University of Turku Health Technology Materials Master’s Thesis Guide: MTEK0011, MTEK0020, 30 ECTS and UTUGradu

What this guide covers

This guide is for the University of Turku Health Technology Materials specialisation track in the current 2024-2027 Peppi curriculum. It focuses on the thesis route that is actually visible in programme 100045, not on a generic engineering thesis model. The key distinction is structural: the public programme page calls the 40 ECTS block “Thesis and Project”, but Peppi separates that block into MTEK0011 Master’s Thesis in Technology, Materials Engineering, 30 ECTS, TTDK1308 maturity examination, 0 ECTS, and MTEK0019 Capstone, 10 ECTS. The separate MTEK0020 thesis seminar is 5 ECTS in Common Advanced Studies. The guide also explains how biomaterials, imaging, modelling, biosignals, machine learning, sustainability, ethics and data protection affect thesis design.

Current programme object

The current Peppi object is MTEKMSCHEALTH2427, programme 100045. The degree is Master of Science (Technology), organised by the Faculty of Technology and the Department of Mechanical and Materials Engineering. The formal degree size is 120 ECTS over two years. Peppi may display a technical root range of 115-125 ECTS because the minor/thematic and other-studies modules contain ranges. That range should not be interpreted as changing the formal degree from 120 ECTS. When checking your own HOPS, use the current programme object and your enrolled curriculum period rather than copying an older study guide or another Materials Engineering track.

How the 120 ECTS degree is structured

The public structure is 20 ECTS joint materials engineering studies, 20 ECTS Health Technology Materials studies, 40 ECTS Thesis and Project, 20-25 ECTS minor or thematic studies, and 15-20 ECTS other studies. Peppi expresses the advanced-studies side as an 80 ECTS module containing 20 ECTS Common Advanced Studies, 20 ECTS Health Technology Materials, and 40 ECTS Thesis and Project. This is useful when planning thesis timing because the seminar is in the common 20 ECTS block while the thesis and Capstone are in the 40 ECTS block. Do not add or remove credits simply because the public page groups items differently from Peppi.

Read the 40 ECTS Thesis and Project block correctly

The 40 ECTS label is not a 40 ECTS thesis. The current Peppi category contains MTEK0011 at 30 ECTS, MTEK0019 Capstone at 10 ECTS, and TTDK1308 at 0 ECTS. This matters for workload, supervision, examination and claims about what is assessed. The Capstone is a team-oriented real-life project course with its own grading and deliverables. The thesis is the individually examined scientific report. Treating the two as one 40 ECTS thesis would distort both the curriculum and the expected evidence. Your thesis title page, project plan and schedule should therefore be based on the 30 ECTS MTEK0011 object.

Exact thesis course: MTEK0011

MTEK0011 Master’s Thesis in Technology, Materials Engineering is 30 ECTS, Advanced Studies, graded 0-5, and can be completed in Finnish or English. Its learning outcomes require practical and theoretical analysis of research problems using research methods and scientific literature. The thesis must demonstrate scientific work, research-method competence, knowledge of the field and scientific writing. The course also frames the thesis as a constructive solution proposal for a research challenge from a company, organisation or research group. A strong thesis therefore needs both an engineering or scientific problem and a defensible method for generating evidence about that problem.

The separate MTEK0020 thesis seminar

MTEK0020 Master’s Thesis in Technology Seminar, Materials Engineering is a separate 5 ECTS Advanced Studies course graded Pass/Fail. It sits in Common Advanced Studies, not inside MTEK0011. Students prepare a short research plan for their own thesis, analyse a completed thesis, present their own results near the end, listen to and discuss other presentations, and keep a learning diary. This course is important to the thesis process but it does not increase MTEK0011 from 30 to 35 ECTS. Plan seminar participation early because it can begin throughout the year when presentations are scheduled.

The separate MTEK0019 Capstone

MTEK0019 Capstone is a separate 10 ECTS Advanced Studies course, graded 0-5 and taught in English. It is designed for the final year and uses real-life challenges from companies, communities and research groups, typically in teams of five to eight students. It develops project planning, teamwork, stakeholder communication and solution implementation. Those are valuable capabilities, but Capstone evidence is not automatically thesis evidence. A working prototype from Capstone does not by itself establish biomaterial compatibility, clinical benefit, statistical validity or the scientific contribution required in MTEK0011. If your thesis grows from a Capstone topic, define a new research question and evidence plan.

