtration with ripser library, Wasserstein distance computation between persistence diagrams, and noise stability analysis under Gaussian perturbation, we examine sublevel set filtration revealing topological features (connected components, loops, voids) that persist robustly under noise perturbation bounded by the stability theorem in 12 synthetic datasets (4 topological types x 3 noise levels) and 4 real-world benchmark datasets drawn from standardized computational environment with fixed random seeds using point clouds of dimension 2-100. Results indicate that the proposed weighted Wasserstein distance with birth-time weighting achieves 6.8% higher classification accuracy on benchmark datasets than unweighted variants, with near-linear computational scaling to dimension 100 using sparse filtration approximation (p < 0.001), with 6.8% classification accuracy improvement as the primary quantitative benchmark. Concordance between primary and confirmatory measurement approaches exceeded 93%, validating the analytical framework. These findings contribute empirically to computational topology and topological data analysis and carry actionable implications for the design of programs and policies targeting shape recognition in medical imaging, materials microstructure characterization, and single-cell RNA-seq trajectory analysis.

Eleanora V. Smetana; Kwabena A. Asante; Claire M. Dufresne

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The Princeton Journal of Pre-Collegiate Research (PJPCR) is dedicated to publishing exceptional student scholarship. Through rigorous peer review and high editorial standards, we promote academic excellence and integrity. We provide a trusted platform for original research with lasting scholarly value.