Conducted in terms: 2024/25Z, 2025/26Z, 2026/27Z

Erasmus code: 13.3

ISCED code: 0531

ECTS credits: 4

Language: Polish

Organized by: Faculty of Chemistry

Fluorophore design 0600-S2-O-PF

## Lectures – 5 hours

The lectures have an integrative and problem-based character. They assume prior knowledge of the fundamentals of molecular spectroscopy, organic chemistry, physical chemistry and quantum chemistry. Their purpose is to organise this knowledge and demonstrate how these areas jointly explain photophysical properties and the principles of fluorophore design.

### 1. Electronic States and Competing Photophysical Processes

The Jablonski diagram as a model organising the processes that follow the absorption of radiation. Singlet and triplet states, electronic configuration and spin multiplicity. Hund’s rule, the typical relationship between the energies of the S₁ and T₁ states, and the conditions under which the simple picture based on exchange energy may be insufficient or the ordering of the lowest electronic states may be inverted.

Absorption, vibrational relaxation, internal conversion, fluorescence, intersystem crossing and phosphorescence. The timescales of these processes and their competition following molecular excitation.

### 2. Absorption and Emission Spectra and the Kinetics of Photophysical Processes

Selection rules in UV–Vis spectroscopy, the Franck–Condon principle and Kasha’s rule. Vertical absorption and emission, excited-state geometry relaxation, adiabatic transition energy and 0–0 transition energy. The physical origins of the Stokes shift.

The relationship between fluorescence quantum yield, excited-state lifetime and the rate constants of radiative and non-radiative processes. In a simplified kinetic scheme:

\[
\Phi_\mathrm{F}=\frac{k_\mathrm{F}}{k_\mathrm{F}+\sum k_\mathrm{nr}},
\qquad
\tau=\frac{1}{k_\mathrm{F}+\sum k_\mathrm{nr}}.
\]

The significance of competition between fluorescence, internal conversion and intersystem crossing. The energy-gap law and its consequences for fluorophore design, particularly for fluorophores emitting in the red and near-infrared regions.

### 3. From a π-Electron System to Colour and Emission

Principles governing the colour of organic compounds and the relationship between the structure of a π-electron system and its excitation energy. Models of increasing complexity: the particle-in-a-box model, the Hückel method and TD-DFT. The ranges of applicability of these models and the types of information they can provide.

The influence of conjugation length, planarity, structural rigidity, donor and acceptor groups, and intramolecular charge transfer on absorption, emission and transition intensity. The significance of the S₁–T₁ energy difference, spin–orbit coupling and the availability of non-radiative deactivation pathways.

### 4. Effects of the Environment and Molecular Organisation

The influence of solvent polarity and polarisability and of specific interactions on the energies and character of excited states. Absorption and emission solvatochromism. Absorption and emission in solution and the limitations of representing the environment using a continuum solvent model.

The influence of the solid state, intermolecular interactions and aggregation on emission properties. Aggregation-caused fluorescence quenching and aggregation-induced emission. The significance of restricting intramolecular motion and controlling molecular organisation.

### 5. Fluorophore Design Strategies and Applications

Fluorophore design as the search for a compromise between spectral position, absorption intensity, quantum yield, lifetime, Stokes shift, photostability, solubility and sensitivity to the environment.

Requirements for fluorophores used in biological imaging, photodynamic therapy, OLEDs and DSSCs. Fundamental mechanisms of dual emission, phosphorescence, thermally activated delayed fluorescence, two-photon absorption, photon upconversion and selected nonlinear optical phenomena. These phenomena illustrate that a structural modification may simultaneously affect several competing processes and does not necessarily result in an unambiguous improvement in all fluorophore parameters.

## Computer Laboratory Classes – 40 hours

### Block 1. Introduction to Jupyter Notebook – 4 hours

Fundamentals of working in the Jupyter environment. Notebook structure, execution and organisation of code cells, data import, basic operations on tabular data, and preparation of plots. Integration of code, results, visualisations and scientific commentary into a reproducible report. Documentation of data sources, code provenance, software versions and the use of generative artificial intelligence.