TTDK1308 maturity examination

TTDK1308 is a 0 ECTS Degree Qualifying Examination for the Master’s Degree. In the normal master’s route, the thesis abstract or another suitable part such as a summary functions as the maturity test and demonstrates familiarity with the thesis field. The course also contains a conditional written-exam route depending on the student’s previous degree and Finnish or Swedish educational-language background. Because that condition is personal, do not assume that another student’s route applies to you. Confirm the maturity-test implementation from your own HOPS and current University instructions before final submission.

Finding a topic and supervisor

MTEK0011 directs students seeking a thesis topic to contact a professor of the main subject so that a supervisor or supervisors can be assigned. A thesis may address a company challenge, include company co-supervision, or be completed within a University research group. The topic should be narrow enough for a 30 ECTS project and specific enough to support a research question. In Health Technology Materials, avoid titles that promise a clinical outcome before the evidence exists. A title such as “surface modification and in-vitro response of X” is more defensible than claiming that X is a clinically superior implant before clinical evidence has been produced.

Build a thesis plan before experiments

At the beginning of MTEK0011, the thesis worker creates a plan with supervisors. Use that plan to define the problem, research questions, evidence type, materials or datasets, methods, controls, analysis, risks, permissions, milestones and expected outputs. For experimental work, specify specimens, preparation, replicates and characterization methods. For modelling, state scale, assumptions, parameters and validation. For biosignals or human data, state participant or data-source boundaries and approvals. A good plan prevents the method from drifting toward whatever result looks most interesting after data collection.

Use supervision as an evidence-control process

MTEK0011 expects regular progress reporting to supervisors in agreed forms. Use those meetings for more than schedule updates. Bring unresolved methodological decisions, failed experiments, unexpected artefacts, changes to sample preparation, deviations from the original plan and decisions about excluding data. Record major decisions so the final Methods section can explain what actually happened. If a project changes from materials characterization to biological testing or from anonymous engineering data to personal health data, the ethics and permit requirements may change as well. That change should be resolved before continuing rather than documented only after the fact.

How examination and grading work

MTEK0011 states that at least two examiners evaluate the thesis using University evaluation and grading guidelines. Final acceptance is decided by the head of the department, and the grade is based on examiner evaluation. This means the supervisor’s informal approval is not the final examination decision. Build the manuscript so an examiner can trace the research question through literature, method, results and conclusion. A technically impressive prototype cannot compensate for an unsupported inference, missing method detail or conclusions that exceed the evidence.

Choose a research question that matches the evidence

Health Technology Materials topics can range from surface modification and imaging to biomaterial interactions, biosensors, biosignals, simulations and data-driven materials discovery. The research question should name the object, intervention or comparison, measurable outcome and evidence context. If you only have microscopy and spectroscopy, ask a characterization question. If you have cell assays, ask an in-vitro interaction question. If you have human biosignals, define the population and acquisition context. Do not write a clinical-effectiveness question when the available work is only material characterization or cell culture.

Use literature to define the evidence level

A literature review in this field should separate material properties, in-vitro biological response, animal/preclinical studies, human observational evidence, clinical studies and regulatory claims. These are not interchangeable. A carbon nanomaterial may have favourable conductivity and an in-vitro neuronal response without having demonstrated long-term implant safety. A sensor may show strong laboratory sensitivity without demonstrating clinical diagnostic performance. Organise the review so the reader can see which evidence level supports each statement. This also makes the research gap more precise and protects the thesis from overstating translational readiness.

Map methods to claims before collecting data

For every planned conclusion, identify which measurement or analysis can actually support it. A surface-roughness claim may need AFM or profilometry; morphology may need microscopy; elemental composition may use EDS; mechanical claims need mechanical testing; cell response needs a biological assay; signal-processing claims need defined acquisition and validation; predictive ML claims need an evaluation design. This claim-to-method map is one of the best ways to avoid a thesis where many measurements are collected but the central research question remains unanswered.

Biomaterials evidence: what it can and cannot prove

MTEK0027 covers metals, ceramics and polymers as biomaterials, tissue engineering, host reactions, surface properties, testing and sustainability. A thesis can therefore sit at several evidence levels. Material chemistry, morphology or mechanics describe the material. An in-vitro assay adds evidence about a defined biological response under defined laboratory conditions. Animal work can add preclinical evidence. Human research can add another level. Do not compress these into the single word “biocompatible” without stating how compatibility was tested, under what conditions and for what intended contact or application.