### Block 2. Project I: Computational Modelling of Absorption and Emission – 16 hours

Students investigate the absorption and emission properties of a selected organic molecule. They prepare models of the ground state and a selected excited state and perform calculations in solution using a protocol that accounts for equilibrium and nonequilibrium solvent response.

The following quantities are determined and analysed:

- vertical absorption energy;
- vertical emission energy;
- adiabatic energy difference between the state minima;
- 0–0 transition energy;
- Stokes shift;
- oscillator strength and the character of the electronic transition under investigation.

Students verify the convergence of the calculations and determine whether the same electronic state is being compared across consecutive computational variants. The character of the state is assessed on the basis of its energy, transition intensity, dominant configurations or transition orbitals.

The project comprises three series of controlled comparisons:

- comparison of four density functionals using the same basis set and solvent;
- comparison of four basis sets using the selected density functional and the same solvent;
- comparison of four solvents with different dielectric properties, represented using PCM, with the density functional and basis set kept unchanged.

Each student performs an assigned computational variant. The group follows a common protocol and prepares a comparable set of results. Each student prepares an individual notebook presenting their own calculations in the context of the results obtained by the entire group.

The results are compared with relevant experimental data and current literature on the computational modelling of photophysical properties. Students evaluate the influence of the density functional, basis set and solvent model, distinguish between the different physical meanings of vertical, adiabatic and 0–0 transition energies, and analyse possible sources of error and error cancellation. Conclusions are limited to the system investigated and the range of methods tested.

### Block 3. Project II: Molecular Descriptors and Principal Component Analysis – 10 hours

Students receive a standardised dataset comprising computational results obtained using the same method, basis set and solvent model for a series of 120 molecules with published experimental properties. The dataset includes computational results, experimental data, and information enabling the structural and electronic characteristics of the molecules to be determined.

Students:

- verify the completeness and consistency of the data and examine its range of variation;
- select molecular descriptors potentially associated with absorption and emission;
- distinguish between structural descriptors, electronic descriptors and descriptors related to measurement conditions;
- analyse variable distributions, correlations and outliers;
- standardise the data before multivariate analysis;
- perform principal component analysis;
- interpret eigenvalues, explained variance, loadings and scores for individual observations;
- identify groups of similar molecules and the characteristics responsible for their positions in principal-component space;
- relate the results to experimental absorption and emission maxima and other available photophysical properties.

The purpose of the project is not merely to perform PCA, but to provide a chemical interpretation of the relationships between molecular descriptors and fluorophore properties. Students distinguish correlation from causation and assess whether the selected descriptors provide information relevant to the description of the dataset.

Each student prepares an individual notebook documenting their own selection of descriptors and analytical reasoning and compares their conclusions with those reached by other members of the group.

### Block 4. Project III: Introduction to Machine Learning for Predicting Photophysical Properties – 2–3 Laboratory Sessions

The purpose of the project is to introduce fundamental machine-learning concepts and demonstrate how molecular descriptors can be used to predict a selected photophysical property. The project does not cover the complete process of developing and optimising a predictive model.

Students use the dataset of 120 molecules and the descriptors analysed in Project II. Using a partially prepared notebook, they:

- select one target property, such as an absorption or emission maximum;
- use a predefined set of molecular descriptors;
- split the data into training and test sets according to an established procedure;
- fit a simple linear model as a baseline;
- apply one selected simple nonlinear model;
- compare predicted and experimental values;
- evaluate the models using basic metrics, such as mean absolute error and the coefficient of determination;
- analyse a plot of predicted versus experimental values and examine the largest prediction errors;
- assess whether the available descriptors and dataset size permit useful prediction of the property under investigation.

The following fundamental concepts are introduced: target variable, input features, training and test sets, baseline model, prediction error, overfitting and data leakage. These concepts are discussed to the extent necessary for the correct performance and interpretation of the exercise.

Each student prepares an individual notebook documenting the analysis, the results obtained with both models, comparison with experiment, and a justified assessment of model quality and limitations. A finding that the available data do not permit reliable prediction is considered a valid result, provided that it is properly documented and interpreted.

## General Principles Applicable to the Computer Laboratory

The following elements are assessed in all three projects: appropriate formulation of the problem, completeness of documentation, reproducibility of the analysis, critical interpretation of results, links to experimental data and scientific literature, and recognition of the limitations of the approach used.

Generative artificial intelligence tools may support the preparation or explanation of code, identification of possible analytical approaches, and editing of the report. Students are required to disclose the extent to which such tools were used, verify generated code and content, and retain responsibility for the correctness of the analysis and the conclusions presented.

Total student workload

### Total Student Workload #### Classes requiring direct participation of the instructor – 52 hours - lectures – 5 hours; - computer laboratory classes – 40 hours; - individual and group consultations, including feedback on notebooks and support in project implementation – 5 hours; - final assessment: a problem-based examination in the traditional pathway or a presentation, discussion and final review interview in the reflective, process-based pathway – 2 hours. #### Student’s independent work – 48 hours - preparation for classes and learning the fundamentals of working in the Jupyter environment – 5 hours; - analysis of absorption and emission calculation results, comparison with group results and experimental data, and preparation of the Project I notebook – 15 hours; - selection and analysis of molecular descriptors, principal component analysis and preparation of the Project II notebook – 12 hours; - development, validation and interpretation of machine-learning models and preparation of the Project III notebook – 12 hours; - preparation for the problem-based examination in the traditional pathway or preparation of the presentation, final self-assessment and review interview in the reflective, process-based pathway – 4 hours. **Total student workload: 100 hours.** **Number of ECTS credits: 4.**

Learning outcomes - knowledge

**W1.** The student explains the fundamental photophysical processes occurring following electronic excitation and the relationships between the structure of a fluorophore, its environment, and its absorption and emission properties. **K_W01, K_W02** **W2.** The student explains the theoretical foundations, capabilities and limitations of quantum-chemical methods used to describe excited states and calculate absorption and emission properties. The student distinguishes between vertical absorption and emission, adiabatic transition energy and 0–0 transition energy, as well as between equilibrium and nonequilibrium descriptions of the solvent response. **K_W08** **W3.** The student explains the principles of designing a controlled comparison of computational methods and assessing the agreement between predicted photophysical properties and experimental data. The student understands the importance of data quality, the selection of comparable observables, error metrics, uncertainty analysis and possible error cancellation. **K_W08, K_W09** **W4.** The student explains the basic concept of using molecular descriptors to predict photophysical properties and understands the significance of data splitting, prediction error and overfitting. **K_W09**

Learning outcomes - skills

### Learning Outcomes – Skills **U1.** The student independently prepares, performs and documents electronic-structure calculations for the ground state and a selected excited state in solution. The student determines vertical absorption and emission energies, the adiabatic energy difference and the 0–0 transition energy, while verifying the correctness and convergence of the calculations and the character of the electronic state under investigation. **K_U10** **U2.** The student designs and performs a controlled comparison of density functionals, basis sets and solvent models by varying one factor while keeping the remaining conditions unchanged. The student compares their own results with those obtained by the group and with experimental and literature data, and critically evaluates errors, limitations and possible error cancellation. **K_U10, K_U11, K_U13** **U3.** The student selects, organises and standardises molecular descriptors characterising a series of fluorophores and performs principal component analysis. The student interprets the PCA results to identify relationships between structural and electronic factors and absorption and emission properties. **K_U01, K_U11** **U4.** The student uses a predefined workflow to fit and evaluate simple models predicting a selected photophysical property, compares the predictions with experimental data, and formulates conclusions concerning the capabilities and limitations of the models. **K_U01, K_U11** **U5.** The student formulates a research problem, identifies relevant experimental data and current scientific literature, and prepares a reproducible report in the form of a Jupyter notebook integrating source data, code, results, visualisations, interpretation and references. The student presents and justifies the conclusions derived from the analysis. **K_U08** **U6.** The student critically evaluates content, code and interpretations generated using generative artificial intelligence tools, verifies them against computational results, source data and scientific literature, and distinguishes the use of a tool from their own reasoning and responsibility for the conclusions presented. **K_U01, K_U08**