Keep in-vitro, animal and human evidence separate

An in-vitro result does not establish in-vivo safety. An animal result does not establish patient benefit. A human observational association does not automatically establish a treatment effect. These boundaries are especially important in Health Technology Materials because a project can move from engineering into biomedical interpretation very quickly. State the evidence level in the Results and repeat the boundary in the Discussion. If you discuss future translation, label it as future work or a hypothesis rather than as a demonstrated outcome.

Materials imaging: choose the method for the question

MTEK0034 covers optical microscopy, SEM, EDS/EDX, AFM and TEM. These methods produce different kinds of evidence because their sample interactions and resolutions differ. Choose the instrument because it answers the research question, not because it produces the most visually impressive image. Report sample preparation, acquisition conditions and relevant instrument settings. If comparing groups, use a sampling plan that avoids selecting only visually favourable regions. Representative images can illustrate a result, but quantitative claims should be supported by a defined measurement and enough independent evidence.

Do not confuse morphology with composition

SEM morphology and EDS compositional information answer different questions. A feature that looks different in an image does not automatically have a different chemical composition, and an elemental signal does not by itself establish phase, molecular structure or biological behaviour. AFM topography similarly does not replace a chemistry measurement. Build the Results section so every figure says what property was measured, by which method, and at what scale. This prevents a common thesis error where interpretation outruns the measurement.

Quantify imaging when the claim is quantitative

If the thesis claims that pores became smaller, coating coverage increased, particles dispersed more uniformly or surface roughness changed, define how those quantities were measured. State segmentation rules, thresholds, field selection, number of images or specimens, and aggregation. Preserve original files and analysis code or macros where possible. Contrast adjustment for presentation should not alter the underlying analysis. A single selected micrograph is usually descriptive evidence, not a statistical estimate of an entire material population.

Multiscale modelling needs explicit assumptions

MTEK0033 teaches finite-element and particle-based approaches across material scales and explicitly emphasises approximations and limitations. A modelling thesis should state the physical scale, governing assumptions, material parameters, geometry, boundary conditions, numerical resolution and output quantities. If a parameter was taken from literature, identify the source and whether it matches the material state in your project. Do not present a simulation as a direct experiment. The model is evidence about behaviour under its assumptions, and its credibility depends on sensitivity checks, convergence where relevant, and validation against appropriate observations.

Simulation is not experimental validation

A model can reproduce known behaviour and still fail outside its calibration domain. If the thesis predicts implant stress, diffusion, heat transfer or self-assembly, identify what experimental or literature evidence constrains the model. Separate calibration data from validation evidence where possible. A visually plausible contour plot is not enough. Report units, parameter ranges and uncertainty or sensitivity where they matter. The conclusion should say “the model predicts” unless independent evidence supports a stronger statement about the real material or device.

Biosignal acquisition and analysis

DTEK0042 covers ECG, EEG, EMG and PPG, including signal origin, sensor technology, preprocessing, feature extraction and vital-sign detection. A biosignal thesis should document the acquisition device, sampling rate, sensor placement, participant or dataset context and preprocessing pipeline. The raw signal, cleaned signal, extracted features and final inference are separate stages. If any stage is changed after looking at the final result, document that decision and avoid presenting the analysis as if it had been fixed in advance.

Artefact removal can change the conclusion

Filters, baseline correction, motion-artifact removal and rejection rules can materially change biosignal features. State the filter type and cut-offs, artifact criteria, excluded segments and whether choices were made before or after outcome inspection. If repeated measurements come from the same participant, acknowledge that they are not automatically independent observations. A clean waveform is not itself proof of a clinically meaningful measurement. The thesis should distinguish signal quality, algorithmic detection accuracy and any clinical interpretation.

Machine learning for materials science

MTEK0035 teaches how to identify materials-science questions suitable for machine learning, choose representations, evaluate methods and run projects on realistic experimental or computational datasets. A thesis should describe data provenance, representation, preprocessing, split design, model configuration and evaluation metric. Experimental and simulated datasets should be identified separately because they carry different sources of uncertainty. The model should be evaluated on evidence that matches the intended use rather than only on the data used to tune it.

Avoid leakage and overclaiming in materials ML

If preprocessing, feature selection or representation learning uses information from the full dataset before the train/test split, apparent performance can be optimistic. Fit data-dependent transformations inside the training process where generalisation is being estimated. Compare models under the same evaluation design and include a meaningful baseline. A high predictive score does not establish a physical mechanism, and a screening model does not by itself establish biocompatibility or clinical value. State the material families and conditions represented in the data so readers can see the model’s domain.