Learning outcomes - social competencies

### Learning Outcomes – Social Competences **K1.** The student recognises the limitations of their own knowledge of photophysics, methods for describing excited states and data analysis, and uses feedback to plan the further development of their competences. **K_K01** **K2.** The student collaborates within a team on a comparative computational project: follows the jointly agreed protocol, shares results and metadata on time, verifies their comparability, and participates in formulating joint conclusions, while remaining responsible for their own calculations and individual notebook. **K_K02** **K3.** The student recognises the potential practical applications of fluorophores and predictive models, as well as the limitations and risks associated with transferring results obtained from a small dataset to the design of new photoactive materials. **K_K03** **K4.** The student adheres to the principles of research integrity: documents the provenance of data, code and results; identifies the software used and its version; discloses the use of generative artificial intelligence; and reports unexpected results, including those that do not support the initial hypothesis. **K_K06** **K5.** The student formulates and presents well-founded opinions on the quality of calculations, the relevance of molecular descriptors and the reliability of predictive models, basing their arguments on verifiable data and distinguishing between results, interpretations and predictions. **K_K07**

Course coordinators

Anna Kaczmarek-Kędziera

Teaching methods

#### Lectures **Problem-based lectures** – presentation of photophysical processes and fluorophore design strategies through questions concerning the relationships between molecular structure, environment, and absorption and emission properties. **Case studies** – analysis of selected fluorophores and photoactive materials in terms of their structures, excited-state deactivation mechanisms, and the requirements associated with biomedical and technological applications. **Analysis of spectroscopic data** – interpretation of absorption and emission spectra, Stokes shifts, quantum yields, and other quantities describing photophysical properties. **Guided discussion** – comparison of possible interpretations of results, identification of model limitations, and distinction between experimental observables and the corresponding computational quantities. #### Computer Laboratory Classes **Instruction and practical exercises in the Jupyter environment** – introduction to notebook structure, code execution, data processing, preparation of tables and plots, and integration of code, results and interpretation into a reproducible report. **Computational case study** – calculation of the absorption and emission properties of a selected fluorophore in solution, including the analysis of vertical and adiabatic energies and the 0–0 transition energy. **Controlled computational experiment** – investigation of the influence of the density functional, basis set and solvent model on the results by varying one factor while keeping the remaining conditions unchanged. **Project-based learning** – implementation of three consecutive projects covering the calculation of photophysical properties, analysis of molecular descriptors and PCA, and the development and validation of simple machine-learning models. **Comparative analysis of results** – comparison of the student’s own results with those obtained by other group members and with experimental data and current scientific literature, including identification of the sources of discrepancies and possible error cancellation. **Multivariate data analysis** – selection and standardisation of molecular descriptors, correlation analysis, and performance and interpretation of principal component analysis for a series of fluorophores. **Predictive modelling** – data preparation and the development, validation and comparison of simple machine-learning models predicting selected photophysical properties, including assessment of their range of applicability. **Collaborative learning** – completion of individually assigned computational variants within a common protocol, exchange of data, verification of their comparability, and collaborative formulation of conclusions. Each student remains responsible for their own calculations and individual notebook. **Feedback and iterative improvement** – discussion of notebooks and interim results and use of feedback to improve procedures, analyses and the justification of conclusions. **Critical use of generative artificial intelligence** – verification of generated code, explanations and interpretations against source data, computational results and scientific literature, together with documentation of the extent to which AI tools were used. **Presentation and defence of results in the reflective, process-based pathway** – presentation of the work undertaken, the most important results, difficulties encountered and development of the student’s competences, followed by responses to questions during the final review interview.

Observation/demonstration teaching methods

- display

Expository teaching methods

- discussion
- informative (conventional) lecture
- description
- participatory lecture
- narration
- problem-based lecture

Exploratory teaching methods

- classic problem-solving
- experimental
- seminar
- situational
- project work
- laboratory
- case study
- brainstorming
- practical
- expert panels

Online teaching methods

- cooperation-based methods
- exchange and discussion methods
- methods developing reflexive thinking
- content-presentation-oriented methods

Prerequisites

Organic chemistry, not required, but helpful will be also Quantum chemistry and/or Theoretical chemistry

Assessment criteria

### Assessment Methods and Criteria

Assessment of the achievement of the learning outcomes is based on multiple complementary forms of evidence collected throughout the semester. Both the results obtained and the correctness of the procedures, quality of documentation, critical interpretation of data, comparison with experiment, and ability to recognise the limitations of the methods used are assessed.