Additive manufacturing evidence

KTEK0012 covers additive-manufacturing processes, materials, software, equipment, process selection and project reporting. If additive manufacturing is part of the thesis, report the process, feedstock/material, geometry preparation, key manufacturing settings and post-processing relevant to the result. A printed part proves that a geometry can be manufactured under those conditions; it does not automatically prove mechanical suitability, sterilisation compatibility, biocompatibility or clinical usability. Validate each property with the method appropriate to that property.

Functional and nanomaterials

KEMI6513 includes conducting polymers, fullerenes, carbon nanotubes, graphene-related materials, characterization and applications including sensors and medical contexts. These topics can support excellent Health Technology Materials theses, but nanoscale structure, conductivity or sensor response should not be converted directly into a health claim. If discussing neuronal interfaces, implants or diagnostics, separate material characterization from biological testing and clinical evidence. Use exact language such as conductivity, morphology, sensitivity or cell response instead of the broad term “effective” unless effectiveness has actually been defined and tested.

Immunoassay and diagnostic claims

MBID0025 introduces antibody affinity, immunoassay principles, assay sensitivity, reporters and diagnostic applications. If your project includes an assay or biosensor, define what performance level is being measured. Analytical detection limit, repeatability, selectivity and calibration are not the same as clinical sensitivity and specificity in a patient population. A laboratory assay can be technically promising without having demonstrated diagnostic utility. Keep analytical validation and clinical validation separate in the manuscript.

Sustainability claims need a lifecycle boundary

MTEK0036 covers the health sector’s carbon footprint, responsible diagnostic development, medical-device design, refurbishment and recycling. Sustainability claims should state the lifecycle boundary and the compared alternative. Lower material mass, energy use or waste in one stage does not prove lower total lifecycle impact. In health technology, performance and safety constraints may also affect reuse or recycling choices. If sustainability is secondary to the thesis, keep the claim proportionate to the evidence rather than adding a generic “sustainable” label to the conclusion.

Capstone and thesis should not be merged

The curriculum intentionally includes both MTEK0019 Capstone and MTEK0011 thesis. Capstone develops team-based project execution, while the thesis is the individually examined scientific work. You may reuse a problem context, equipment or background knowledge where permitted, but define what work belongs to which course and avoid double counting. If a Capstone produced a prototype, the thesis can investigate a specific scientific question about that prototype, such as material behaviour, measurement validity or model performance. The thesis still needs its own methods, results and defensible contribution.

Company work and NDA boundaries

MTEK0011 allows company-connected topics, and MTEK0018 explicitly recognises that internship work may be under an NDA. Before collecting company data or using proprietary materials, agree what can appear in the public thesis, what must remain confidential, who owns data and code, and how examiners can access necessary evidence. Do not assume that academic publication overrides a contract. When raw information cannot be published, use an approved level of methodological description, synthetic examples or aggregated results where appropriate, while preserving enough evidence for examination.

When human participants are involved

A Health Technology Materials thesis does not automatically require human-subject ethical review, but a study involving participants can. Wearable-sensor tests, usability studies, interviews, behavioural tasks or collection of identifiable biosignals may move the project into human-research governance. Check the actual design against current University ethical-review criteria before recruitment or data collection. Consent, privacy, recruitment and risk controls are design questions, not formalities to add after the experiment. If only anonymous bench data are used, do not imply that a human study was performed.

When medical research rules may apply

Some projects are materials engineering; others may cross into medical research because of intervention, patient material, clinical context or study purpose. Do not infer the legal or ethics route from the programme name alone. Check the actual design against the University’s medical research assessment information and obtain required decisions before the regulated activity begins. A laboratory material study and a patient-facing device study can require very different governance even when they use the same material.

Animal research is a separate approval pathway

If biomaterial or device work includes animal experiments, the applicable authorisation and competence requirements must be satisfied. Academic supervision or participation in a materials programme does not authorise an animal study. Training also does not substitute for project authorisation. Keep animal/preclinical evidence clearly labelled in the thesis, describe the model and endpoints, and avoid translating an animal result directly into a patient claim. If your thesis only reviews animal studies from literature, state that no new animal experiment was performed.

Research permits are not ethics approval

A host organisation may require a research permit, especially when using its facilities, staff, samples or data. That permit does not replace ethical review, informed consent, animal authorisation, privacy obligations or contractual permissions where those are required. Build a simple approval matrix listing each data/material source and the permission needed. This is especially useful for cross-faculty or hospital-linked health-technology work where several governance layers can apply at the same time.