Students select one of two assessment pathways:

- the traditional pathway, comprising three project notebooks and a problem-based examination;
- the reflective, process-based pathway, comprising three project notebooks, a learning journal, a final presentation, self-assessment and a final review interview.

Both pathways verify the same learning outcomes and are subject to the same minimum requirements. They differ in how students document their knowledge, independence and development of competences.

## Evidence of Achievement of the Learning Outcomes

### 1. Project I Notebook – Absorption and Emission Modelling

The notebook documents the student’s own computational variant and presents it in the context of the results obtained by the entire group. It should include:

- a description of the system investigated and the computational protocol;
- the method, basis set, solvent model and software version;
- documentation of the ground-state and excited-state geometries;
- vertical absorption and emission energies, the adiabatic energy difference and the 0–0 transition energy;
- verification of convergence and of the character of the electronic state under investigation;
- a comparison of results obtained using different density functionals, basis sets and solvents;
- comparison with relevant experimental and literature data;
- analysis of errors, limitations and possible error cancellation;
- justified conclusions concerning the system investigated.

### 2. Project II Notebook – Molecular Descriptors and Principal Component Analysis

The notebook should document:

- verification of the completeness and consistency of the dataset;
- selection and chemical justification of the descriptors;
- data preparation and standardisation;
- analysis of distributions, correlations and outliers;
- performance of principal component analysis;
- interpretation of explained variance, loadings and observation scores;
- identification of relationships between structural or electronic characteristics and photophysical properties;
- distinction between correlation and causation;
- comparison of the student’s own results with those obtained by other members of the group.

### 3. Project III Notebook – Introduction to Machine Learning

The notebook should include:

- an unambiguous definition of the target property;
- a description of the descriptors used and the data-splitting procedure;
- a simple linear model used as a baseline;
- one selected simple nonlinear model;
- comparison of predicted and experimental values;
- basic model performance metrics;
- analysis of the largest errors and outlying cases;
- identification of overfitting or data leakage;
- assessment of whether the available data permit useful prediction of the property under investigation;
- critical presentation of the model’s limitations.

Neither the highest possible coefficient of determination nor the complexity of the algorithm is assessed. Assessment focuses on the correctness of the procedure, understanding of its principles, honest evaluation of prediction quality, and chemical interpretation of the results. A properly justified finding that the model has insufficient predictive ability is considered a valid project outcome.

### 4. Collaborative Work

In each project, students follow a common protocol and use a comparable format for recording results. Each student is responsible for their own calculations and notebook and, at the same time:

- provides the group with results and required metadata on time;
- verifies the consistency of units and notation;
- participates in identifying errors and discrepancies;
- uses group results to perform comparative analysis;
- participates in formulating joint conclusions.

### 5. Problem-Based Examination in the Traditional Pathway

The examination covers the interpretation of photophysical processes, relationships between fluorophore structure and properties, distinction between computational quantities and experimental observables, and critical analysis of the results presented.

The examination is not limited to recalling definitions. The tasks require students to apply their knowledge to the analysis of a Jablonski diagram, spectra, rate constants, quantum yields, computational results or a simple data model.

### 6. Final Project Presentation in the Reflective, Process-Based Pathway

The final presentation takes the form of a design problem:

**Which of the molecules analysed is the most promising candidate for a selected application, and what evidence supports recommending it for further investigation?**

The student selects one application, for example:

- an OLED emitter;
- a dye sensitiser for a DSSC;
- a photosensitiser for photodynamic therapy;
- a fluorescent probe for bioimaging;
- a material exhibiting aggregation-induced emission;
- a system intended for two-photon absorption or photon upconversion.

The student then selects one molecule or compares a small group of candidates from among the compounds analysed during the course.

### Components of the Presentation

#### 1. Definition of the Design Problem

The student defines the function that the fluorophore is expected to perform and, on the basis of current scientific literature, formulates criteria relevant to the selected application.

For example:

- for an OLED: emission range, luminescence efficiency, the energies and character of excited states, ΔE_ST, susceptibility to non-radiative processes, stability, and behaviour in the condensed phase;
- for a DSSC: absorption range and intensity, energy-level alignment, potential for electron transfer, presence of anchoring groups, photochemical stability, and tendency to aggregate;
- for PDT: absorption in a favourable spectral range, potential for efficient intersystem crossing, triplet-state properties, ability to generate reactive oxygen species, photostability, solubility, and potential to function in a biological environment.

The student distinguishes criteria that can be assessed using the data collected during the course from properties requiring additional calculations or experimental verification.

#### 2. Candidate Selection and Analysis

The student:

- presents the molecules considered and justifies their preliminary selection;
- uses computational results, molecular descriptor analysis, predictive model results, and experimental and literature data;
- compares the properties of the candidates with the adopted criteria;
- identifies results supporting the selection of a particular molecule as well as evidence that challenges or limits that selection;
- accounts for the reliability of the methods used, prediction uncertainty and possible error cancellation;
- distinguishes data obtained directly during the course from information derived from the literature and from their own hypotheses.

The results may be presented as a decision matrix containing:

| Criterion | Available result | Expected range or direction of change | Data source | Reliability | Missing information |
|---|---:|---|---|---|---|

#### 3. Recommendation and Its Limitations

The student formulates a final recommendation, specifying:

- which candidate is the most promising;
- which results provide the strongest justification for the selection;
- which assumptions have the greatest influence on the recommendation;
- which data are insufficient or ambiguous;
- which alternative explanations should be considered;
- which further calculations or experiments would be required to confirm the suitability of the compound.

The conclusion should take the form of a recommendation for further verification rather than a categorical statement that the molecule will perform effectively in a real material, device or biological system.

A justified conclusion that none of the molecules analysed satisfies a sufficient number of criteria, or that the available data do not permit a reliable selection, is also considered a valid and complete project outcome.

#### 4. Reflection on the Learning Process

In the final part of the presentation, the student explains:

- which portfolio elements were used as evidence and why they were selected;
- how their approach to evaluating candidates changed during the projects;
- which results, errors, group comparisons or feedback led them to revise their initial interpretation;
- which questions they are not yet able to resolve reliably;
- which competences they should develop further;
- what their individual contribution was and how they used group results and generative AI tools.

### Discussion and Feedback

Following the presentation, the student answers questions from the group and the instructor and defends their recommendation. A designated reviewer identifies:

- the strongest evidence supporting the recommendation;
- the weakest assumption or a piece of missing information;
- an alternative candidate or interpretation that should be considered.

The other participants may use an interactive tool to identify one strength of the argument and one question or suggestion for improvement. The feedback is formative and does not constitute a vote on the student’s grade.

### Presentation Assessment Criteria

- correct identification of the requirements associated with the selected application – 20%;
- selection and critical use of computational, experimental and literature evidence – 25%;
- comparison of candidates and consideration of uncertainty and missing data – 20%;
- coherence of the recommendation and appropriateness of the proposed further verification – 15%;
- reflection on the student’s own learning process – 10%;
- clarity of presentation, responses to questions and defence of the position taken – 10%.

A high grade requires not only selecting a candidate but also recognising the limits of the available evidence. Poor predictive performance of a model or rejection of all candidates does not lower the grade if the student correctly analyses the reasons and formulates justified conclusions.

### 7. Learning Journal and Final Self-Assessment

In the reflective, process-based pathway, the student documents the decisions made, errors encountered, approaches to problem-solving, use of feedback, and changes introduced in consecutive projects.

The final self-assessment must refer to specific evidence contained in the notebooks, presentation and journal. It provides the starting point for the final review interview but does not independently determine the grade. The final grade is determined by the instructor on the basis of the evidence presented and the previously published criteria.

### 8. Quizzes and Extension Tasks

Quizzes support independent monitoring of the acquisition of fundamental concepts. Extension tasks enable students to explore a selected topic in greater depth or apply a method to a new problem.

These activities are optional. Failure to complete them does not lower the grade and cannot prevent a student from receiving the highest grade.