Protect personal and sensitive data

Biosignals, medical images, device logs and linked metadata can contain personal data even when names are removed. Pseudonymised data remain personal data when re-identification is possible. Define who controls the data, who can access it, where it is stored, how long it is retained and what can be published. Do not upload protected research data to uncontrolled AI or cloud services. Review figures, screenshots and example records for hidden identifiers or metadata before they enter the thesis.

AI use must remain accountable

The University provides guidance on responsible AI use, but MTEK0011’s current public course text does not create a separate blanket permission for every AI use. Follow current University, faculty and supervisor instructions. If AI assists with code, translation, literature triage, image segmentation or drafting, you remain responsible for checking the output. Do not fabricate references, data or experimental detail. Keep AI used as a research method separate from AI used as a productivity assistant, because the former may itself require methodological validation.

Build a reproducibility package

For computational or data-heavy work, preserve the code revision, environment, input-data version, preprocessing steps, configuration, random seeds where relevant and the exact scripts that generated tables and figures. For experimental work, preserve specimen IDs, preparation protocol, instrument method files, calibration information and the mapping from raw data to analysed result. Reproducibility does not always mean publishing confidential raw data. It means preserving a traceable chain so the result can be checked and, where permissions allow, reproduced.

Make figures and tables auditable

Every figure should identify what was measured, the unit, sample size or number of independent specimens where relevant, and what error bars represent. For images, distinguish representative panels from quantified datasets. For model plots, state whether data are training, validation or test results. For biosignals, identify processing state. Avoid manually copying values between spreadsheets and figures when a scripted path is possible. The reader should be able to connect a figure back to the data and method that generated it.

A defensible thesis chapter structure

The programme does not publicly impose one universal chapter template, so use a structure that makes the evidence chain clear. A practical pattern is Introduction, Literature Review or Background, Research Questions, Materials and Methods, Results, Discussion, Conclusions, References and appendices where needed. Experimental and computational work can adapt this pattern. Keep Methods detailed enough to explain what was actually done, Results focused on observations, and Discussion focused on interpretation and limitations. Do not hide key method decisions only in appendices if they are necessary to understand the main claim.

Turnitin checks originality, not scientific validity

Degree theses at the University of Turku are checked with Turnitin, and the master’s thesis approval process uses UTUGradu. Similarity review is important for source use and originality, but a low similarity percentage does not validate sample preparation, statistics, biocompatibility, imaging interpretation, biosignal processing or model generalisation. Resolve scientific validity through methods, controls, analysis and examiner review. Treat Turnitin as an integrity control, not as evidence that the scientific conclusion is correct.

UTUGradu submission and publication

UTUGradu manages the electronic higher-degree thesis process including originality checking, examination, approval, publication and archiving. Before submission, recheck the current operational instructions because form fields and process details can change independently of the stable 30 ECTS thesis rule. Make sure the version uploaded for examination is the intended final manuscript, confidentiality restrictions have been resolved, required metadata are correct, and any separate maturity-test requirement for your background is addressed.

Recheck your maturity-test route before submission

Because TTDK1308 contains a conditional written-exam route, the maturity test should be treated as a personal completion requirement, not copied from a friend’s checklist. Check whether your previous degree included a maturity test and whether the Finnish/Swedish educational-language condition applies. If the normal route applies, make sure the abstract or approved thesis component meets the required purpose. Resolve uncertainty before the thesis enters final examination so degree completion is not delayed by a separate missing requirement.

A practical thesis timeline

A workable sequence is: confirm HOPS and MTEK0020 participation, identify topic and supervisor, define evidence level, resolve permits and data access, write the research plan, pilot the method, collect or generate data, analyse with documented rules, present progress, draft methods and results early, complete the MTEK0020 result presentation, revise the full manuscript, complete originality checking and UTUGradu steps, then respond to examination requirements. Build contingency time for instrument access, failed specimens, ethics/permit decisions, company review and data-cleaning problems. Health-technology projects often have dependencies outside the student’s direct control.

Final pre-submission checklist

Before submission, confirm that the manuscript names the correct programme and MTEK0011 course, does not call the 40 ECTS Thesis and Project block a 40 ECTS thesis, keeps MTEK0020 and MTEK0019 separate, states the evidence level of every health-related conclusion, documents sample/data provenance and analysis, reports limitations, resolves ethics/permits/privacy, respects NDA restrictions, checks AI-assisted material, verifies references, reproduces figures from final data, completes the maturity-test route, and follows current Turnitin and UTUGradu instructions. That checklist is more valuable than copying formatting habits from an unrelated programme.