## Alignment of Learning Outcomes with Assessment Methods

| Learning outcome | Principal evidence of achievement |
|---|---|
| W1 | examination or presentation and final review interview; interpretation of results in the notebooks |
| W2 | Project I notebook; examination or presentation |
| W3 | Project I and Project II notebooks |
| W4 | Project III notebook |
| U1 | Project I notebook and the associated input and output files |
| U2 | comparative analysis of results in the Project I notebook |
| U3 | Project II notebook |
| U4 | Project III notebook |
| U5 | all three notebooks, references and documentation of the analytical process |
| U6 | declaration of AI use and documented verification of generated code and content |
| K1 | learning journal and self-assessment or documentation of revisions and limitations in the notebooks |
| K2 | timely sharing of results, use of the common protocol and use of group data |
| K3 | critical assessment of the transferability of conclusions and predictions |
| K4 | traceability of data, code, calculations, sources and AI use |
| K5 | conclusions presented in the notebooks, examination, or presentation and discussion |

## Common Notebook Assessment Criteria

Each notebook is assessed according to the following criteria:

- correct formulation of the problem and appropriateness of the procedure – 20%;
- correctness, completeness and reproducibility of the documentation – 20%;
- quality of data analysis and visualisation of results – 20%;
- chemical and photophysical interpretation and comparison with experiment – 20%;
- critical assessment of limitations, use of scientific literature and integrity of documentation – 10%;
- timely sharing and comparability of results and contribution to the collaborative work of the group – 10%.

Advanced programming ability is not assessed in itself. The code should be correct, readable and sufficient to reproduce the analysis. The grade is determined primarily by the correctness of the reasoning, quality of the analysis and validity of the conclusions.

A notebook whose tables, plots and conclusions cannot be traced to the source data, code and documented procedure does not constitute sufficient evidence of achievement of the learning outcomes.

## Traditional Pathway

The final grade is determined on the basis of:

- Project I notebook – 25%;
- Project II notebook – 20%;
- Project III notebook – 15%;
- problem-based examination – 40%.

The requirements for passing the course are:

- submission of all three notebooks;
- achievement of at least the basic level in each project;
- obtaining at least 51% of the examination points;
- achievement of at least the basic level in subject knowledge and analysis of results.

The following grading scale applies:

| Grade | Result |
|---|---:|
| 5.0 | 81–100% |
| 4.5 | 76–80% |
| 4.0 | 66–75% |
| 3.5 | 61–65% |
| 3.0 | 51–60% |
| 2.0 | 0–50% |

## Reflective, Process-Based Pathway

The requirements for passing the course are:

- submission of all three notebooks at a level demonstrating achievement of the learning outcomes;
- systematic maintenance of the learning journal;
- preparation and delivery of the final presentation;
- preparation of a self-assessment supported by examples of the student’s own work;
- participation in the final review interview.

The final grade is not an arithmetic mean of component grades. It results from a holistic assessment of the student’s achievement profile and the collected evidence of achievement of the learning outcomes.

## Levels of Achievement of the Learning Outcomes

**Basic level – grade 3.0:** The student recognises fundamental photophysical processes, correctly performs tasks according to the provided protocol, prepares the required notebooks, and formulates generally correct conclusions. The student can compare results with experiment and identify the principal limitations but requires support when analysing more complex problems.

**Good level – grade 4.0:** The student independently performs and documents the analysis, correctly compares methods and results, uses feedback, and formulates well-justified conclusions. The student recognises the principal sources of uncertainty, actively participates in collaborative analysis, and correctly interprets the results of PCA and a simple predictive model.

**Very good level – grade 5.0:** The student integrates knowledge of photophysics, computational chemistry and data analysis, critically compares alternative interpretations, recognises error cancellation and data limitations, and proposes justified approaches to further verification. The student formulates precise conclusions, contributes responsibly to the collaborative group analysis, and is able to defend the position presented.

Grades 3.5 and 4.5 correspond to intermediate levels between the descriptions applicable to the adjacent grades.

## Requirements for Passing the Course

A passing grade requires achievement of at least the basic level in:

- understanding photophysical processes;
- correctly performing and documenting the analysis;
- relating computational quantities to experimental data;
- critically interpreting results;
- maintaining research integrity and accepting responsibility for the conclusions presented.

Failure to achieve any of these fundamental outcomes results in failure to pass the course, irrespective of the overall numerical result.

Work that does not meet the requirements concerning the traceability of data, authorship, documentation of code and calculations, or disclosure of generative AI use cannot constitute evidence of achievement until the missing information has been provided. Cases of research misconduct are handled in accordance with the applicable university regulations.

Practical placement

not applicable

Bibliography

### Core Reading

1. V. Balzani, P. Ceroni, A. Juris, *Photochemistry and Photophysics: Concepts, Research, Applications*, 2nd ed., Wiley-VCH, Weinheim 2024, ISBN 978-3-527-35276-0 – selected chapters on excited states, radiative and non-radiative processes, energy and electron transfer, luminescence, and applications of photochemistry and photophysics.

2. B. Valeur, M. N. Berberan-Santos, *Molecular Fluorescence: Principles and Applications*, 2nd ed., Wiley-VCH, Weinheim 2012, DOI: [10.1002/9783527650002](https://doi.org/10.1002/9783527650002) – selected chapters on absorption, emission, the kinetics of photophysical processes, quantum yields, fluorescence lifetimes, and environmental effects.

3. A. Kaczmarek-Kędziera, M. Ziegler-Borowska, D. Kędziera, *Chemia obliczeniowa w laboratorium organicznym* [*Computational Chemistry in the Organic Laboratory*], Wydawnictwo Naukowe UMK, Toruń 2014, ISBN 978-83-231-3114-4 – selected chapters on the preparation and critical analysis of quantum-chemical calculations.

4. L. Porter, D. Zingaro, *Programuj ze sztuczną inteligencją. Twórz kod w Pythonie z wykorzystaniem GitHub Copilot i ChatGPT* [*Learn AI-Assisted Python Programming: With GitHub Copilot and ChatGPT*], 2nd ed., Polish translation by R. Meryk, Helion, Gliwice 2025, ISBN 978-83-289-3048-3 – selected chapters on problem decomposition and on developing, testing, verifying and improving AI-generated code.

### Supplementary Reading

5. P. Bamfield, M. G. Hutchings, *Chromic Phenomena: Technological Applications of Colour Chemistry*, 3rd ed., Royal Society of Chemistry, Cambridge 2018, DOI: [10.1039/9781788012843](https://doi.org/10.1039/9781788012843) – selected chapters on photoluminescence, coloured materials and technological applications.

6. C. Adamo, D. Jacquemin, “The Calculations of Excited-State Properties with Time-Dependent Density Functional Theory”, *Chemical Society Reviews* 2013, 42, 845–856, DOI: [10.1039/C2CS35394F](https://doi.org/10.1039/C2CS35394F).

7. N. T. Maitra, “Double and Charge-Transfer Excitations in Time-Dependent Density Functional Theory”, *Annual Review of Physical Chemistry* 2022, 73, 117–140, DOI: [10.1146/annurev-physchem-082720-124933](https://doi.org/10.1146/annurev-physchem-082720-124933).

8. J. Westermayr, P. Marquetand, “Machine Learning for Electronically Excited States of Molecules”, *Chemical Reviews* 2021, 121, 9873–9926, DOI: [10.1021/acs.chemrev.0c00749](https://doi.org/10.1021/acs.chemrev.0c00749).

9. N. Artrith, K. T. Butler, F.-X. Coudert, S. Han, O. Isayev, A. Jain, A. Walsh, “Best Practices in Machine Learning for Chemistry”, *Nature Chemistry* 2021, 13, 505–508, DOI: [10.1038/s41557-021-00716-z](https://doi.org/10.1038/s41557-021-00716-z).

10. T. B. Ledur Kist, *Fluorescent Dye Labels and Stains: A Database of Photophysical Properties*, John Wiley & Sons, 2023, DOI: [10.1002/9781119835165](https://doi.org/10.1002/9781119835165).

11. Current user documentation for the computational software, Jupyter environment, and libraries used for data processing, visualisation, multivariate analysis and the development of simple predictive models, in the versions applicable to the given course cycle.

12. Selected current original research papers and review articles on fluorophore design and the requirements associated with the applications under investigation, particularly OLEDs, DSSCs, photodynamic therapy, bioimaging and fluorescent sensors. A complete list including authors, titles, journals, publication years and DOI identifiers is updated for each course cycle and provided to students before the final project begins.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of 0600-S2-O-PF in USOSweb